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Sample records for strong protein-protein interaction

  1. Evolution of protein-protein interactions

    Indian Academy of Sciences (India)

    Evolution of protein-protein interactions · Our interests in protein-protein interactions · Slide 3 · Slide 4 · Slide 5 · Slide 6 · Slide 7 · Slide 8 · Slide 9 · Slide 10 · Slide 11 · Slide 12 · Slide 13 · Slide 14 · Slide 15 · Slide 16 · Slide 17 · Slide 18 · Slide 19 · Slide 20.

  2. Aquaporin Protein-Protein Interactions

    Directory of Open Access Journals (Sweden)

    Jennifer Virginia Roche

    2017-10-01

    Full Text Available Aquaporins are tetrameric membrane-bound channels that facilitate transport of water and other small solutes across cell membranes. In eukaryotes, they are frequently regulated by gating or trafficking, allowing for the cell to control membrane permeability in a specific manner. Protein–protein interactions play crucial roles in both regulatory processes and also mediate alternative functions such as cell adhesion. In this review, we summarize recent knowledge about aquaporin protein–protein interactions; dividing the interactions into three types: (1 interactions between aquaporin tetramers; (2 interactions between aquaporin monomers within a tetramer (hetero-tetramerization; and (3 transient interactions with regulatory proteins. We particularly focus on the structural aspects of the interactions, discussing the small differences within a conserved overall fold that allow for aquaporins to be differentially regulated in an organism-, tissue- and trigger-specific manner. A deep knowledge about these differences is needed to fully understand aquaporin function and regulation in many physiological processes, and may enable design of compounds targeting specific aquaporins for treatment of human disease.

  3. Protein-Protein Interaction Databases

    DEFF Research Database (Denmark)

    Szklarczyk, Damian; Jensen, Lars Juhl

    2015-01-01

    Years of meticulous curation of scientific literature and increasingly reliable computational predictions have resulted in creation of vast databases of protein interaction data. Over the years, these repositories have become a basic framework in which experiments are analyzed and new directions...

  4. Scoring functions for protein-protein interactions.

    Science.gov (United States)

    Moal, Iain H; Moretti, Rocco; Baker, David; Fernández-Recio, Juan

    2013-12-01

    The computational evaluation of protein-protein interactions will play an important role in organising the wealth of data being generated by high-throughput initiatives. Here we discuss future applications, report recent developments and identify areas requiring further investigation. Many functions have been developed to quantify the structural and energetic properties of interacting proteins, finding use in interrelated challenges revolving around the relationship between sequence, structure and binding free energy. These include loop modelling, side-chain refinement, docking, multimer assembly, affinity prediction, affinity change upon mutation, hotspots location and interface design. Information derived from models optimised for one of these challenges can be used to benefit the others, and can be unified within the theoretical frameworks of multi-task learning and Pareto-optimal multi-objective learning. Copyright © 2013 Elsevier Ltd. All rights reserved.

  5. Specificity and affinity quantification of protein-protein interactions.

    Science.gov (United States)

    Yan, Zhiqiang; Guo, Liyong; Hu, Liang; Wang, Jin

    2013-05-01

    Most biological processes are mediated by the protein-protein interactions. Determination of the protein-protein structures and insight into their interactions are vital to understand the mechanisms of protein functions. Currently, compared with the isolated protein structures, only a small fraction of protein-protein structures are experimentally solved. Therefore, the computational docking methods play an increasing role in predicting the structures and interactions of protein-protein complexes. The scoring function of protein-protein interactions is the key responsible for the accuracy of the computational docking. Previous scoring functions were mostly developed by optimizing the binding affinity which determines the stability of the protein-protein complex, but they are often lack of the consideration of specificity which determines the discrimination of native protein-protein complex against competitive ones. We developed a scoring function (named as SPA-PP, specificity and affinity of the protein-protein interactions) by incorporating both the specificity and affinity into the optimization strategy. The testing results and comparisons with other scoring functions show that SPA-PP performs remarkably on both predictions of binding pose and binding affinity. Thus, SPA-PP is a promising quantification of protein-protein interactions, which can be implemented into the protein docking tools and applied for the predictions of protein-protein structure and affinity. The algorithm is implemented in C language, and the code can be downloaded from http://dl.dropbox.com/u/1865642/Optimization.cpp.

  6. Intercellular protein-protein interactions at synapses.

    Science.gov (United States)

    Yang, Xiaofei; Hou, Dongmei; Jiang, Wei; Zhang, Chen

    2014-06-01

    Chemical synapses are asymmetric intercellular junctions through which neurons send nerve impulses to communicate with other neurons or excitable cells. The appropriate formation of synapses, both spatially and temporally, is essential for brain function and depends on the intercellular protein-protein interactions of cell adhesion molecules (CAMs) at synaptic clefts. The CAM proteins link pre- and post-synaptic sites, and play essential roles in promoting synapse formation and maturation, maintaining synapse number and type, accumulating neurotransmitter receptors and ion channels, controlling neuronal differentiation, and even regulating synaptic plasticity directly. Alteration of the interactions of CAMs leads to structural and functional impairments, which results in many neurological disorders, such as autism, Alzheimer's disease and schizophrenia. Therefore, it is crucial to understand the functions of CAMs during development and in the mature neural system, as well as in the pathogenesis of some neurological disorders. Here, we review the function of the major classes of CAMs, and how dysfunction of CAMs relates to several neurological disorders.

  7. Our interests in protein-protein interactions

    Indian Academy of Sciences (India)

    protein interactions. Evolution of P-P partnerships. Evolution of P-P structures. Evolutionary dynamics of P-P interactions. Dynamics of P-P interaction network. Host-pathogen interactions. CryoEM mapping of gigantic protein assemblies.

  8. Information assessment on predicting protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Gerstein Mark

    2004-10-01

    Full Text Available Abstract Background Identifying protein-protein interactions is fundamental for understanding the molecular machinery of the cell. Proteome-wide studies of protein-protein interactions are of significant value, but the high-throughput experimental technologies suffer from high rates of both false positive and false negative predictions. In addition to high-throughput experimental data, many diverse types of genomic data can help predict protein-protein interactions, such as mRNA expression, localization, essentiality, and functional annotation. Evaluations of the information contributions from different evidences help to establish more parsimonious models with comparable or better prediction accuracy, and to obtain biological insights of the relationships between protein-protein interactions and other genomic information. Results Our assessment is based on the genomic features used in a Bayesian network approach to predict protein-protein interactions genome-wide in yeast. In the special case, when one does not have any missing information about any of the features, our analysis shows that there is a larger information contribution from the functional-classification than from expression correlations or essentiality. We also show that in this case alternative models, such as logistic regression and random forest, may be more effective than Bayesian networks for predicting interactions. Conclusions In the restricted problem posed by the complete-information subset, we identified that the MIPS and Gene Ontology (GO functional similarity datasets as the dominating information contributors for predicting the protein-protein interactions under the framework proposed by Jansen et al. Random forests based on the MIPS and GO information alone can give highly accurate classifications. In this particular subset of complete information, adding other genomic data does little for improving predictions. We also found that the data discretizations used in the

  9. Strong interactions

    International Nuclear Information System (INIS)

    Froissart, Marcel

    1976-01-01

    Strong interactions are introduced by their more obvious aspect: nuclear forces. In hadron family, the nucleon octet, OMEGA - decuplet, and quark triply are successively considered. Pion wave having been put at the origin of nuclear forces, low energy phenomena are described, the force being explained as an exchange of structure corresponding to a Regge trajectory in a variable rotating state instead of the exchange of a well defined particle. At high energies the concepts of pomeron, parton and stratons are introduced, pionization and fragmentation are briefly differentiated [fr

  10. On the role of electrostatics on protein-protein interactions

    Science.gov (United States)

    Zhang, Zhe; Witham, Shawn; Alexov, Emil

    2011-01-01

    The role of electrostatics on protein-protein interactions and binding is reviewed in this article. A brief outline of the computational modeling, in the framework of continuum electrostatics, is presented and basic electrostatic effects occurring upon the formation of the complex are discussed. The role of the salt concentration and pH of the water phase on protein-protein binding free energy is demonstrated and indicates that the increase of the salt concentration tends to weaken the binding, an observation that is attributed to the optimization of the charge-charge interactions across the interface. It is pointed out that the pH-optimum (pH of optimal binding affinity) varies among the protein-protein complexes, and perhaps is a result of their adaptation to particular subcellular compartment. At the end, the similarities and differences between hetero- and homo-complexes are outlined and discussed with respect to the binding mode and charge complementarity. PMID:21572182

  11. Protein-protein interactions and cancer: targeting the central dogma.

    Science.gov (United States)

    Garner, Amanda L; Janda, Kim D

    2011-01-01

    Between 40,000 and 200,000 protein-protein interactions have been predicted to exist within the human interactome. As these interactions are of a critical nature in many important cellular functions and their dysregulation is causal of disease, the modulation of these binding events has emerged as a leading, yet difficult therapeutic arena. In particular, the targeting of protein-protein interactions relevant to cancer is of fundamental importance as the tumor-promoting function of several aberrantly expressed proteins in the cancerous state is directly resultant of its ability to interact with a protein-binding partner. Of significance, these protein complexes play a crucial role in each of the steps of the central dogma of molecular biology, the fundamental processes of genetic transmission. With the many important discoveries being made regarding the mechanisms of these genetic process, the identification of new chemical probes are needed to better understand and validate the druggability of protein-protein interactions related to the central dogma. In this review, we provide an overview of current small molecule-based protein-protein interaction inhibitors for each stage of the central dogma: transcription, mRNA splicing and translation. Importantly, through our analysis we have uncovered a lack of necessary probes targeting mRNA splicing and translation, thus, opening up the possibility for expansion of these fields.

  12. Can infrared spectroscopy provide information on protein-protein interactions?

    Science.gov (United States)

    Haris, Parvez I

    2010-08-01

    For most biophysical techniques, characterization of protein-protein interactions is challenging; this is especially true with methods that rely on a physical phenomenon that is common to both of the interacting proteins. Thus, for example, in IR spectroscopy, the carbonyl vibration (1600-1700 cm(-1)) associated with the amide bonds from both of the interacting proteins will overlap extensively, making the interpretation of spectral changes very complicated. Isotope-edited infrared spectroscopy, where one of the interacting proteins is uniformly labelled with (13)C or (13)C,(15)N has been introduced as a solution to this problem, enabling the study of protein-protein interactions using IR spectroscopy. The large shift of the amide I band (approx. 45 cm(-1) towards lower frequency) upon (13)C labelling of one of the proteins reveals the amide I band of the unlabelled protein, enabling it to be used as a probe for monitoring conformational changes. With site-specific isotopic labelling, structural resolution at the level of individual amino acid residues can be achieved. Furthermore, the ability to record IR spectra of proteins in diverse environments means that isotope-edited IR spectroscopy can be used to structurally characterize difficult systems such as protein-protein complexes bound to membranes or large insoluble peptide/protein aggregates. In the present article, examples of application of isotope-edited IR spectroscopy for studying protein-protein interactions are provided.

  13. Detecting mutually exclusive interactions in protein-protein interaction maps.

    KAUST Repository

    Sá nchez Claros, Carmen; Tramontano, Anna

    2012-01-01

    Comprehensive protein interaction maps can complement genetic and biochemical experiments and allow the formulation of new hypotheses to be tested in the system of interest. The computational analysis of the maps may help to focus on interesting cases and thereby to appropriately prioritize the validation experiments. We show here that, by automatically comparing and analyzing structurally similar regions of proteins of known structure interacting with a common partner, it is possible to identify mutually exclusive interactions present in the maps with a sensitivity of 70% and a specificity higher than 85% and that, in about three fourth of the correctly identified complexes, we also correctly recognize at least one residue (five on average) belonging to the interaction interface. Given the present and continuously increasing number of proteins of known structure, the requirement of the knowledge of the structure of the interacting proteins does not substantially impact on the coverage of our strategy that can be estimated to be around 25%. We also introduce here the Estrella server that embodies this strategy, is designed for users interested in validating specific hypotheses about the functional role of a protein-protein interaction and it also allows access to pre-computed data for seven organisms.

  14. Detecting mutually exclusive interactions in protein-protein interaction maps.

    KAUST Repository

    Sánchez Claros, Carmen

    2012-06-08

    Comprehensive protein interaction maps can complement genetic and biochemical experiments and allow the formulation of new hypotheses to be tested in the system of interest. The computational analysis of the maps may help to focus on interesting cases and thereby to appropriately prioritize the validation experiments. We show here that, by automatically comparing and analyzing structurally similar regions of proteins of known structure interacting with a common partner, it is possible to identify mutually exclusive interactions present in the maps with a sensitivity of 70% and a specificity higher than 85% and that, in about three fourth of the correctly identified complexes, we also correctly recognize at least one residue (five on average) belonging to the interaction interface. Given the present and continuously increasing number of proteins of known structure, the requirement of the knowledge of the structure of the interacting proteins does not substantially impact on the coverage of our strategy that can be estimated to be around 25%. We also introduce here the Estrella server that embodies this strategy, is designed for users interested in validating specific hypotheses about the functional role of a protein-protein interaction and it also allows access to pre-computed data for seven organisms.

  15. Oligomeric protein structure networks: insights into protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Brinda KV

    2005-12-01

    Full Text Available Abstract Background Protein-protein association is essential for a variety of cellular processes and hence a large number of investigations are being carried out to understand the principles of protein-protein interactions. In this study, oligomeric protein structures are viewed from a network perspective to obtain new insights into protein association. Structure graphs of proteins have been constructed from a non-redundant set of protein oligomer crystal structures by considering amino acid residues as nodes and the edges are based on the strength of the non-covalent interactions between the residues. The analysis of such networks has been carried out in terms of amino acid clusters and hubs (highly connected residues with special emphasis to protein interfaces. Results A variety of interactions such as hydrogen bond, salt bridges, aromatic and hydrophobic interactions, which occur at the interfaces are identified in a consolidated manner as amino acid clusters at the interface, from this study. Moreover, the characterization of the highly connected hub-forming residues at the interfaces and their comparison with the hubs from the non-interface regions and the non-hubs in the interface regions show that there is a predominance of charged interactions at the interfaces. Further, strong and weak interfaces are identified on the basis of the interaction strength between amino acid residues and the sizes of the interface clusters, which also show that many protein interfaces are stronger than their monomeric protein cores. The interface strengths evaluated based on the interface clusters and hubs also correlate well with experimentally determined dissociation constants for known complexes. Finally, the interface hubs identified using the present method correlate very well with experimentally determined hotspots in the interfaces of protein complexes obtained from the Alanine Scanning Energetics database (ASEdb. A few predictions of interface hot

  16. Protein-Protein Interactions (PPI) reagents: | Office of Cancer Genomics

    Science.gov (United States)

    The CTD2 Center at Emory University has a library of genes used to study protein-protein interactions in mammalian cells. These genes are cloned in different mammalian expression vectors. A list of available cancer-associated genes can be accessed below.

  17. Protein-Protein Interaction Reagents | Office of Cancer Genomics

    Science.gov (United States)

    The CTD2 Center at Emory University has a library of genes used to study protein-protein interactions in mammalian cells. These genes are cloned in different mammalian expression vectors. A list of available cancer-associated genes can be accessed below. Emory_CTD^2_PPI_Reagents.xlsx Contact: Haian Fu

  18. Coarse-grain modelling of protein-protein interactions

    NARCIS (Netherlands)

    Baaden, Marc; Marrink, Siewert J.

    2013-01-01

    Here, we review recent advances towards the modelling of protein-protein interactions (PPI) at the coarse-grained (CG) level, a technique that is now widely used to understand protein affinity, aggregation and self-assembly behaviour. PPI models of soluble proteins and membrane proteins are

  19. Evolutionary reprograming of protein-protein interaction specificity.

    Science.gov (United States)

    Akiva, Eyal; Babbitt, Patricia C

    2015-10-22

    Using mutation libraries and deep sequencing, Aakre et al. study the evolution of protein-protein interactions using a toxin-antitoxin model. The results indicate probable trajectories via "intermediate" proteins that are promiscuous, thus avoiding transitions via non-interactions. These results extend observations about other biological interactions and enzyme evolution, suggesting broadly general principles. Copyright © 2015 Elsevier Inc. All rights reserved.

  20. Globular and disordered-the non-identical twins in protein-protein interactions

    DEFF Research Database (Denmark)

    Teilum, Kaare; Olsen, Johan Gotthardt; Kragelund, Birthe Brandt

    2015-01-01

    as a strong determinant for their function. This has fostered the notion that IDP's bind with low affinity but high specificity. Here we have analyzed available detailed thermodynamic data for protein-protein interactions to put to the test if the thermodynamic profiles of IDP interactions differ from those...... of other protein-protein interactions. We find that ordered proteins and the disordered ones act as non-identical twins operating by similar principles but where the disordered proteins complexes are on average less stable by 2.5 kcal mol(-1)....

  1. Understanding Protein-Protein Interactions Using Local Structural Features

    DEFF Research Database (Denmark)

    Planas-Iglesias, Joan; Bonet, Jaume; García-García, Javier

    2013-01-01

    Protein-protein interactions (PPIs) play a relevant role among the different functions of a cell. Identifying the PPI network of a given organism (interactome) is useful to shed light on the key molecular mechanisms within a biological system. In this work, we show the role of structural features...... interacting and non-interacting protein pairs to classify the structural features that sustain the binding (or non-binding) behavior. Our study indicates that not only the interacting region but also the rest of the protein surface are important for the interaction fate. The interpretation...... to score the likelihood of the interaction between two proteins and to develop a method for the prediction of PPIs. We have tested our method on several sets with unbalanced ratios of interactions and non-interactions to simulate real conditions, obtaining accuracies higher than 25% in the most unfavorable...

  2. Selection of peptides interfering with protein-protein interaction.

    Science.gov (United States)

    Gaida, Annette; Hagemann, Urs B; Mattay, Dinah; Räuber, Christina; Müller, Kristian M; Arndt, Katja M

    2009-01-01

    Cell physiology depends on a fine-tuned network of protein-protein interactions, and misguided interactions are often associated with various diseases. Consequently, peptides, which are able to specifically interfere with such adventitious interactions, are of high interest for analytical as well as medical purposes. One of the most abundant protein interaction domains is the coiled-coil motif, and thus provides a premier target. Coiled coils, which consist of two or more alpha-helices wrapped around each other, have one of the simplest interaction interfaces, yet they are able to confer highly specific homo- and heterotypic interactions involved in virtually any cellular process. While there are several ways to generate interfering peptides, the combination of library design with a powerful selection system seems to be one of the most effective and promising approaches. This chapter guides through all steps of such a process, starting with library options and cloning, detailing suitable selection techniques and ending with purification for further down-stream characterization. Such generated peptides will function as versatile tools to interfere with the natural function of their targets thereby illuminating their down-stream signaling and, in general, promoting understanding of factors leading to specificity and stability in protein-protein interactions. Furthermore, peptides interfering with medically relevant proteins might become important diagnostics and therapeutics.

  3. Cell penetrating peptides to dissect host-pathogen protein-protein interactions in Theileria -transformed leukocytes

    KAUST Repository

    Haidar, Malak; de Laté , Perle Latré ; Kennedy, Eileen J.; Langsley, Gordon

    2017-01-01

    One powerful application of cell penetrating peptides is the delivery into cells of molecules that function as specific competitors or inhibitors of protein-protein interactions. Ablating defined protein-protein interactions is a refined way

  4. Potential disruption of protein-protein interactions by graphene oxide

    Energy Technology Data Exchange (ETDEWEB)

    Feng, Mei [Department of Physics, Institute of Quantitative Biology, Zhejiang University, Hangzhou 310027 (China); Kang, Hongsuk; Luan, Binquan [Computational Biological Center, IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598 (United States); Yang, Zaixing [Institute of Quantitative Biology and Medicine, SRMP and RAD-X, and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123 (China); Zhou, Ruhong, E-mail: ruhong@us.ibm.com [Department of Physics, Institute of Quantitative Biology, Zhejiang University, Hangzhou 310027 (China); Computational Biological Center, IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598 (United States); Department of Chemistry, Columbia University, New York, New York 10027 (United States)

    2016-06-14

    Graphene oxide (GO) is a promising novel nanomaterial with a wide range of potential biomedical applications due to its many intriguing properties. However, very little research has been conducted to study its possible adverse effects on protein-protein interactions (and thus subsequent toxicity to human). Here, the potential cytotoxicity of GO is investigated at molecular level using large-scale, all-atom molecular dynamics simulations to explore the interaction mechanism between a protein dimer and a GO nanosheet oxidized at different levels. Our theoretical results reveal that GO nanosheet could intercalate between the two monomers of HIV-1 integrase dimer, disrupting the protein-protein interactions and eventually lead to dimer disassociation as graphene does [B. Luan et al., ACS Nano 9(1), 663 (2015)], albeit its insertion process is slower when compared with graphene due to the additional steric and attractive interactions. This study helps to better understand the toxicity of GO to cell functions which could shed light on how to improve its biocompatibility and biosafety for its wide potential biomedical applications.

  5. Potential disruption of protein-protein interactions by graphene oxide

    International Nuclear Information System (INIS)

    Feng, Mei; Kang, Hongsuk; Luan, Binquan; Yang, Zaixing; Zhou, Ruhong

    2016-01-01

    Graphene oxide (GO) is a promising novel nanomaterial with a wide range of potential biomedical applications due to its many intriguing properties. However, very little research has been conducted to study its possible adverse effects on protein-protein interactions (and thus subsequent toxicity to human). Here, the potential cytotoxicity of GO is investigated at molecular level using large-scale, all-atom molecular dynamics simulations to explore the interaction mechanism between a protein dimer and a GO nanosheet oxidized at different levels. Our theoretical results reveal that GO nanosheet could intercalate between the two monomers of HIV-1 integrase dimer, disrupting the protein-protein interactions and eventually lead to dimer disassociation as graphene does [B. Luan et al., ACS Nano 9(1), 663 (2015)], albeit its insertion process is slower when compared with graphene due to the additional steric and attractive interactions. This study helps to better understand the toxicity of GO to cell functions which could shed light on how to improve its biocompatibility and biosafety for its wide potential biomedical applications.

  6. Bioinformatic Prediction of WSSV-Host Protein-Protein Interaction

    Directory of Open Access Journals (Sweden)

    Zheng Sun

    2014-01-01

    Full Text Available WSSV is one of the most dangerous pathogens in shrimp aquaculture. However, the molecular mechanism of how WSSV interacts with shrimp is still not very clear. In the present study, bioinformatic approaches were used to predict interactions between proteins from WSSV and shrimp. The genome data of WSSV (NC_003225.1 and the constructed transcriptome data of F. chinensis were used to screen potentially interacting proteins by searching in protein interaction databases, including STRING, Reactome, and DIP. Forty-four pairs of proteins were suggested to have interactions between WSSV and the shrimp. Gene ontology analysis revealed that 6 pairs of these interacting proteins were classified into “extracellular region” or “receptor complex” GO-terms. KEGG pathway analysis showed that they were involved in the “ECM-receptor interaction pathway.” In the 6 pairs of interacting proteins, an envelope protein called “collagen-like protein” (WSSV-CLP encoded by an early virus gene “wsv001” in WSSV interacted with 6 deduced proteins from the shrimp, including three integrin alpha (ITGA, two integrin beta (ITGB, and one syndecan (SDC. Sequence analysis on WSSV-CLP, ITGA, ITGB, and SDC revealed that they possessed the sequence features for protein-protein interactions. This study might provide new insights into the interaction mechanisms between WSSV and shrimp.

  7. Feature generation and representations for protein-protein interaction classification.

    Science.gov (United States)

    Lan, Man; Tan, Chew Lim; Su, Jian

    2009-10-01

    Automatic detecting protein-protein interaction (PPI) relevant articles is a crucial step for large-scale biological database curation. The previous work adopted POS tagging, shallow parsing and sentence splitting techniques, but they achieved worse performance than the simple bag-of-words representation. In this paper, we generated and investigated multiple types of feature representations in order to further improve the performance of PPI text classification task. Besides the traditional domain-independent bag-of-words approach and the term weighting methods, we also explored other domain-dependent features, i.e. protein-protein interaction trigger keywords, protein named entities and the advanced ways of incorporating Natural Language Processing (NLP) output. The integration of these multiple features has been evaluated on the BioCreAtIvE II corpus. The experimental results showed that both the advanced way of using NLP output and the integration of bag-of-words and NLP output improved the performance of text classification. Specifically, in comparison with the best performance achieved in the BioCreAtIvE II IAS, the feature-level and classifier-level integration of multiple features improved the performance of classification 2.71% and 3.95%, respectively.

  8. Next-Generation Sequencing for Binary Protein-Protein Interactions

    Directory of Open Access Journals (Sweden)

    Bernhard eSuter

    2015-12-01

    Full Text Available The yeast two-hybrid (Y2H system exploits host cell genetics in order to display binary protein-protein interactions (PPIs via defined and selectable phenotypes. Numerous improvements have been made to this method, adapting the screening principle for diverse applications, including drug discovery and the scale-up for proteome wide interaction screens in human and other organisms. Here we discuss a systematic workflow and analysis scheme for screening data generated by Y2H and related assays that includes high-throughput selection procedures, readout of comprehensive results via next-generation sequencing (NGS, and the interpretation of interaction data via quantitative statistics. The novel assays and tools will serve the broader scientific community to harness the power of NGS technology to address PPI networks in health and disease. We discuss examples of how this next-generation platform can be applied to address specific questions in diverse fields of biology and medicine.

  9. Mapping Protein-Protein Interactions by Quantitative Proteomics

    DEFF Research Database (Denmark)

    Dengjel, Joern; Kratchmarova, Irina; Blagoev, Blagoy

    2010-01-01

    spectrometry (MS)-based proteomics in combination with affinity purification protocols has become the method of choice to map and track the dynamic changes in protein-protein interactions, including the ones occurring during cellular signaling events. Different quantitative MS strategies have been used...... to characterize protein interaction networks. In this chapter we describe in detail the use of stable isotope labeling by amino acids in cell culture (SILAC) for the quantitative analysis of stimulus-dependent dynamic protein interactions.......Proteins exert their function inside a cell generally in multiprotein complexes. These complexes are highly dynamic structures changing their composition over time and cell state. The same protein may thereby fulfill different functions depending on its binding partners. Quantitative mass...

  10. Globular and disordered – the non-identical twins in protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Kaare eTeilum

    2015-07-01

    Full Text Available In biology proteins from different structural classes interact across and within classes in ways that are optimized to achieve balanced functional outputs. The interactions between intrinsically disordered proteins (IDPs and other proteins rely on changes in flexibility and this is seen as a strong determinant for their function. This has fostered the notion that IDP’s bind with low affinity but high specificity. Here we have analyzed available detailed thermodynamic data for protein-protein interactions to put to the test if the thermodynamic profiles of IDP interactions differ from those of other protein-protein interactions. We find that ordered proteins and the disordered ones act as non identical twins operating by similar principles but where the disordered proteins complexes are on average less stable by 2.5 kcal mol-1.

  11. Detecting protein-protein interactions in living cells

    DEFF Research Database (Denmark)

    Gottschalk, Marie; Bach, Anders; Hansen, Jakob Lerche

    2009-01-01

    to the endogenous C-terminal peptide of the NMDA receptor, as evaluated by a cell-free protein-protein interaction assay. However, it is important to address both membrane permeability and effect in living cells. Therefore a bioluminescence resonance energy transfer (BRET) assay was established, where the C......-terminal of the NMDA receptor and PDZ2 of PSD-95 were fused to green fluorescent protein (GFP) and Renilla luciferase (Rluc) and expressed in COS7 cells. A robust and specific BRET signal was obtained by expression of the appropriate partner proteins and subsequently, the assay was used to evaluate a Tat......The PDZ domain mediated interaction between the NMDA receptor and its intracellular scaffolding protein, PSD-95, is a potential target for treatment of ischemic brain diseases. We have recently developed a number of peptide analogues with improved affinity for the PDZ domains of PSD-95 compared...

  12. Human cancer protein-protein interaction network: a structural perspective.

    Directory of Open Access Journals (Sweden)

    Gozde Kar

    2009-12-01

    Full Text Available Protein-protein interaction networks provide a global picture of cellular function and biological processes. Some proteins act as hub proteins, highly connected to others, whereas some others have few interactions. The dysfunction of some interactions causes many diseases, including cancer. Proteins interact through their interfaces. Therefore, studying the interface properties of cancer-related proteins will help explain their role in the interaction networks. Similar or overlapping binding sites should be used repeatedly in single interface hub proteins, making them promiscuous. Alternatively, multi-interface hub proteins make use of several distinct binding sites to bind to different partners. We propose a methodology to integrate protein interfaces into cancer interaction networks (ciSPIN, cancer structural protein interface network. The interactions in the human protein interaction network are replaced by interfaces, coming from either known or predicted complexes. We provide a detailed analysis of cancer related human protein-protein interfaces and the topological properties of the cancer network. The results reveal that cancer-related proteins have smaller, more planar, more charged and less hydrophobic binding sites than non-cancer proteins, which may indicate low affinity and high specificity of the cancer-related interactions. We also classified the genes in ciSPIN according to phenotypes. Within phenotypes, for breast cancer, colorectal cancer and leukemia, interface properties were found to be discriminating from non-cancer interfaces with an accuracy of 71%, 67%, 61%, respectively. In addition, cancer-related proteins tend to interact with their partners through distinct interfaces, corresponding mostly to multi-interface hubs, which comprise 56% of cancer-related proteins, and constituting the nodes with higher essentiality in the network (76%. We illustrate the interface related affinity properties of two cancer-related hub

  13. Rationalizing the chemical space of protein-protein interaction inhibitors.

    Science.gov (United States)

    Sperandio, Olivier; Reynès, Christelle H; Camproux, Anne-Claude; Villoutreix, Bruno O

    2010-03-01

    Protein-protein interactions (PPIs) are one of the next major classes of therapeutic targets, although they are too intricate to tackle with standard approaches. This is due, in part, to the inadequacy of today's chemical libraries. However, the emergence of a growing number of experimentally validated inhibitors of PPIs (i-PPIs) allows drug designers to use chemoinformatics and machine learning technologies to unravel the nature of the chemical space covered by the reported compounds. Key characteristics of i-PPIs can then be revealed and highlight the importance of specific shapes and/or aromatic bonds, enabling the design of i-PPI-enriched focused libraries and, therefore, of cost-effective screening strategies. 2009 Elsevier Ltd. All rights reserved.

  14. [Detection of protein-protein interactions by FRET and BRET methods].

    Science.gov (United States)

    Matoulková, E; Vojtěšek, B

    2014-01-01

    Nowadays, in vivo protein-protein interaction studies have become preferable detecting meth-ods that enable to show or specify (already known) protein interactions and discover their inhibitors. They also facilitate detection of protein conformational changes and discovery or specification of signaling pathways in living cells. One group of in vivo methods enabling these findings is based on fluorescent resonance energy transfer (FRET) and its bio-luminescent modification (BRET). They are based on visualization of protein-protein interactions via light or enzymatic excitation of fluorescent or bio-luminescent proteins. These methods allow not only protein localization within the cell or its organelles (or small animals) but they also allow us to quantify fluorescent signals and to discover weak or strong interaction partners. In this review, we explain the principles of FRET and BRET, their applications in the characterization of protein-protein interactions and we describe several findings using these two methods that clarify molecular and cellular mechanisms and signals related to cancer biology.

  15. Prediction of Protein-Protein Interactions Related to Protein Complexes Based on Protein Interaction Networks

    Directory of Open Access Journals (Sweden)

    Peng Liu

    2015-01-01

    Full Text Available A method for predicting protein-protein interactions based on detected protein complexes is proposed to repair deficient interactions derived from high-throughput biological experiments. Protein complexes are pruned and decomposed into small parts based on the adaptive k-cores method to predict protein-protein interactions associated with the complexes. The proposed method is adaptive to protein complexes with different structure, number, and size of nodes in a protein-protein interaction network. Based on different complex sets detected by various algorithms, we can obtain different prediction sets of protein-protein interactions. The reliability of the predicted interaction sets is proved by using estimations with statistical tests and direct confirmation of the biological data. In comparison with the approaches which predict the interactions based on the cliques, the overlap of the predictions is small. Similarly, the overlaps among the predicted sets of interactions derived from various complex sets are also small. Thus, every predicted set of interactions may complement and improve the quality of the original network data. Meanwhile, the predictions from the proposed method replenish protein-protein interactions associated with protein complexes using only the network topology.

  16. Inferring high-confidence human protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Yu Xueping

    2012-05-01

    Full Text Available Abstract Background As numerous experimental factors drive the acquisition, identification, and interpretation of protein-protein interactions (PPIs, aggregated assemblies of human PPI data invariably contain experiment-dependent noise. Ascertaining the reliability of PPIs collected from these diverse studies and scoring them to infer high-confidence networks is a non-trivial task. Moreover, a large number of PPIs share the same number of reported occurrences, making it impossible to distinguish the reliability of these PPIs and rank-order them. For example, for the data analyzed here, we found that the majority (>83% of currently available human PPIs have been reported only once. Results In this work, we proposed an unsupervised statistical approach to score a set of diverse, experimentally identified PPIs from nine primary databases to create subsets of high-confidence human PPI networks. We evaluated this ranking method by comparing it with other methods and assessing their ability to retrieve protein associations from a number of diverse and independent reference sets. These reference sets contain known biological data that are either directly or indirectly linked to interactions between proteins. We quantified the average effect of using ranked protein interaction data to retrieve this information and showed that, when compared to randomly ranked interaction data sets, the proposed method created a larger enrichment (~134% than either ranking based on the hypergeometric test (~109% or occurrence ranking (~46%. Conclusions From our evaluations, it was clear that ranked interactions were always of value because higher-ranked PPIs had a higher likelihood of retrieving high-confidence experimental data. Reducing the noise inherent in aggregated experimental PPIs via our ranking scheme further increased the accuracy and enrichment of PPIs derived from a number of biologically relevant data sets. These results suggest that using our high

  17. A scored human protein-protein interaction network to catalyze genomic interpretation

    DEFF Research Database (Denmark)

    Li, Taibo; Wernersson, Rasmus; Hansen, Rasmus B

    2017-01-01

    Genome-scale human protein-protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein-protein interaction network (InWeb_InBioMap,......Genome-scale human protein-protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein-protein interaction network (In...

  18. Parallel force assay for protein-protein interactions.

    Science.gov (United States)

    Aschenbrenner, Daniela; Pippig, Diana A; Klamecka, Kamila; Limmer, Katja; Leonhardt, Heinrich; Gaub, Hermann E

    2014-01-01

    Quantitative proteome research is greatly promoted by high-resolution parallel format assays. A characterization of protein complexes based on binding forces offers an unparalleled dynamic range and allows for the effective discrimination of non-specific interactions. Here we present a DNA-based Molecular Force Assay to quantify protein-protein interactions, namely the bond between different variants of GFP and GFP-binding nanobodies. We present different strategies to adjust the maximum sensitivity window of the assay by influencing the binding strength of the DNA reference duplexes. The binding of the nanobody Enhancer to the different GFP constructs is compared at high sensitivity of the assay. Whereas the binding strength to wild type and enhanced GFP are equal within experimental error, stronger binding to superfolder GFP is observed. This difference in binding strength is attributed to alterations in the amino acids that form contacts according to the crystal structure of the initial wild type GFP-Enhancer complex. Moreover, we outline the potential for large-scale parallelization of the assay.

  19. Parallel force assay for protein-protein interactions.

    Directory of Open Access Journals (Sweden)

    Daniela Aschenbrenner

    Full Text Available Quantitative proteome research is greatly promoted by high-resolution parallel format assays. A characterization of protein complexes based on binding forces offers an unparalleled dynamic range and allows for the effective discrimination of non-specific interactions. Here we present a DNA-based Molecular Force Assay to quantify protein-protein interactions, namely the bond between different variants of GFP and GFP-binding nanobodies. We present different strategies to adjust the maximum sensitivity window of the assay by influencing the binding strength of the DNA reference duplexes. The binding of the nanobody Enhancer to the different GFP constructs is compared at high sensitivity of the assay. Whereas the binding strength to wild type and enhanced GFP are equal within experimental error, stronger binding to superfolder GFP is observed. This difference in binding strength is attributed to alterations in the amino acids that form contacts according to the crystal structure of the initial wild type GFP-Enhancer complex. Moreover, we outline the potential for large-scale parallelization of the assay.

  20. Protein-Protein Interaction Network and Gene Ontology

    Science.gov (United States)

    Choi, Yunkyu; Kim, Seok; Yi, Gwan-Su; Park, Jinah

    Evolution of computer technologies makes it possible to access a large amount and various kinds of biological data via internet such as DNA sequences, proteomics data and information discovered about them. It is expected that the combination of various data could help researchers find further knowledge about them. Roles of a visualization system are to invoke human abilities to integrate information and to recognize certain patterns in the data. Thus, when the various kinds of data are examined and analyzed manually, an effective visualization system is an essential part. One instance of these integrated visualizations can be combination of protein-protein interaction (PPI) data and Gene Ontology (GO) which could help enhance the analysis of PPI network. We introduce a simple but comprehensive visualization system that integrates GO and PPI data where GO and PPI graphs are visualized side-by-side and supports quick reference functions between them. Furthermore, the proposed system provides several interactive visualization methods for efficiently analyzing the PPI network and GO directedacyclic- graph such as context-based browsing and common ancestors finding.

  1. Unified Alignment of Protein-Protein Interaction Networks.

    Science.gov (United States)

    Malod-Dognin, Noël; Ban, Kristina; Pržulj, Nataša

    2017-04-19

    Paralleling the increasing availability of protein-protein interaction (PPI) network data, several network alignment methods have been proposed. Network alignments have been used to uncover functionally conserved network parts and to transfer annotations. However, due to the computational intractability of the network alignment problem, aligners are heuristics providing divergent solutions and no consensus exists on a gold standard, or which scoring scheme should be used to evaluate them. We comprehensively evaluate the alignment scoring schemes and global network aligners on large scale PPI data and observe that three methods, HUBALIGN, L-GRAAL and NATALIE, regularly produce the most topologically and biologically coherent alignments. We study the collective behaviour of network aligners and observe that PPI networks are almost entirely aligned with a handful of aligners that we unify into a new tool, Ulign. Ulign enables complete alignment of two networks, which traditional global and local aligners fail to do. Also, multiple mappings of Ulign define biologically relevant soft clusterings of proteins in PPI networks, which may be used for refining the transfer of annotations across networks. Hence, PPI networks are already well investigated by current aligners, so to gain additional biological insights, a paradigm shift is needed. We propose such a shift come from aligning all available data types collectively rather than any particular data type in isolation from others.

  2. Targeting protein-protein interactions for parasite control.

    Directory of Open Access Journals (Sweden)

    Christina M Taylor

    2011-04-01

    Full Text Available Finding new drug targets for pathogenic infections would be of great utility for humanity, as there is a large need to develop new drugs to fight infections due to the developing resistance and side effects of current treatments. Current drug targets for pathogen infections involve only a single protein. However, proteins rarely act in isolation, and the majority of biological processes occur via interactions with other proteins, so protein-protein interactions (PPIs offer a realm of unexplored potential drug targets and are thought to be the next-generation of drug targets. Parasitic worms were chosen for this study because they have deleterious effects on human health, livestock, and plants, costing society billions of dollars annually and many sequenced genomes are available. In this study, we present a computational approach that utilizes whole genomes of 6 parasitic and 1 free-living worm species and 2 hosts. The species were placed in orthologous groups, then binned in species-specific orthologous groups. Proteins that are essential and conserved among species that span a phyla are of greatest value, as they provide foundations for developing broad-control strategies. Two PPI databases were used to find PPIs within the species specific bins. PPIs with unique helminth proteins and helminth proteins with unique features relative to the host, such as indels, were prioritized as drug targets. The PPIs were scored based on RNAi phenotype and homology to the PDB (Protein DataBank. EST data for the various life stages, GO annotation, and druggability were also taken into consideration. Several PPIs emerged from this study as potential drug targets. A few interactions were supported by co-localization of expression in M. incognita (plant parasite and B. malayi (H. sapiens parasite, which have extremely different modes of parasitism. As more genomes of pathogens are sequenced and PPI databases expanded, this methodology will become increasingly

  3. Protein-protein interactions within late pre-40S ribosomes.

    Directory of Open Access Journals (Sweden)

    Melody G Campbell

    2011-01-01

    Full Text Available Ribosome assembly in eukaryotic organisms requires more than 200 assembly factors to facilitate and coordinate rRNA transcription, processing, and folding with the binding of the ribosomal proteins. Many of these assembly factors bind and dissociate at defined times giving rise to discrete assembly intermediates, some of which have been partially characterized with regards to their protein and RNA composition. Here, we have analyzed the protein-protein interactions between the seven assembly factors bound to late cytoplasmic pre-40S ribosomes using recombinant proteins in binding assays. Our data show that these factors form two modules: one comprising Enp1 and the export adaptor Ltv1 near the beak structure, and the second comprising the kinase Rio2, the nuclease Nob1, and a regulatory RNA binding protein Dim2/Pno1 on the front of the head. The GTPase-like Tsr1 and the universally conserved methylase Dim1 are also peripherally connected to this second module. Additionally, in an effort to further define the locations for these essential proteins, we have analyzed the interactions between these assembly factors and six ribosomal proteins: Rps0, Rps3, Rps5, Rps14, Rps15 and Rps29. Together, these results and previous RNA-protein crosslinking data allow us to propose a model for the binding sites of these seven assembly factors. Furthermore, our data show that the essential kinase Rio2 is located at the center of the pre-ribosomal particle and interacts, directly or indirectly, with every other assembly factor, as well as three ribosomal proteins required for cytoplasmic 40S maturation. These data suggest that Rio2 could play a central role in regulating cytoplasmic maturation steps.

  4. Quantifying the molecular origins of opposite solvent effects on protein-protein interactions.

    Directory of Open Access Journals (Sweden)

    Vincent Vagenende

    Full Text Available Although the nature of solvent-protein interactions is generally weak and non-specific, addition of cosolvents such as denaturants and osmolytes strengthens protein-protein interactions for some proteins, whereas it weakens protein-protein interactions for others. This is exemplified by the puzzling observation that addition of glycerol oppositely affects the association constants of two antibodies, D1.3 and D44.1, with lysozyme. To resolve this conundrum, we develop a methodology based on the thermodynamic principles of preferential interaction theory and the quantitative characterization of local protein solvation from molecular dynamics simulations. We find that changes of preferential solvent interactions at the protein-protein interface quantitatively account for the opposite effects of glycerol on the antibody-antigen association constants. Detailed characterization of local protein solvation in the free and associated protein states reveals how opposite solvent effects on protein-protein interactions depend on the extent of dewetting of the protein-protein contact region and on structural changes that alter cooperative solvent-protein interactions at the periphery of the protein-protein interface. These results demonstrate the direct relationship between macroscopic solvent effects on protein-protein interactions and atom-scale solvent-protein interactions, and establish a general methodology for predicting and understanding solvent effects on protein-protein interactions in diverse biological environments.

  5. Sequence motifs in MADS transcription factors responsible for specificity and diversification of protein-protein interaction.

    Directory of Open Access Journals (Sweden)

    Aalt D J van Dijk

    Full Text Available Protein sequences encompass tertiary structures and contain information about specific molecular interactions, which in turn determine biological functions of proteins. Knowledge about how protein sequences define interaction specificity is largely missing, in particular for paralogous protein families with high sequence similarity, such as the plant MADS domain transcription factor family. In comparison to the situation in mammalian species, this important family of transcription regulators has expanded enormously in plant species and contains over 100 members in the model plant species Arabidopsis thaliana. Here, we provide insight into the mechanisms that determine protein-protein interaction specificity for the Arabidopsis MADS domain transcription factor family, using an integrated computational and experimental approach. Plant MADS proteins have highly similar amino acid sequences, but their dimerization patterns vary substantially. Our computational analysis uncovered small sequence regions that explain observed differences in dimerization patterns with reasonable accuracy. Furthermore, we show the usefulness of the method for prediction of MADS domain transcription factor interaction networks in other plant species. Introduction of mutations in the predicted interaction motifs demonstrated that single amino acid mutations can have a large effect and lead to loss or gain of specific interactions. In addition, various performed bioinformatics analyses shed light on the way evolution has shaped MADS domain transcription factor interaction specificity. Identified protein-protein interaction motifs appeared to be strongly conserved among orthologs, indicating their evolutionary importance. We also provide evidence that mutations in these motifs can be a source for sub- or neo-functionalization. The analyses presented here take us a step forward in understanding protein-protein interactions and the interplay between protein sequences and

  6. Non-interacting surface solvation and dynamics in protein-protein interactions

    NARCIS (Netherlands)

    Visscher, Koen M.; Kastritis, Panagiotis L.|info:eu-repo/dai/nl/315886668; Bonvin, Alexandre M J J|info:eu-repo/dai/nl/113691238

    2015-01-01

    Protein-protein interactions control a plethora of cellular processes, including cell proliferation, differentiation, apoptosis, and signal transduction. Understanding how and why proteins interact will inevitably lead to novel structure-based drug design methods, as well as design of de novo

  7. Topology-function conservation in protein-protein interaction networks.

    Science.gov (United States)

    Davis, Darren; Yaveroğlu, Ömer Nebil; Malod-Dognin, Noël; Stojmirovic, Aleksandar; Pržulj, Nataša

    2015-05-15

    Proteins underlay the functioning of a cell and the wiring of proteins in protein-protein interaction network (PIN) relates to their biological functions. Proteins with similar wiring in the PIN (topology around them) have been shown to have similar functions. This property has been successfully exploited for predicting protein functions. Topological similarity is also used to guide network alignment algorithms that find similarly wired proteins between PINs of different species; these similarities are used to transfer annotation across PINs, e.g. from model organisms to human. To refine these functional predictions and annotation transfers, we need to gain insight into the variability of the topology-function relationships. For example, a function may be significantly associated with specific topologies, while another function may be weakly associated with several different topologies. Also, the topology-function relationships may differ between different species. To improve our understanding of topology-function relationships and of their conservation among species, we develop a statistical framework that is built upon canonical correlation analysis. Using the graphlet degrees to represent the wiring around proteins in PINs and gene ontology (GO) annotations to describe their functions, our framework: (i) characterizes statistically significant topology-function relationships in a given species, and (ii) uncovers the functions that have conserved topology in PINs of different species, which we term topologically orthologous functions. We apply our framework to PINs of yeast and human, identifying seven biological process and two cellular component GO terms to be topologically orthologous for the two organisms. © The Author 2015. Published by Oxford University Press.

  8. Categorizing Biases in High-Confidence High-Throughput Protein-Protein Interaction Data Sets*

    Science.gov (United States)

    Yu, Xueping; Ivanic, Joseph; Memišević, Vesna; Wallqvist, Anders; Reifman, Jaques

    2011-01-01

    We characterized and evaluated the functional attributes of three yeast high-confidence protein-protein interaction data sets derived from affinity purification/mass spectrometry, protein-fragment complementation assay, and yeast two-hybrid experiments. The interacting proteins retrieved from these data sets formed distinct, partially overlapping sets with different protein-protein interaction characteristics. These differences were primarily a function of the deployed experimental technologies used to recover these interactions. This affected the total coverage of interactions and was especially evident in the recovery of interactions among different functional classes of proteins. We found that the interaction data obtained by the yeast two-hybrid method was the least biased toward any particular functional characterization. In contrast, interacting proteins in the affinity purification/mass spectrometry and protein-fragment complementation assay data sets were over- and under-represented among distinct and different functional categories. We delineated how these differences affected protein complex organization in the network of interactions, in particular for strongly interacting complexes (e.g. RNA and protein synthesis) versus weak and transient interacting complexes (e.g. protein transport). We quantified methodological differences in detecting protein interactions from larger protein complexes, in the correlation of protein abundance among interacting proteins, and in their connectivity of essential proteins. In the latter case, we showed that minimizing inherent methodology biases removed many of the ambiguous conclusions about protein essentiality and protein connectivity. We used these findings to rationalize how biological insights obtained by analyzing data sets originating from different sources sometimes do not agree or may even contradict each other. An important corollary of this work was that discrepancies in biological insights did not

  9. Hot-spot analysis for drug discovery targeting protein-protein interactions.

    Science.gov (United States)

    Rosell, Mireia; Fernández-Recio, Juan

    2018-04-01

    Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.

  10. Inferring domain-domain interactions from protein-protein interactions with formal concept analysis.

    Directory of Open Access Journals (Sweden)

    Susan Khor

    Full Text Available Identifying reliable domain-domain interactions will increase our ability to predict novel protein-protein interactions, to unravel interactions in protein complexes, and thus gain more information about the function and behavior of genes. One of the challenges of identifying reliable domain-domain interactions is domain promiscuity. Promiscuous domains are domains that can occur in many domain architectures and are therefore found in many proteins. This becomes a problem for a method where the score of a domain-pair is the ratio between observed and expected frequencies because the protein-protein interaction network is sparse. As such, many protein-pairs will be non-interacting and domain-pairs with promiscuous domains will be penalized. This domain promiscuity challenge to the problem of inferring reliable domain-domain interactions from protein-protein interactions has been recognized, and a number of work-arounds have been proposed. This paper reports on an application of Formal Concept Analysis to this problem. It is found that the relationship between formal concepts provides a natural way for rare domains to elevate the rank of promiscuous domain-pairs and enrich highly ranked domain-pairs with reliable domain-domain interactions. This piggybacking of promiscuous domain-pairs onto less promiscuous domain-pairs is possible only with concept lattices whose attribute-labels are not reduced and is enhanced by the presence of proteins that comprise both promiscuous and rare domains.

  11. Inferring Domain-Domain Interactions from Protein-Protein Interactions with Formal Concept Analysis

    Science.gov (United States)

    Khor, Susan

    2014-01-01

    Identifying reliable domain-domain interactions will increase our ability to predict novel protein-protein interactions, to unravel interactions in protein complexes, and thus gain more information about the function and behavior of genes. One of the challenges of identifying reliable domain-domain interactions is domain promiscuity. Promiscuous domains are domains that can occur in many domain architectures and are therefore found in many proteins. This becomes a problem for a method where the score of a domain-pair is the ratio between observed and expected frequencies because the protein-protein interaction network is sparse. As such, many protein-pairs will be non-interacting and domain-pairs with promiscuous domains will be penalized. This domain promiscuity challenge to the problem of inferring reliable domain-domain interactions from protein-protein interactions has been recognized, and a number of work-arounds have been proposed. This paper reports on an application of Formal Concept Analysis to this problem. It is found that the relationship between formal concepts provides a natural way for rare domains to elevate the rank of promiscuous domain-pairs and enrich highly ranked domain-pairs with reliable domain-domain interactions. This piggybacking of promiscuous domain-pairs onto less promiscuous domain-pairs is possible only with concept lattices whose attribute-labels are not reduced and is enhanced by the presence of proteins that comprise both promiscuous and rare domains. PMID:24586450

  12. Testing strong interaction theories

    International Nuclear Information System (INIS)

    Ellis, J.

    1979-01-01

    The author discusses possible tests of the current theories of the strong interaction, in particular, quantum chromodynamics. High energy e + e - interactions should provide an excellent means of studying the strong force. (W.D.L.)

  13. Specificity of molecular interactions in transient protein-protein interaction interfaces.

    Science.gov (United States)

    Cho, Kyu-il; Lee, KiYoung; Lee, Kwang H; Kim, Dongsup; Lee, Doheon

    2006-11-15

    In this study, we investigate what types of interactions are specific to their biological function, and what types of interactions are persistent regardless of their functional category in transient protein-protein heterocomplexes. This is the first approach to analyze protein-protein interfaces systematically at the molecular interaction level in the context of protein functions. We perform systematic analysis at the molecular interaction level using classification and feature subset selection technique prevalent in the field of pattern recognition. To represent the physicochemical properties of protein-protein interfaces, we design 18 molecular interaction types using canonical and noncanonical interactions. Then, we construct input vector using the frequency of each interaction type in protein-protein interface. We analyze the 131 interfaces of transient protein-protein heterocomplexes in PDB: 33 protease-inhibitors, 52 antibody-antigens, 46 signaling proteins including 4 cyclin dependent kinase and 26 G-protein. Using kNN classification and feature subset selection technique, we show that there are specific interaction types based on their functional category, and such interaction types are conserved through the common binding mechanism, rather than through the sequence or structure conservation. The extracted interaction types are C(alpha)-- H...O==C interaction, cation...anion interaction, amine...amine interaction, and amine...cation interaction. With these four interaction types, we achieve the classification success rate up to 83.2% with leave-one-out cross-validation at k = 15. Of these four interaction types, C(alpha)--H...O==C shows binding specificity for protease-inhibitor complexes, while cation-anion interaction is predominant in signaling complexes. The amine ... amine and amine...cation interaction give a minor contribution to the classification accuracy. When combined with these two interactions, they increase the accuracy by 3.8%. In the case of

  14. The role of electrostatics in protein-protein interactions of a monoclonal antibody.

    Science.gov (United States)

    Roberts, D; Keeling, R; Tracka, M; van der Walle, C F; Uddin, S; Warwicker, J; Curtis, R

    2014-07-07

    Understanding how protein-protein interactions depend on the choice of buffer, salt, ionic strength, and pH is needed to have better control over protein solution behavior. Here, we have characterized the pH and ionic strength dependence of protein-protein interactions in terms of an interaction parameter kD obtained from dynamic light scattering and the osmotic second virial coefficient B22 measured by static light scattering. A simplified protein-protein interaction model based on a Baxter adhesive potential and an electric double layer force is used to separate out the contributions of longer-ranged electrostatic interactions from short-ranged attractive forces. The ionic strength dependence of protein-protein interactions for solutions at pH 6.5 and below can be accurately captured using a Deryaguin-Landau-Verwey-Overbeek (DLVO) potential to describe the double layer forces. In solutions at pH 9, attractive electrostatics occur over the ionic strength range of 5-275 mM. At intermediate pH values (7.25 to 8.5), there is a crossover effect characterized by a nonmonotonic ionic strength dependence of protein-protein interactions, which can be rationalized by the competing effects of long-ranged repulsive double layer forces at low ionic strength and a shorter ranged electrostatic attraction, which dominates above a critical ionic strength. The change of interactions from repulsive to attractive indicates a concomitant change in the angular dependence of protein-protein interaction from isotropic to anisotropic. In the second part of the paper, we show how the Baxter adhesive potential can be used to predict values of kD from fitting to B22 measurements, thus providing a molecular basis for the linear correlation between the two protein-protein interaction parameters.

  15. A credit-card library approach for disrupting protein-protein interactions.

    Science.gov (United States)

    Xu, Yang; Shi, Jin; Yamamoto, Noboru; Moss, Jason A; Vogt, Peter K; Janda, Kim D

    2006-04-15

    Protein-protein interfaces are prominent in many therapeutically important targets. Using small organic molecules to disrupt protein-protein interactions is a current challenge in chemical biology. An important example of protein-protein interactions is provided by the Myc protein, which is frequently deregulated in human cancers. Myc belongs to the family of basic helix-loop-helix leucine zipper (bHLH-ZIP) transcription factors. It is biologically active only as heterodimer with the bHLH-ZIP protein Max. Herein, we report a new strategy for the disruption of protein-protein interactions that has been corroborated through the design and synthesis of a small parallel library composed of 'credit-card' compounds. These compounds are derived from a planar, aromatic scaffold and functionalized with four points of diversity. From a 285 membered library, several hits were obtained that disrupted the c-Myc-Max interaction and cellular functions of c-Myc. The IC50 values determined for this small focused library for the disruption of Myc-Max dimerization are quite potent, especially since small molecule antagonists of protein-protein interactions are notoriously difficult to find. Furthermore, several of the compounds were active at the cellular level as shown by their biological effects on Myc action in chicken embryo fibroblast assays. In light of our findings, this approach is considered a valuable addition to the armamentarium of new molecules being developed to interact with protein-protein interfaces. Finally, this strategy for disrupting protein-protein interactions should prove applicable to other families of proteins.

  16. NatalieQ: A web server for protein-protein interaction network querying

    NARCIS (Netherlands)

    El-Kebir, M.; Brandt, B.W.; Heringa, J.; Klau, G.W.

    2014-01-01

    Background Molecular interactions need to be taken into account to adequately model the complex behavior of biological systems. These interactions are captured by various types of biological networks, such as metabolic, gene-regulatory, signal transduction and protein-protein interaction networks.

  17. Affinity purification combined with mass spectrometry to identify herpes simplex virus protein-protein interactions.

    Science.gov (United States)

    Meckes, David G

    2014-01-01

    The identification and characterization of herpes simplex virus protein interaction complexes are fundamental to understanding the molecular mechanisms governing the replication and pathogenesis of the virus. Recent advances in affinity-based methods, mass spectrometry configurations, and bioinformatics tools have greatly increased the quantity and quality of protein-protein interaction datasets. In this chapter, detailed and reliable methods that can easily be implemented are presented for the identification of protein-protein interactions using cryogenic cell lysis, affinity purification, trypsin digestion, and mass spectrometry.

  18. Coevolution study of mitochondria respiratory chain proteins: toward the understanding of protein--protein interaction.

    Science.gov (United States)

    Yang, Ming; Ge, Yan; Wu, Jiayan; Xiao, Jingfa; Yu, Jun

    2011-05-20

    Coevolution can be seen as the interdependency between evolutionary histories. In the context of protein evolution, functional correlation proteins are ever-present coordinated evolutionary characters without disruption of organismal integrity. As to complex system, there are two forms of protein--protein interactions in vivo, which refer to inter-complex interaction and intra-complex interaction. In this paper, we studied the difference of coevolution characters between inter-complex interaction and intra-complex interaction using "Mirror tree" method on the respiratory chain (RC) proteins. We divided the correlation coefficients of every pairwise RC proteins into two groups corresponding to the binary protein--protein interaction in intra-complex and the binary protein--protein interaction in inter-complex, respectively. A dramatical discrepancy is detected between the coevolution characters of the two sets of protein interactions (Wilcoxon test, p-value = 4.4 × 10(-6)). Our finding reveals some critical information on coevolutionary study and assists the mechanical investigation of protein--protein interaction. Furthermore, the results also provide some unique clue for supramolecular organization of protein complexes in the mitochondrial inner membrane. More detailed binding sites map and genome information of nuclear encoded RC proteins will be extraordinary valuable for the further mitochondria dynamics study. Copyright © 2011. Published by Elsevier Ltd.

  19. Emergence of modularity and disassortativity in protein-protein interaction networks.

    Science.gov (United States)

    Wan, Xi; Cai, Shuiming; Zhou, Jin; Liu, Zengrong

    2010-12-01

    In this paper, we present a simple evolution model of protein-protein interaction networks by introducing a rule of small-preference duplication of a node, meaning that the probability of a node chosen to duplicate is inversely proportional to its degree, and subsequent divergence plus nonuniform heterodimerization based on some plausible mechanisms in biology. We show that our model cannot only reproduce scale-free connectivity and small-world pattern, but also exhibit hierarchical modularity and disassortativity. After comparing the features of our model with those of real protein-protein interaction networks, we believe that our model can provide relevant insights into the mechanism underlying the evolution of protein-protein interaction networks. © 2010 American Institute of Physics.

  20. Proteins interacting with cloning scars: a source of false positive protein-protein interactions.

    Science.gov (United States)

    Banks, Charles A S; Boanca, Gina; Lee, Zachary T; Florens, Laurence; Washburn, Michael P

    2015-02-23

    A common approach for exploring the interactome, the network of protein-protein interactions in cells, uses a commercially available ORF library to express affinity tagged bait proteins; these can be expressed in cells and endogenous cellular proteins that copurify with the bait can be identified as putative interacting proteins using mass spectrometry. Control experiments can be used to limit false-positive results, but in many cases, there are still a surprising number of prey proteins that appear to copurify specifically with the bait. Here, we have identified one source of false-positive interactions in such studies. We have found that a combination of: 1) the variable sequence of the C-terminus of the bait with 2) a C-terminal valine "cloning scar" present in a commercially available ORF library, can in some cases create a peptide motif that results in the aberrant co-purification of endogenous cellular proteins. Control experiments may not identify false positives resulting from such artificial motifs, as aberrant binding depends on sequences that vary from one bait to another. It is possible that such cryptic protein binding might occur in other systems using affinity tagged proteins; this study highlights the importance of conducting careful follow-up studies where novel protein-protein interactions are suspected.

  1. Analysis of protein-protein interaction networks by means of annotated graph mining algorithms

    NARCIS (Netherlands)

    Rahmani, Hossein

    2012-01-01

    This thesis discusses solutions to several open problems in Protein-Protein Interaction (PPI) networks with the aid of Knowledge Discovery. PPI networks are usually represented as undirected graphs, with nodes corresponding to proteins and edges representing interactions among protein pairs. A large

  2. A proteomics strategy to elucidate functional protein-protein interactions applied to EGF signaling

    DEFF Research Database (Denmark)

    Blagoev, B.; Kratchmarova, I.; Ong, S.E.

    2003-01-01

    Mass spectrometry-based proteomics can reveal protein-protein interactions on a large scale, but it has been difficult to separate background binding from functionally important interactions and still preserve weak binders. To investigate the epidermal growth factor receptor (EGFR) pathway, we em...

  3. An ontology-based search engine for protein-protein interactions.

    Science.gov (United States)

    Park, Byungkyu; Han, Kyungsook

    2010-01-18

    Keyword matching or ID matching is the most common searching method in a large database of protein-protein interactions. They are purely syntactic methods, and retrieve the records in the database that contain a keyword or ID specified in a query. Such syntactic search methods often retrieve too few search results or no results despite many potential matches present in the database. We have developed a new method for representing protein-protein interactions and the Gene Ontology (GO) using modified Gödel numbers. This representation is hidden from users but enables a search engine using the representation to efficiently search protein-protein interactions in a biologically meaningful way. Given a query protein with optional search conditions expressed in one or more GO terms, the search engine finds all the interaction partners of the query protein by unique prime factorization of the modified Gödel numbers representing the query protein and the search conditions. Representing the biological relations of proteins and their GO annotations by modified Gödel numbers makes a search engine efficiently find all protein-protein interactions by prime factorization of the numbers. Keyword matching or ID matching search methods often miss the interactions involving a protein that has no explicit annotations matching the search condition, but our search engine retrieves such interactions as well if they satisfy the search condition with a more specific term in the ontology.

  4. PPI-IRO: A two-stage method for protein-protein interaction extraction based on interaction relation ontology

    KAUST Repository

    Li, Chuanxi; Chen, Peng; Wang, Rujing; Wang, Xiujie; Su, Yaru; Li, Jinyan

    2014-01-01

    Mining Protein-Protein Interactions (PPIs) from the fast-growing biomedical literature resources has been proven as an effective approach for the identifi cation of biological regulatory networks. This paper presents a novel method based on the idea

  5. Essential multimeric enzymes in kinetoplastid parasites: A host of potentially druggable protein-protein interactions.

    Science.gov (United States)

    Wachsmuth, Leah M; Johnson, Meredith G; Gavenonis, Jason

    2017-06-01

    Parasitic diseases caused by kinetoplastid parasites of the genera Trypanosoma and Leishmania are an urgent public health crisis in the developing world. These closely related species possess a number of multimeric enzymes in highly conserved pathways involved in vital functions, such as redox homeostasis and nucleotide synthesis. Computational alanine scanning of these protein-protein interfaces has revealed a host of potentially ligandable sites on several established and emerging anti-parasitic drug targets. Analysis of interfaces with multiple clustered hotspots has suggested several potentially inhibitable protein-protein interactions that may have been overlooked by previous large-scale analyses focusing solely on secondary structure. These protein-protein interactions provide a promising lead for the development of new peptide and macrocycle inhibitors of these enzymes.

  6. Protein- protein interaction detection system using fluorescent protein microdomains

    Science.gov (United States)

    Waldo, Geoffrey S.; Cabantous, Stephanie

    2010-02-23

    The invention provides a protein labeling and interaction detection system based on engineered fragments of fluorescent and chromophoric proteins that require fused interacting polypeptides to drive the association of the fragments, and further are soluble and stable, and do not change the solubility of polypeptides to which they are fused. In one embodiment, a test protein X is fused to a sixteen amino acid fragment of GFP (.beta.-strand 10, amino acids 198-214), engineered to not perturb fusion protein solubility. A second test protein Y is fused to a sixteen amino acid fragment of GFP (.beta.-strand 11, amino acids 215-230), engineered to not perturb fusion protein solubility. When X and Y interact, they bring the GFP strands into proximity, and are detected by complementation with a third GFP fragment consisting of GFP amino acids 1-198 (strands 1-9). When GFP strands 10 and 11 are held together by interaction of protein X and Y, they spontaneous association with GFP strands 1-9, resulting in structural complementation, folding, and concomitant GFP fluorescence.

  7. Molecular simulations of lipid-mediated protein-protein interactions

    NARCIS (Netherlands)

    de Meyer, F.J.M.; Venturoli, M.; Smit, B.

    2008-01-01

    Recent experimental results revealed that lipid-mediated interactions due to hydrophobic forces may be important in determining the protein topology after insertion in the membrane, in regulating the protein activity, in protein aggregation and in signal transduction. To gain insight into the

  8. Alignment of non-covalent interactions at protein-protein interfaces.

    Directory of Open Access Journals (Sweden)

    Hongbo Zhu

    Full Text Available BACKGROUND: The study and comparison of protein-protein interfaces is essential for the understanding of the mechanisms of interaction between proteins. While there are many methods for comparing protein structures and protein binding sites, so far no methods have been reported for comparing the geometry of non-covalent interactions occurring at protein-protein interfaces. METHODOLOGY/PRINCIPAL FINDINGS: Here we present a method for aligning non-covalent interactions between different protein-protein interfaces. The method aligns the vector representations of van der Waals interactions and hydrogen bonds based on their geometry. The method has been applied to a dataset which comprises a variety of protein-protein interfaces. The alignments are consistent to a large extent with the results obtained using two other complementary approaches. In addition, we apply the method to three examples of protein mimicry. The method successfully aligns respective interfaces and allows for recognizing conserved interface regions. CONCLUSIONS/SIGNIFICANCE: The Galinter method has been validated in the comparison of interfaces in which homologous subunits are involved, including cases of mimicry. The method is also applicable to comparing interfaces involving non-peptidic compounds. Galinter assists users in identifying local interface regions with similar patterns of non-covalent interactions. This is particularly relevant to the investigation of the molecular basis of interaction mimicry.

  9. Evaluation of clustering algorithms for protein-protein interaction networks

    Directory of Open Access Journals (Sweden)

    van Helden Jacques

    2006-11-01

    Full Text Available Abstract Background Protein interactions are crucial components of all cellular processes. Recently, high-throughput methods have been developed to obtain a global description of the interactome (the whole network of protein interactions for a given organism. In 2002, the yeast interactome was estimated to contain up to 80,000 potential interactions. This estimate is based on the integration of data sets obtained by various methods (mass spectrometry, two-hybrid methods, genetic studies. High-throughput methods are known, however, to yield a non-negligible rate of false positives, and to miss a fraction of existing interactions. The interactome can be represented as a graph where nodes correspond with proteins and edges with pairwise interactions. In recent years clustering methods have been developed and applied in order to extract relevant modules from such graphs. These algorithms require the specification of parameters that may drastically affect the results. In this paper we present a comparative assessment of four algorithms: Markov Clustering (MCL, Restricted Neighborhood Search Clustering (RNSC, Super Paramagnetic Clustering (SPC, and Molecular Complex Detection (MCODE. Results A test graph was built on the basis of 220 complexes annotated in the MIPS database. To evaluate the robustness to false positives and false negatives, we derived 41 altered graphs by randomly removing edges from or adding edges to the test graph in various proportions. Each clustering algorithm was applied to these graphs with various parameter settings, and the clusters were compared with the annotated complexes. We analyzed the sensitivity of the algorithms to the parameters and determined their optimal parameter values. We also evaluated their robustness to alterations of the test graph. We then applied the four algorithms to six graphs obtained from high-throughput experiments and compared the resulting clusters with the annotated complexes. Conclusion This

  10. Deciphering peculiar protein-protein interacting modules in Deinococcus radiodurans

    Directory of Open Access Journals (Sweden)

    Barkallah Insaf

    2009-04-01

    Full Text Available Abstract Interactomes of proteins under positive selection from ionizing-radiation-resistant bacteria (IRRB might be a part of the answer to the question as to how IRRB, particularly Deinococcus radiodurans R1 (Deira, resist ionizing radiation. Here, using the Database of Interacting Proteins (DIP and the Protein Structural Interactome (PSI-base server for PSI map, we have predicted novel interactions of orthologs of the 58 proteins under positive selection in Deira and other IRRB, but which are absent in IRSB. Among these, 18 domains and their interactomes have been identified in DNA checkpoint and repair; kinases pathways; energy and nucleotide metabolisms were the important biological processes that were found to be involved. This finding provides new clues to the cellular pathways that can to be important for ionizing-radiation resistance in Deira.

  11. A Mesoscopic Model for Protein-Protein Interactions in Solution

    OpenAIRE

    Lund, Mikael; Jönsson, Bo

    2003-01-01

    Protein self-association may be detrimental in biological systems, but can be utilized in a controlled fashion for protein crystallization. It is hence of considerable interest to understand how factors like solution conditions prevent or promote aggregation. Here we present a computational model describing interactions between protein molecules in solution. The calculations are based on a molecular description capturing the detailed structure of the protein molecule using x-ray or nuclear ma...

  12. Evolutionary diversification of protein-protein interactions by interface add-ons.

    Science.gov (United States)

    Plach, Maximilian G; Semmelmann, Florian; Busch, Florian; Busch, Markus; Heizinger, Leonhard; Wysocki, Vicki H; Merkl, Rainer; Sterner, Reinhard

    2017-10-03

    Cells contain a multitude of protein complexes whose subunits interact with high specificity. However, the number of different protein folds and interface geometries found in nature is limited. This raises the question of how protein-protein interaction specificity is achieved on the structural level and how the formation of nonphysiological complexes is avoided. Here, we describe structural elements called interface add-ons that fulfill this function and elucidate their role for the diversification of protein-protein interactions during evolution. We identified interface add-ons in 10% of a representative set of bacterial, heteromeric protein complexes. The importance of interface add-ons for protein-protein interaction specificity is demonstrated by an exemplary experimental characterization of over 30 cognate and hybrid glutamine amidotransferase complexes in combination with comprehensive genetic profiling and protein design. Moreover, growth experiments showed that the lack of interface add-ons can lead to physiologically harmful cross-talk between essential biosynthetic pathways. In sum, our complementary in silico, in vitro, and in vivo analysis argues that interface add-ons are a practical and widespread evolutionary strategy to prevent the formation of nonphysiological complexes by specializing protein-protein interactions.

  13. An analysis pipeline for the inference of protein-protein interaction networks

    Energy Technology Data Exchange (ETDEWEB)

    Taylor, Ronald C.; Singhal, Mudita; Daly, Don S.; Gilmore, Jason M.; Cannon, William R.; Domico, Kelly O.; White, Amanda M.; Auberry, Deanna L.; Auberry, Kenneth J.; Hooker, Brian S.; Hurst, G. B.; McDermott, Jason E.; McDonald, W. H.; Pelletier, Dale A.; Schmoyer, Denise A.; Wiley, H. S.

    2009-12-01

    An analysis pipeline has been created for deployment of a novel algorithm, the Bayesian Estimator of Protein-Protein Association Probabilities (BEPro), for use in the reconstruction of protein-protein interaction networks. We have combined the Software Environment for BIological Network Inference (SEBINI), an interactive environment for the deployment and testing of network inference algorithms that use high-throughput data, and the Collective Analysis of Biological Interaction Networks (CABIN), software that allows integration and analysis of protein-protein interaction and gene-to-gene regulatory evidence obtained from multiple sources, to allow interactions computed by BEPro to be stored, visualized, and further analyzed. Incorporating BEPro into SEBINI and automatically feeding the resulting inferred network into CABIN, we have created a structured workflow for protein-protein network inference and supplemental analysis from sets of mass spectrometry bait-prey experiment data. SEBINI demo site: https://www.emsl.pnl.gov /SEBINI/ Contact: ronald.taylor@pnl.gov. BEPro is available at http://www.pnl.gov/statistics/BEPro3/index.htm. Contact: ds.daly@pnl.gov. CABIN is available at http://www.sysbio.org/dataresources/cabin.stm. Contact: mudita.singhal@pnl.gov.

  14. Protein-protein interaction inference based on semantic similarity of Gene Ontology terms.

    Science.gov (United States)

    Zhang, Shu-Bo; Tang, Qiang-Rong

    2016-07-21

    Identifying protein-protein interactions is important in molecular biology. Experimental methods to this issue have their limitations, and computational approaches have attracted more and more attentions from the biological community. The semantic similarity derived from the Gene Ontology (GO) annotation has been regarded as one of the most powerful indicators for protein interaction. However, conventional methods based on GO similarity fail to take advantage of the specificity of GO terms in the ontology graph. We proposed a GO-based method to predict protein-protein interaction by integrating different kinds of similarity measures derived from the intrinsic structure of GO graph. We extended five existing methods to derive the semantic similarity measures from the descending part of two GO terms in the GO graph, then adopted a feature integration strategy to combines both the ascending and the descending similarity scores derived from the three sub-ontologies to construct various kinds of features to characterize each protein pair. Support vector machines (SVM) were employed as discriminate classifiers, and five-fold cross validation experiments were conducted on both human and yeast protein-protein interaction datasets to evaluate the performance of different kinds of integrated features, the experimental results suggest the best performance of the feature that combines information from both the ascending and the descending parts of the three ontologies. Our method is appealing for effective prediction of protein-protein interaction. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. Strongly interacting Fermi gases

    Directory of Open Access Journals (Sweden)

    Bakr W.

    2013-08-01

    Full Text Available Strongly interacting gases of ultracold fermions have become an amazingly rich test-bed for many-body theories of fermionic matter. Here we present our recent experiments on these systems. Firstly, we discuss high-precision measurements on the thermodynamics of a strongly interacting Fermi gas across the superfluid transition. The onset of superfluidity is directly observed in the compressibility, the chemical potential, the entropy, and the heat capacity. Our measurements provide benchmarks for current many-body theories on strongly interacting fermions. Secondly, we have studied the evolution of fermion pairing from three to two dimensions in these gases, relating to the physics of layered superconductors. In the presence of p-wave interactions, Fermi gases are predicted to display toplogical superfluidity carrying Majorana edge states. Two possible avenues in this direction are discussed, our creation and direct observation of spin-orbit coupling in Fermi gases and the creation of fermionic molecules of 23Na 40K that will feature strong dipolar interactions in their absolute ground state.

  16. The effect of protein-protein and protein-membrane interactions on membrane fouling in ultrafiltration

    NARCIS (Netherlands)

    Huisman, I.H.; Prádanos, P.; Hernández, A.

    2000-01-01

    It was studied how protein-protein and protein-membrane interactions influence the filtration performance during the ultrafiltration of protein solutions over polymeric membranes. This was done by measuring flux, streaming potential, and protein transmission during filtration of bovine serum albumin

  17. Manipulating fatty acid biosynthesis in microalgae for biofuel through protein-protein interactions.

    Directory of Open Access Journals (Sweden)

    Jillian L Blatti

    Full Text Available Microalgae are a promising feedstock for renewable fuels, and algal metabolic engineering can lead to crop improvement, thus accelerating the development of commercially viable biodiesel production from algae biomass. We demonstrate that protein-protein interactions between the fatty acid acyl carrier protein (ACP and thioesterase (TE govern fatty acid hydrolysis within the algal chloroplast. Using green microalga Chlamydomonas reinhardtii (Cr as a model, a structural simulation of docking CrACP to CrTE identifies a protein-protein recognition surface between the two domains. A virtual screen reveals plant TEs with similar in silico binding to CrACP. Employing an activity-based crosslinking probe designed to selectively trap transient protein-protein interactions between the TE and ACP, we demonstrate in vitro that CrTE must functionally interact with CrACP to release fatty acids, while TEs of vascular plants show no mechanistic crosslinking to CrACP. This is recapitulated in vivo, where overproduction of the endogenous CrTE increased levels of short-chain fatty acids and engineering plant TEs into the C. reinhardtii chloroplast did not alter the fatty acid profile. These findings highlight the critical role of protein-protein interactions in manipulating fatty acid biosynthesis for algae biofuel engineering as illuminated by activity-based probes.

  18. Filtering high-throughput protein-protein interaction data using a combination of genomic features

    Directory of Open Access Journals (Sweden)

    Patil Ashwini

    2005-04-01

    Full Text Available Abstract Background Protein-protein interaction data used in the creation or prediction of molecular networks is usually obtained from large scale or high-throughput experiments. This experimental data is liable to contain a large number of spurious interactions. Hence, there is a need to validate the interactions and filter out the incorrect data before using them in prediction studies. Results In this study, we use a combination of 3 genomic features – structurally known interacting Pfam domains, Gene Ontology annotations and sequence homology – as a means to assign reliability to the protein-protein interactions in Saccharomyces cerevisiae determined by high-throughput experiments. Using Bayesian network approaches, we show that protein-protein interactions from high-throughput data supported by one or more genomic features have a higher likelihood ratio and hence are more likely to be real interactions. Our method has a high sensitivity (90% and good specificity (63%. We show that 56% of the interactions from high-throughput experiments in Saccharomyces cerevisiae have high reliability. We use the method to estimate the number of true interactions in the high-throughput protein-protein interaction data sets in Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens to be 27%, 18% and 68% respectively. Our results are available for searching and downloading at http://helix.protein.osaka-u.ac.jp/htp/. Conclusion A combination of genomic features that include sequence, structure and annotation information is a good predictor of true interactions in large and noisy high-throughput data sets. The method has a very high sensitivity and good specificity and can be used to assign a likelihood ratio, corresponding to the reliability, to each interaction.

  19. On the analysis of protein-protein interactions via knowledge-based potentials for the prediction of protein-protein docking

    DEFF Research Database (Denmark)

    Feliu, Elisenda; Aloy, Patrick; Oliva, Baldo

    2011-01-01

    Development of effective methods to screen binary interactions obtained by rigid-body protein-protein docking is key for structure prediction of complexes and for elucidating physicochemical principles of protein-protein binding. We have derived empirical knowledge-based potential functions for s...... and with independence of the partner. This information is encoded at the residue level and could be easily incorporated in the initial grid scoring for Fast Fourier Transform rigid-body docking methods.......Development of effective methods to screen binary interactions obtained by rigid-body protein-protein docking is key for structure prediction of complexes and for elucidating physicochemical principles of protein-protein binding. We have derived empirical knowledge-based potential functions...... for selecting rigid-body docking poses. These potentials include the energetic component that provides the residues with a particular secondary structure and surface accessibility. These scoring functions have been tested on a state-of-art benchmark dataset and on a decoy dataset of permanent interactions. Our...

  20. A Structural Perspective on the Modulation of Protein-Protein Interactions with Small Molecules.

    Science.gov (United States)

    Demirel, Habibe Cansu; Dogan, Tunca; Tuncbag, Nurcan

    2018-05-31

    Protein-protein interactions (PPIs) are the key components in many cellular processes including signaling pathways, enzymatic reactions and epigenetic regulation. Abnormal interactions of some proteins may be pathogenic and cause various disorders including cancer and neurodegenerative diseases. Although inhibiting PPIs with small molecules is a challenging task, it gained an increasing interest because of its strong potential for drug discovery and design. The knowledge of the interface as well as the structural and chemical characteristics of the PPIs and their roles in the cellular pathways are necessary for a rational design of small molecules to modulate PPIs. In this study, we review the recent progress in the field and detail the physicochemical properties of PPIs including binding hot spots with a focus on structural methods. Then, we review recent approaches for structural prediction of PPIs. Finally, we revisit the concept of targeting PPIs in a systems biology perspective and we refer to the non-structural approaches, usually employed when the structural information is not present. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  1. Targeting Plant Ethylene Responses by Controlling Essential Protein-Protein Interactions in the Ethylene Pathway.

    Science.gov (United States)

    Bisson, Melanie M A; Groth, Georg

    2015-08-01

    The gaseous plant hormone ethylene regulates many processes of high agronomic relevance throughout the life span of plants. A central element in ethylene signaling is the endoplasmic reticulum (ER)-localized membrane protein ethylene insensitive2 (EIN2). Recent studies indicate that in response to ethylene, the extra-membranous C-terminal end of EIN2 is proteolytically processed and translocated from the ER to the nucleus. Here, we report that the conserved nuclear localization signal (NLS) mediating nuclear import of the EIN2 C-terminus provides an important domain for complex formation with ethylene receptor ethylene response1 (ETR1). EIN2 lacking the NLS domain shows strongly reduced affinity for the receptor. Interaction of EIN2 and ETR1 is also blocked by a synthetic peptide of the NLS motif. The corresponding peptide substantially reduces ethylene responses in planta. Our results uncover a novel mechanism and type of inhibitor interfering with ethylene signal transduction and ethylene responses in plants. Disruption of essential protein-protein interactions in the ethylene signaling pathway as shown in our study for the EIN2-ETR1 complex has the potential to guide the development of innovative ethylene antagonists for modern agriculture and horticulture. Copyright © 2015 The Author. Published by Elsevier Inc. All rights reserved.

  2. Protein-protein interactions: an application of Tus-Ter mediated protein microarray system.

    Science.gov (United States)

    Sitaraman, Kalavathy; Chatterjee, Deb K

    2011-01-01

    In this chapter, we present a novel, cost-effective microarray strategy that utilizes expression-ready plasmid DNAs to generate protein arrays on-demand and its use to validate protein-protein interactions. These expression plasmids were constructed in such a way so as to serve a dual purpose of synthesizing the protein of interest as well as capturing the synthesized protein. The microarray system is based on the high affinity binding of Escherichia coli "Tus" protein to "Ter," a 20 bp DNA sequence involved in the regulation of DNA replication. The protein expression is carried out in a cell-free protein synthesis system, with rabbit reticulocyte lysates, and the target proteins are detected either by labeled incorporated tag specific or by gene-specific antibodies. This microarray system has been successfully used for the detection of protein-protein interaction because both the target protein and the query protein can be transcribed and translated simultaneously in the microarray slides. The utility of this system for detecting protein-protein interaction is demonstrated by a few well-known examples: Jun/Fos, FRB/FKBP12, p53/MDM2, and CDK4/p16. In all these cases, the presence of protein complexes resulted in the localization of fluorophores at the specific sites of the immobilized target plasmids. Interestingly, during our interactions studies we also detected a previously unknown interaction between CDK2 and p16. Thus, this Tus-Ter based system of protein microarray can be used for the validation of known protein interactions as well as for identifying new protein-protein interactions. In addition, it can be used to examine and identify targets of nucleic acid-protein, ligand-receptor, enzyme-substrate, and drug-protein interactions.

  3. Using the clustered circular layout as an informative method for visualizing protein-protein interaction networks.

    Science.gov (United States)

    Fung, David C Y; Wilkins, Marc R; Hart, David; Hong, Seok-Hee

    2010-07-01

    The force-directed layout is commonly used in computer-generated visualizations of protein-protein interaction networks. While it is good for providing a visual outline of the protein complexes and their interactions, it has two limitations when used as a visual analysis method. The first is poor reproducibility. Repeated running of the algorithm does not necessarily generate the same layout, therefore, demanding cognitive readaptation on the investigator's part. The second limitation is that it does not explicitly display complementary biological information, e.g. Gene Ontology, other than the protein names or gene symbols. Here, we present an alternative layout called the clustered circular layout. Using the human DNA replication protein-protein interaction network as a case study, we compared the two network layouts for their merits and limitations in supporting visual analysis.

  4. Cell penetrating peptides to dissect host-pathogen protein-protein interactions in Theileria -transformed leukocytes

    KAUST Repository

    Haidar, Malak

    2017-09-08

    One powerful application of cell penetrating peptides is the delivery into cells of molecules that function as specific competitors or inhibitors of protein-protein interactions. Ablating defined protein-protein interactions is a refined way to explore their contribution to a particular cellular phenotype in a given disease context. Cell-penetrating peptides can be synthetically constrained through various chemical modifications that stabilize a given structural fold with the potential to improve competitive binding to specific targets. Theileria-transformed leukocytes display high PKA activity, but PKAis an enzyme that plays key roles in multiple cellular processes; consequently genetic ablation of kinase activity gives rise to a myriad of confounding phenotypes. By contrast, ablation of a specific kinase-substrate interaction has the potential to give more refined information and we illustrate this here by describing how surgically ablating PKA interactions with BAD gives precise information on the type of glycolysis performed by Theileria-transformed leukocytes. In addition, we provide two other examples of how ablating specific protein-protein interactions in Theileria-infected leukocytes leads to precise phenotypes and argue that constrained penetrating peptides have great therapeutic potential to combat infectious diseases in general.

  5. Bioluminescence resonance energy transfer system for measuring dynamic protein-protein interactions in bacteria.

    Science.gov (United States)

    Cui, Boyu; Wang, Yao; Song, Yunhong; Wang, Tietao; Li, Changfu; Wei, Yahong; Luo, Zhao-Qing; Shen, Xihui

    2014-05-20

    Protein-protein interactions are important for virtually every biological process, and a number of elegant approaches have been designed to detect and evaluate such interactions. However, few of these methods allow the detection of dynamic and real-time protein-protein interactions in bacteria. Here we describe a bioluminescence resonance energy transfer (BRET) system based on the bacterial luciferase LuxAB. We found that enhanced yellow fluorescent protein (eYFP) accepts the emission from LuxAB and emits yellow fluorescence. Importantly, BRET occurred when LuxAB and eYFP were fused, respectively, to the interacting protein pair FlgM and FliA. Furthermore, we observed sirolimus (i.e., rapamycin)-inducible interactions between FRB and FKBP12 and a dose-dependent abolishment of such interactions by FK506, the ligand of FKBP12. Using this system, we showed that osmotic stress or low pH efficiently induced multimerization of the regulatory protein OmpR and that the multimerization induced by low pH can be reversed by a neutralizing agent, further indicating the usefulness of this system in the measurement of dynamic interactions. This method can be adapted to analyze dynamic protein-protein interactions and the importance of such interactions in bacterial processes such as development and pathogenicity. Real-time measurement of protein-protein interactions in prokaryotes is highly desirable for determining the roles of protein complex in the development or virulence of bacteria, but methods that allow such measurement are not available. Here we describe the development of a bioluminescence resonance energy transfer (BRET) technology that meets this need. The use of endogenous excitation light in this strategy circumvents the requirement for the sophisticated instrument demanded by standard fluorescence resonance energy transfer (FRET). Furthermore, because the LuxAB substrate decanal is membrane permeable, the assay can be performed without lysing the bacterial cells

  6. Strongly interacting Higgs bosons

    International Nuclear Information System (INIS)

    Appelquist, T.; Bernard, C.

    1980-01-01

    The sensitivity of present-energy weak interactions to a strongly interacting heavy-Higgs-boson sector is discussed. The gauged nonlinear sigma model, which is the limit of the linear model as the Higgs-boson mass goes to infinity, is used to organize and catalogue all possible heavy-Higgs-boson effects. As long as the SU(2)/sub L/ x SU(2)/sub R/ symmetry of the Higgs sector is preserved, these effects are found to be small, of the order of the square of the gauge coupling times logarithms (but not powers) of the Higgs-boson mass divided by the W mass. We work in the context of a simplified model with gauge group SU(2)/sub L/; the extension to SU(2)/sub L/ x U(1) is briefly discussed

  7. A selection that reports on protein-protein interactions within a thermophilic bacterium.

    Science.gov (United States)

    Nguyen, Peter Q; Silberg, Jonathan J

    2010-07-01

    Many proteins can be split into fragments that exhibit enhanced function upon fusion to interacting proteins. While this strategy has been widely used to create protein-fragment complementation assays (PCAs) for discovering protein-protein interactions within mesophilic organisms, similar assays have not yet been developed for studying natural and engineered protein complexes at the temperatures where thermophilic microbes grow. We describe the development of a selection for protein-protein interactions within Thermus thermophilus that is based upon growth complementation by fragments of Thermotoga neapolitana adenylate kinase (AK(Tn)). Complementation studies with an engineered thermophile (PQN1) that is not viable above 75 degrees C because its adk gene has been replaced by a Geobacillus stearothermophilus ortholog revealed that growth could be restored at 78 degrees C by a vector that coexpresses polypeptides corresponding to residues 1-79 and 80-220 of AK(Tn). In contrast, PQN1 growth was not complemented by AK(Tn) fragments harboring a C156A mutation within the zinc-binding tetracysteine motif unless these fragments were fused to Thermotoga maritima chemotaxis proteins that heterodimerize (CheA and CheY) or homodimerize (CheX). This enhanced complementation is interpreted as arising from chemotaxis protein-protein interactions, since AK(Tn)-C156A fragments having only one polypeptide fused to a chemotaxis protein did not complement PQN1 to the same extent. This selection increases the maximum temperature where a PCA can be used to engineer thermostable protein complexes and to map protein-protein interactions.

  8. A new protein-protein interaction sensor based on tripartite split-GFP association.

    Science.gov (United States)

    Cabantous, Stéphanie; Nguyen, Hau B; Pedelacq, Jean-Denis; Koraïchi, Faten; Chaudhary, Anu; Ganguly, Kumkum; Lockard, Meghan A; Favre, Gilles; Terwilliger, Thomas C; Waldo, Geoffrey S

    2013-10-04

    Monitoring protein-protein interactions in living cells is key to unraveling their roles in numerous cellular processes and various diseases. Previously described split-GFP based sensors suffer from poor folding and/or self-assembly background fluorescence. Here, we have engineered a micro-tagging system to monitor protein-protein interactions in vivo and in vitro. The assay is based on tripartite association between two twenty amino-acids long GFP tags, GFP10 and GFP11, fused to interacting protein partners, and the complementary GFP1-9 detector. When proteins interact, GFP10 and GFP11 self-associate with GFP1-9 to reconstitute a functional GFP. Using coiled-coils and FRB/FKBP12 model systems we characterize the sensor in vitro and in Escherichia coli. We extend the studies to mammalian cells and examine the FK-506 inhibition of the rapamycin-induced association of FRB/FKBP12. The small size of these tags and their minimal effect on fusion protein behavior and solubility should enable new experiments for monitoring protein-protein association by fluorescence.

  9. Protein-Protein Interactions of Viroporins in Coronaviruses and Paramyxoviruses: New Targets for Antivirals?

    Directory of Open Access Journals (Sweden)

    Jaume Torres

    2015-06-01

    Full Text Available Viroporins are members of a rapidly growing family of channel-forming small polypeptides found in viruses. The present review will be focused on recent structural and protein-protein interaction information involving two viroporins found in enveloped viruses that target the respiratory tract; (i the envelope protein in coronaviruses and (ii the small hydrophobic protein in paramyxoviruses. Deletion of these two viroporins leads to viral attenuation in vivo, whereas data from cell culture shows involvement in the regulation of stress and inflammation. The channel activity and structure of some representative members of these viroporins have been recently characterized in some detail. In addition, searches for protein-protein interactions using yeast-two hybrid techniques have shed light on possible functional roles for their exposed cytoplasmic domains. A deeper analysis of these interactions should not only provide a more complete overview of the multiple functions of these viroporins, but also suggest novel strategies that target protein-protein interactions as much needed antivirals. These should complement current efforts to block viroporin channel activity.

  10. A conserved NAD+ binding pocket that regulates protein-protein interactions during aging.

    Science.gov (United States)

    Li, Jun; Bonkowski, Michael S; Moniot, Sébastien; Zhang, Dapeng; Hubbard, Basil P; Ling, Alvin J Y; Rajman, Luis A; Qin, Bo; Lou, Zhenkun; Gorbunova, Vera; Aravind, L; Steegborn, Clemens; Sinclair, David A

    2017-03-24

    DNA repair is essential for life, yet its efficiency declines with age for reasons that are unclear. Numerous proteins possess Nudix homology domains (NHDs) that have no known function. We show that NHDs are NAD + (oxidized form of nicotinamide adenine dinucleotide) binding domains that regulate protein-protein interactions. The binding of NAD + to the NHD domain of DBC1 (deleted in breast cancer 1) prevents it from inhibiting PARP1 [poly(adenosine diphosphate-ribose) polymerase], a critical DNA repair protein. As mice age and NAD + concentrations decline, DBC1 is increasingly bound to PARP1, causing DNA damage to accumulate, a process rapidly reversed by restoring the abundance of NAD + Thus, NAD + directly regulates protein-protein interactions, the modulation of which may protect against cancer, radiation, and aging. Copyright © 2017, American Association for the Advancement of Science.

  11. Identification of Protein-Protein Interactions with Glutathione-S-Transferase (GST) Fusion Proteins.

    Science.gov (United States)

    Einarson, Margret B; Pugacheva, Elena N; Orlinick, Jason R

    2007-08-01

    INTRODUCTIONGlutathione-S-transferase (GST) fusion proteins have had a wide range of applications since their introduction as tools for synthesis of recombinant proteins in bacteria. GST was originally selected as a fusion moiety because of several desirable properties. First and foremost, when expressed in bacteria alone, or as a fusion, GST is not sequestered in inclusion bodies (in contrast to previous fusion protein systems). Second, GST can be affinity-purified without denaturation because it binds to immobilized glutathione, which provides the basis for simple purification. Consequently, GST fusion proteins are routinely used for antibody generation and purification, protein-protein interaction studies, and biochemical analysis. This article describes the use of GST fusion proteins as probes for the identification of protein-protein interactions.

  12. Structural interface parameters are discriminatory in recognising near-native poses of protein-protein interactions.

    Directory of Open Access Journals (Sweden)

    Sony Malhotra

    Full Text Available Interactions at the molecular level in the cellular environment play a very crucial role in maintaining the physiological functioning of the cell. These molecular interactions exist at varied levels viz. protein-protein interactions, protein-nucleic acid interactions or protein-small molecules interactions. Presently in the field, these interactions and their mechanisms mark intensively studied areas. Molecular interactions can also be studied computationally using the approach named as Molecular Docking. Molecular docking employs search algorithms to predict the possible conformations for interacting partners and then calculates interaction energies. However, docking proposes number of solutions as different docked poses and hence offers a serious challenge to identify the native (or near native structures from the pool of these docked poses. Here, we propose a rigorous scoring scheme called DockScore which can be used to rank the docked poses and identify the best docked pose out of many as proposed by docking algorithm employed. The scoring identifies the optimal interactions between the two protein partners utilising various features of the putative interface like area, short contacts, conservation, spatial clustering and the presence of positively charged and hydrophobic residues. DockScore was first trained on a set of 30 protein-protein complexes to determine the weights for different parameters. Subsequently, we tested the scoring scheme on 30 different protein-protein complexes and native or near-native structure were assigned the top rank from a pool of docked poses in 26 of the tested cases. We tested the ability of DockScore to discriminate likely dimer interactions that differ substantially within a homologous family and also demonstrate that DOCKSCORE can distinguish correct pose for all 10 recent CAPRI targets.

  13. A coevolution analysis for identifying protein-protein interactions by Fourier transform

    Science.gov (United States)

    Yin, Changchuan; Yau, Stephen S. -T.

    2017-01-01

    Protein-protein interactions (PPIs) play key roles in life processes, such as signal transduction, transcription regulations, and immune response, etc. Identification of PPIs enables better understanding of the functional networks within a cell. Common experimental methods for identifying PPIs are time consuming and expensive. However, recent developments in computational approaches for inferring PPIs from protein sequences based on coevolution theory avoid these problems. In the coevolution theory model, interacted proteins may show coevolutionary mutations and have similar phylogenetic trees. The existing coevolution methods depend on multiple sequence alignments (MSA); however, the MSA-based coevolution methods often produce high false positive interactions. In this paper, we present a computational method using an alignment-free approach to accurately detect PPIs and reduce false positives. In the method, protein sequences are numerically represented by biochemical properties of amino acids, which reflect the structural and functional differences of proteins. Fourier transform is applied to the numerical representation of protein sequences to capture the dissimilarities of protein sequences in biophysical context. The method is assessed for predicting PPIs in Ebola virus. The results indicate strong coevolution between the protein pairs (NP-VP24, NP-VP30, NP-VP40, VP24-VP30, VP24-VP40, and VP30-VP40). The method is also validated for PPIs in influenza and E.coli genomes. Since our method can reduce false positive and increase the specificity of PPI prediction, it offers an effective tool to understand mechanisms of disease pathogens and find potential targets for drug design. The Python programs in this study are available to public at URL (https://github.com/cyinbox/PPI). PMID:28430779

  14. A coevolution analysis for identifying protein-protein interactions by Fourier transform.

    Directory of Open Access Journals (Sweden)

    Changchuan Yin

    Full Text Available Protein-protein interactions (PPIs play key roles in life processes, such as signal transduction, transcription regulations, and immune response, etc. Identification of PPIs enables better understanding of the functional networks within a cell. Common experimental methods for identifying PPIs are time consuming and expensive. However, recent developments in computational approaches for inferring PPIs from protein sequences based on coevolution theory avoid these problems. In the coevolution theory model, interacted proteins may show coevolutionary mutations and have similar phylogenetic trees. The existing coevolution methods depend on multiple sequence alignments (MSA; however, the MSA-based coevolution methods often produce high false positive interactions. In this paper, we present a computational method using an alignment-free approach to accurately detect PPIs and reduce false positives. In the method, protein sequences are numerically represented by biochemical properties of amino acids, which reflect the structural and functional differences of proteins. Fourier transform is applied to the numerical representation of protein sequences to capture the dissimilarities of protein sequences in biophysical context. The method is assessed for predicting PPIs in Ebola virus. The results indicate strong coevolution between the protein pairs (NP-VP24, NP-VP30, NP-VP40, VP24-VP30, VP24-VP40, and VP30-VP40. The method is also validated for PPIs in influenza and E.coli genomes. Since our method can reduce false positive and increase the specificity of PPI prediction, it offers an effective tool to understand mechanisms of disease pathogens and find potential targets for drug design. The Python programs in this study are available to public at URL (https://github.com/cyinbox/PPI.

  15. Predicting protein-protein interactions in Arabidopsis thaliana through integration of orthology, gene ontology and co-expression

    Directory of Open Access Journals (Sweden)

    Vandepoele Klaas

    2009-06-01

    Full Text Available Abstract Background Large-scale identification of the interrelationships between different components of the cell, such as the interactions between proteins, has recently gained great interest. However, unraveling large-scale protein-protein interaction maps is laborious and expensive. Moreover, assessing the reliability of the interactions can be cumbersome. Results In this study, we have developed a computational method that exploits the existing knowledge on protein-protein interactions in diverse species through orthologous relations on the one hand, and functional association data on the other hand to predict and filter protein-protein interactions in Arabidopsis thaliana. A highly reliable set of protein-protein interactions is predicted through this integrative approach making use of existing protein-protein interaction data from yeast, human, C. elegans and D. melanogaster. Localization, biological process, and co-expression data are used as powerful indicators for protein-protein interactions. The functional repertoire of the identified interactome reveals interactions between proteins functioning in well-conserved as well as plant-specific biological processes. We observe that although common mechanisms (e.g. actin polymerization and components (e.g. ARPs, actin-related proteins exist between different lineages, they are active in specific processes such as growth, cancer metastasis and trichome development in yeast, human and Arabidopsis, respectively. Conclusion We conclude that the integration of orthology with functional association data is adequate to predict protein-protein interactions. Through this approach, a high number of novel protein-protein interactions with diverse biological roles is discovered. Overall, we have predicted a reliable set of protein-protein interactions suitable for further computational as well as experimental analyses.

  16. Improving accuracy of protein-protein interaction prediction by considering the converse problem for sequence representation

    Directory of Open Access Journals (Sweden)

    Wang Yong

    2011-10-01

    Full Text Available Abstract Background With the development of genome-sequencing technologies, protein sequences are readily obtained by translating the measured mRNAs. Therefore predicting protein-protein interactions from the sequences is of great demand. The reason lies in the fact that identifying protein-protein interactions is becoming a bottleneck for eventually understanding the functions of proteins, especially for those organisms barely characterized. Although a few methods have been proposed, the converse problem, if the features used extract sufficient and unbiased information from protein sequences, is almost untouched. Results In this study, we interrogate this problem theoretically by an optimization scheme. Motivated by the theoretical investigation, we find novel encoding methods for both protein sequences and protein pairs. Our new methods exploit sufficiently the information of protein sequences and reduce artificial bias and computational cost. Thus, it significantly outperforms the available methods regarding sensitivity, specificity, precision, and recall with cross-validation evaluation and reaches ~80% and ~90% accuracy in Escherichia coli and Saccharomyces cerevisiae respectively. Our findings here hold important implication for other sequence-based prediction tasks because representation of biological sequence is always the first step in computational biology. Conclusions By considering the converse problem, we propose new representation methods for both protein sequences and protein pairs. The results show that our method significantly improves the accuracy of protein-protein interaction predictions.

  17. Distinct Mechanism Evolved for Mycobacterial RNA Polymerase and Topoisomerase I Protein-Protein Interaction.

    Science.gov (United States)

    Banda, Srikanth; Cao, Nan; Tse-Dinh, Yuk-Ching

    2017-09-15

    We report here a distinct mechanism of interaction between topoisomerase I and RNA polymerase in Mycobacterium tuberculosis and Mycobacterium smegmatis that has evolved independently from the previously characterized interaction between bacterial topoisomerase I and RNA polymerase. Bacterial DNA topoisomerase I is responsible for preventing the hyper-negative supercoiling of genomic DNA. The association of topoisomerase I with RNA polymerase during transcription elongation could efficiently relieve transcription-driven negative supercoiling. Our results demonstrate a direct physical interaction between the C-terminal domains of topoisomerase I (TopoI-CTDs) and the β' subunit of RNA polymerase of M. smegmatis in the absence of DNA. The TopoI-CTDs in mycobacteria are evolutionarily unrelated in amino acid sequence and three-dimensional structure to the TopoI-CTD found in the majority of bacterial species outside Actinobacteria, including Escherichia coli. The functional interaction between topoisomerase I and RNA polymerase has evolved independently in mycobacteria and E. coli, with distinctively different structural elements of TopoI-CTD utilized for this protein-protein interaction. Zinc ribbon motifs in E. coli TopoI-CTD are involved in the interaction with RNA polymerase. For M. smegmatis TopoI-CTD, a 27-amino-acid tail that is rich in basic residues at the C-terminal end is responsible for the interaction with RNA polymerase. Overexpression of recombinant TopoI-CTD in M. smegmatis competed with the endogenous topoisomerase I for protein-protein interactions with RNA polymerase. The TopoI-CTD overexpression resulted in decreased survival following treatment with antibiotics and hydrogen peroxide, supporting the importance of the protein-protein interaction between topoisomerase I and RNA polymerase during stress response of mycobacteria. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. False positive reduction in protein-protein interaction predictions using gene ontology annotations

    Directory of Open Access Journals (Sweden)

    Lin Yen-Han

    2007-07-01

    Full Text Available Abstract Background Many crucial cellular operations such as metabolism, signalling, and regulations are based on protein-protein interactions. However, the lack of robust protein-protein interaction information is a challenge. One reason for the lack of solid protein-protein interaction information is poor agreement between experimental findings and computational sets that, in turn, comes from huge false positive predictions in computational approaches. Reduction of false positive predictions and enhancing true positive fraction of computationally predicted protein-protein interaction datasets based on highly confident experimental results has not been adequately investigated. Results Gene Ontology (GO annotations were used to reduce false positive protein-protein interactions (PPI pairs resulting from computational predictions. Using experimentally obtained PPI pairs as a training dataset, eight top-ranking keywords were extracted from GO molecular function annotations. The sensitivity of these keywords is 64.21% in the yeast experimental dataset and 80.83% in the worm experimental dataset. The specificities, a measure of recovery power, of these keywords applied to four predicted PPI datasets for each studied organisms, are 48.32% and 46.49% (by average of four datasets in yeast and worm, respectively. Based on eight top-ranking keywords and co-localization of interacting proteins a set of two knowledge rules were deduced and applied to remove false positive protein pairs. The 'strength', a measure of improvement provided by the rules was defined based on the signal-to-noise ratio and implemented to measure the applicability of knowledge rules applying to the predicted PPI datasets. Depending on the employed PPI-predicting methods, the strength varies between two and ten-fold of randomly removing protein pairs from the datasets. Conclusion Gene Ontology annotations along with the deduced knowledge rules could be implemented to partially

  19. HitPredict version 4: comprehensive reliability scoring of physical protein?protein interactions from more than 100 species

    OpenAIRE

    L?pez, Yosvany; Nakai, Kenta; Patil, Ashwini

    2015-01-01

    HitPredict is a consolidated resource of experimentally identified, physical protein?protein interactions with confidence scores to indicate their reliability. The study of genes and their inter-relationships using methods such as network and pathway analysis requires high quality protein?protein interaction information. Extracting reliable interactions from most of the existing databases is challenging because they either contain only a subset of the available interactions, or a mixture of p...

  20. Towards a better understanding of the specificity of protein-protein interaction

    Czech Academy of Sciences Publication Activity Database

    Kysilka, Jiří; Vondrášek, Jiří

    2012-01-01

    Roč. 25, č. 11 (2012), s. 604-615 ISSN 0952-3499 R&D Projects: GA ČR GAP208/10/0725; GA ČR GAP302/10/0427; GA MŠk(CZ) LH11020 Institutional research plan: CEZ:AV0Z40550506; CEZ:AV0Z50520701 Keywords : protein-protein interaction * molecular recognition * x-ray structure analysis * empirical potentials * side chain-side chain interaction * interaction energy * bioinformatics Subject RIV: CE - Biochemistry Impact factor: 3.006, year: 2012

  1. Strong-interaction nonuniversality

    International Nuclear Information System (INIS)

    Volkas, R.R.; Foot, R.; He, X.; Joshi, G.C.

    1989-01-01

    The universal QCD color theory is extended to an SU(3) 1 direct product SU(3) 2 direct product SU(3) 3 gauge theory, where quarks of the ith generation transform as triplets under SU(3)/sub i/ and singlets under the other two factors. The usual color group is then identified with the diagonal subgroup, which remains exact after symmetry breaking. The gauge bosons associated with the 16 broken generators then form two massive octets under ordinary color. The interactions between quarks and these heavy gluonlike particles are explicitly nonuniversal and thus an exploration of their physical implications allows us to shed light on the fundamental issue of strong-interaction universality. Nonuniversality and weak flavor mixing are shown to generate heavy-gluon-induced flavor-changing neutral currents. The phenomenology of these processes is studied, as they provide the major experimental constraint on the extended theory. Three symmetry-breaking scenarios are presented. The first has color breaking occurring at the weak scale, while the second and third divorce the two scales. The third model has the interesting feature of radiatively induced off-diagonal Kobayashi-Maskawa matrix elements

  2. Towards a map of the Populus biomass protein-protein interaction network

    Energy Technology Data Exchange (ETDEWEB)

    Beers, Eric [Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States); Brunner, Amy [Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States); Helm, Richard [Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States); Dickerman, Allan [Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)

    2015-07-31

    Biofuels can be produced from a variety of plant feedstocks. The value of a particular feedstock for biofuels production depends in part on the degree of difficulty associated with the extraction of fermentable sugars from the plant biomass. The wood of trees is potentially a rich source fermentable sugars. However, the sugars in wood exist in a tightly cross-linked matrix of cellulose, hemicellulose, and lignin, making them largely recalcitrant to release and fermentation for biofuels production. Before breeders and genetic engineers can effectively develop plants with reduced recalcitrance to fermentation, it is necessary to gain a better understanding of the fundamental biology of the mechanisms responsible for wood formation. Regulatory, structural, and enzymatic proteins are required for the complicated process of wood formation. To function properly, proteins must interact with other proteins. Yet, very few of the protein-protein interactions necessary for wood formation are known. The main objectives of this project were to 1) identify new protein-protein interactions relevant to wood formation, and 2) perform in-depth characterizations of selected protein-protein interactions. To identify relevant protein-protein interactions, we cloned a set of approximately 400 genes that were highly expressed in the wood-forming tissue (known as secondary xylem) of poplar (Populus trichocarpa). We tested whether the proteins encoded by these biomass genes interacted with each other in a binary matrix design using the yeast two-hybrid (Y2H) method for protein-protein interaction discovery. We also tested a subset of the 400 biomass proteins for interactions with all proteins present in wood-forming tissue of poplar in a biomass library screen design using Y2H. Together, these two Y2H screens yielded over 270 interactions involving over 75 biomass proteins. For the second main objective we selected several interacting pairs or groups of interacting proteins for in

  3. Exploiting conformational ensembles in modeling protein-protein interactions on the proteome scale

    Science.gov (United States)

    Kuzu, Guray; Gursoy, Attila; Nussinov, Ruth; Keskin, Ozlem

    2013-01-01

    Cellular functions are performed through protein-protein interactions; therefore, identification of these interactions is crucial for understanding biological processes. Recent studies suggest that knowledge-based approaches are more useful than ‘blind’ docking for modeling at large scales. However, a caveat of knowledge-based approaches is that they treat molecules as rigid structures. The Protein Data Bank (PDB) offers a wealth of conformations. Here, we exploited ensemble of the conformations in predictions by a knowledge-based method, PRISM. We tested ‘difficult’ cases in a docking-benchmark dataset, where the unbound and bound protein forms are structurally different. Considering alternative conformations for each protein, the percentage of successfully predicted interactions increased from ~26% to 66%, and 57% of the interactions were successfully predicted in an ‘unbiased’ scenario, in which data related to the bound forms were not utilized. If the appropriate conformation, or relevant template interface, is unavailable in the PDB, PRISM could not predict the interaction successfully. The pace of the growth of the PDB promises a rapid increase of ensemble conformations emphasizing the merit of such knowledge-based ensemble strategies for higher success rates in protein-protein interaction predictions on an interactome-scale. We constructed the structural network of ERK interacting proteins as a case study. PMID:23590674

  4. Novel Technology for Protein-Protein Interaction-based Targeted Drug Discovery

    Directory of Open Access Journals (Sweden)

    Jung Me Hwang

    2011-12-01

    Full Text Available We have developed a simple but highly efficient in-cell protein-protein interaction (PPI discovery system based on the translocation properties of protein kinase C- and its C1a domain in live cells. This system allows the visual detection of trimeric and dimeric protein interactions including cytosolic, nuclear, and/or membrane proteins with their cognate ligands. In addition, this system can be used to identify pharmacological small compounds that inhibit specific PPIs. These properties make this PPI system an attractive tool for screening drug candidates and mapping the protein interactome.

  5. Molecular imaging of drug-modulated protein-protein interactions in living subjects.

    Science.gov (United States)

    Paulmurugan, Ramasamy; Massoud, Tarik F; Huang, Jing; Gambhir, Sanjiv S

    2004-03-15

    Networks of protein interactions mediate cellular responses to environmental stimuli and direct the execution of many different cellular functional pathways. Small molecules synthesized within cells or recruited from the external environment mediate many protein interactions. The study of small molecule-mediated interactions of proteins is important to understand abnormal signal transduction pathways in cancer and in drug development and validation. In this study, we used split synthetic renilla luciferase (hRLUC) protein fragment-assisted complementation to evaluate heterodimerization of the human proteins FRB and FKBP12 mediated by the small molecule rapamycin. The concentration of rapamycin required for efficient dimerization and that of its competitive binder ascomycin required for dimerization inhibition were studied in cell lines. The system was dually modulated in cell culture at the transcription level, by controlling nuclear factor kappaB promoter/enhancer elements using tumor necrosis factor alpha, and at the interaction level, by controlling the concentration of the dimerizer rapamycin. The rapamycin-mediated dimerization of FRB and FKBP12 also was studied in living mice by locating, quantifying, and timing the hRLUC complementation-based bioluminescence imaging signal using a cooled charged coupled device camera. This split reporter system can be used to efficiently screen small molecule drugs that modulate protein-protein interactions and also to assess drugs in living animals. Both are essential steps in the preclinical evaluation of candidate pharmaceutical agents targeting protein-protein interactions, including signaling pathways in cancer cells.

  6. Protein-protein interactions in the regulation of WRKY transcription factors.

    Science.gov (United States)

    Chi, Yingjun; Yang, Yan; Zhou, Yuan; Zhou, Jie; Fan, Baofang; Yu, Jing-Quan; Chen, Zhixiang

    2013-03-01

    It has been almost 20 years since the first report of a WRKY transcription factor, SPF1, from sweet potato. Great progress has been made since then in establishing the diverse biological roles of WRKY transcription factors in plant growth, development, and responses to biotic and abiotic stress. Despite the functional diversity, almost all analyzed WRKY proteins recognize the TTGACC/T W-box sequences and, therefore, mechanisms other than mere recognition of the core W-box promoter elements are necessary to achieve the regulatory specificity of WRKY transcription factors. Research over the past several years has revealed that WRKY transcription factors physically interact with a wide range of proteins with roles in signaling, transcription, and chromatin remodeling. Studies of WRKY-interacting proteins have provided important insights into the regulation and mode of action of members of the important family of transcription factors. It has also emerged that the slightly varied WRKY domains and other protein motifs conserved within each of the seven WRKY subfamilies participate in protein-protein interactions and mediate complex functional interactions between WRKY proteins and between WRKY and other regulatory proteins in the modulation of important biological processes. In this review, we summarize studies of protein-protein interactions for WRKY transcription factors and discuss how the interacting partners contribute, at different levels, to the establishment of the complex regulatory and functional network of WRKY transcription factors.

  7. Quality control methodology for high-throughput protein-protein interaction screening.

    Science.gov (United States)

    Vazquez, Alexei; Rual, Jean-François; Venkatesan, Kavitha

    2011-01-01

    Protein-protein interactions are key to many aspects of the cell, including its cytoskeletal structure, the signaling processes in which it is involved, or its metabolism. Failure to form protein complexes or signaling cascades may sometimes translate into pathologic conditions such as cancer or neurodegenerative diseases. The set of all protein interactions between the proteins encoded by an organism constitutes its protein interaction network, representing a scaffold for biological function. Knowing the protein interaction network of an organism, combined with other sources of biological information, can unravel fundamental biological circuits and may help better understand the molecular basics of human diseases. The protein interaction network of an organism can be mapped by combining data obtained from both low-throughput screens, i.e., "one gene at a time" experiments and high-throughput screens, i.e., screens designed to interrogate large sets of proteins at once. In either case, quality controls are required to deal with the inherent imperfect nature of experimental assays. In this chapter, we discuss experimental and statistical methodologies to quantify error rates in high-throughput protein-protein interactions screens.

  8. Energy landscape of all-atom protein-protein interactions revealed by multiscale enhanced sampling.

    Directory of Open Access Journals (Sweden)

    Kei Moritsugu

    2014-10-01

    Full Text Available Protein-protein interactions are regulated by a subtle balance of complicated atomic interactions and solvation at the interface. To understand such an elusive phenomenon, it is necessary to thoroughly survey the large configurational space from the stable complex structure to the dissociated states using the all-atom model in explicit solvent and to delineate the energy landscape of protein-protein interactions. In this study, we carried out a multiscale enhanced sampling (MSES simulation of the formation of a barnase-barstar complex, which is a protein complex characterized by an extraordinary tight and fast binding, to determine the energy landscape of atomistic protein-protein interactions. The MSES adopts a multicopy and multiscale scheme to enable for the enhanced sampling of the all-atom model of large proteins including explicit solvent. During the 100-ns MSES simulation of the barnase-barstar system, we observed the association-dissociation processes of the atomistic protein complex in solution several times, which contained not only the native complex structure but also fully non-native configurations. The sampled distributions suggest that a large variety of non-native states went downhill to the stable complex structure, like a fast folding on a funnel-like potential. This funnel landscape is attributed to dominant configurations in the early stage of the association process characterized by near-native orientations, which will accelerate the native inter-molecular interactions. These configurations are guided mostly by the shape complementarity between barnase and barstar, and lead to the fast formation of the final complex structure along the downhill energy landscape.

  9. Recovering protein-protein and domain-domain interactions from aggregation of IP-MS proteomics of coregulator complexes.

    Directory of Open Access Journals (Sweden)

    Amin R Mazloom

    2011-12-01

    Full Text Available Coregulator proteins (CoRegs are part of multi-protein complexes that transiently assemble with transcription factors and chromatin modifiers to regulate gene expression. In this study we analyzed data from 3,290 immuno-precipitations (IP followed by mass spectrometry (MS applied to human cell lines aimed at identifying CoRegs complexes. Using the semi-quantitative spectral counts, we scored binary protein-protein and domain-domain associations with several equations. Unlike previous applications, our methods scored prey-prey protein-protein interactions regardless of the baits used. We also predicted domain-domain interactions underlying predicted protein-protein interactions. The quality of predicted protein-protein and domain-domain interactions was evaluated using known binary interactions from the literature, whereas one protein-protein interaction, between STRN and CTTNBP2NL, was validated experimentally; and one domain-domain interaction, between the HEAT domain of PPP2R1A and the Pkinase domain of STK25, was validated using molecular docking simulations. The scoring schemes presented here recovered known, and predicted many new, complexes, protein-protein, and domain-domain interactions. The networks that resulted from the predictions are provided as a web-based interactive application at http://maayanlab.net/HT-IP-MS-2-PPI-DDI/.

  10. An assessment of machine and statistical learning approaches to inferring networks of protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Browne Fiona

    2006-12-01

    Full Text Available Protein-protein interactions (PPI play a key role in many biological systems. Over the past few years, an explosion in availability of functional biological data obtained from high-throughput technologies to infer PPI has been observed. However, results obtained from such experiments show high rates of false positives and false negatives predictions as well as systematic predictive bias. Recent research has revealed that several machine and statistical learning methods applied to integrate relatively weak, diverse sources of large-scale functional data may provide improved predictive accuracy and coverage of PPI. In this paper we describe the effects of applying different computational, integrative methods to predict PPI in Saccharomyces cerevisiae. We investigated the predictive ability of combining different sets of relatively strong and weak predictive datasets. We analysed several genomic datasets ranging from mRNA co-expression to marginal essentiality. Moreover, we expanded an existing multi-source dataset from S. cerevisiae by constructing a new set of putative interactions extracted from Gene Ontology (GO- driven annotations in the Saccharomyces Genome Database. Different classification techniques: Simple Naive Bayesian (SNB, Multilayer Perceptron (MLP and K-Nearest Neighbors (KNN were evaluated. Relatively simple classification methods (i.e. less computing intensive and mathematically complex, such as SNB, have been proven to be proficient at predicting PPI. SNB produced the “highest” predictive quality obtaining an area under Receiver Operating Characteristic (ROC curve (AUC value of 0.99. The lowest AUC value of 0.90 was obtained by the KNN classifier. This assessment also demonstrates the strong predictive power of GO-driven models, which offered predictive performance above 0.90 using the different machine learning and statistical techniques. As the predictive power of single-source datasets became weaker MLP and SNB performed

  11. SynechoNET: integrated protein-protein interaction database of a model cyanobacterium Synechocystis sp. PCC 6803

    OpenAIRE

    Kim, Woo-Yeon; Kang, Sungsoo; Kim, Byoung-Chul; Oh, Jeehyun; Cho, Seongwoong; Bhak, Jong; Choi, Jong-Soon

    2008-01-01

    Background Cyanobacteria are model organisms for studying photosynthesis, carbon and nitrogen assimilation, evolution of plant plastids, and adaptability to environmental stresses. Despite many studies on cyanobacteria, there is no web-based database of their regulatory and signaling protein-protein interaction networks to date. Description We report a database and website SynechoNET that provides predicted protein-protein interactions. SynechoNET shows cyanobacterial domain-domain interactio...

  12. Phthalic Acid Chemical Probes Synthesized for Protein-Protein Interaction Analysis

    Directory of Open Access Journals (Sweden)

    Chin-Jen Wu

    2013-06-01

    Full Text Available Plasticizers are additives that are used to increase the flexibility of plastic during manufacturing. However, in injection molding processes, plasticizers cannot be generated with monomers because they can peel off from the plastics into the surrounding environment, water, or food, or become attached to skin. Among the various plasticizers that are used, 1,2-benzenedicarboxylic acid (phthalic acid is a typical precursor to generate phthalates. In addition, phthalic acid is a metabolite of diethylhexyl phthalate (DEHP. According to Gene_Ontology gene/protein database, phthalates can cause genital diseases, cardiotoxicity, hepatotoxicity, nephrotoxicity, etc. In this study, a silanized linker (3-aminopropyl triethoxyslane, APTES was deposited on silicon dioxides (SiO2 particles and phthalate chemical probes were manufactured from phthalic acid and APTES–SiO2. These probes could be used for detecting proteins that targeted phthalic acid and for protein-protein interactions. The phthalic acid chemical probes we produced were incubated with epithelioid cell lysates of normal rat kidney (NRK-52E cells to detect the interactions between phthalic acid and NRK-52E extracted proteins. These chemical probes interacted with a number of chaperones such as protein disulfide-isomerase A6, heat shock proteins, and Serpin H1. Ingenuity Pathways Analysis (IPA software showed that these chemical probes were a practical technique for protein-protein interaction analysis.

  13. Uncovering Viral Protein-Protein Interactions and their Role in Arenavirus Life Cycle

    Directory of Open Access Journals (Sweden)

    Nora López

    2012-09-01

    Full Text Available The Arenaviridae family includes widely distributed pathogens that cause severe hemorrhagic fever in humans. Replication and packaging of their single-stranded RNA genome involve RNA recognition by viral proteins and a number of key protein-protein interactions. Viral RNA synthesis is directed by the virus-encoded RNA dependent-RNA polymerase (L protein and requires viral RNA encapsidation by the Nucleoprotein. In addition to the role that the interaction between L and the Nucleoprotein may have in the replication process, polymerase activity appears to be modulated by the association between L and the small multifunctional Z protein. Z is also a structural component of the virions that plays an essential role in viral morphogenesis. Indeed, interaction of the Z protein with the Nucleoprotein is critical for genome packaging. Furthermore, current evidence suggests that binding between Z and the viral envelope glycoprotein complex is required for virion infectivity, and that Z homo-oligomerization is an essential step for particle assembly and budding. Efforts to understand the molecular basis of arenavirus life cycle have revealed important details on these viral protein-protein interactions that will be reviewed in this article.

  14. A method for investigating protein-protein interactions related to Salmonella typhimurium pathogenesis

    Energy Technology Data Exchange (ETDEWEB)

    Chowdhury, Saiful M. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Shi, Liang [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Yoon, Hyunjin [Dartmouth College, Hanover, NH (United States); Ansong, Charles [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Rommereim, Leah M. [Dartmouth College, Hanover, NH (United States); Norbeck, Angela D. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Auberry, Kenneth J. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Moore, R. J. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Adkins, Joshua N. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Heffron, Fred [Oregon Health and Science Univ., Portland, OR (United States); Smith, Richard D. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

    2009-02-10

    We successfully modified an existing method to investigate protein-protein interactions in the pathogenic bacterium Salmonella typhimurium (STM). This method includes i) addition of a histidine-biotin-histidine tag to the bait proteins via recombinant DNA techniques; ii) in vivo cross-linking with formaldehyde; iii) tandem affinity purification of bait proteins under fully denaturing conditions; and iv) identification of the proteins cross-linked to the bait proteins by liquid-chromatography in conjunction with tandem mass-spectrometry. In vivo cross-linking stabilized protein interactions permitted the subsequent two-step purification step conducted under denaturing conditions. The two-step purification greatly reduced nonspecific binding of non-cross-linked proteins to bait proteins. Two different negative controls were employed to reduce false-positive identification. In an initial demonstration of this approach, we tagged three selected STM proteins- HimD, PduB and PhoP- with known binding partners that ranged from stable (e.g., HimD) to transient (i.e., PhoP). Distinct sets of interacting proteins were identified with each bait protein, including the known binding partners such as HimA for HimD, as well as anticipated and unexpected binding partners. Our results suggest that novel protein-protein interactions may be critical to pathogenesis by Salmonella typhimurium. .

  15. Protein-protein interaction site predictions with minimum covariance determinant and Mahalanobis distance.

    Science.gov (United States)

    Qiu, Zhijun; Zhou, Bo; Yuan, Jiangfeng

    2017-11-21

    Protein-protein interaction site (PPIS) prediction must deal with the diversity of interaction sites that limits their prediction accuracy. Use of proteins with unknown or unidentified interactions can also lead to missing interfaces. Such data errors are often brought into the training dataset. In response to these two problems, we used the minimum covariance determinant (MCD) method to refine the training data to build a predictor with better performance, utilizing its ability of removing outliers. In order to predict test data in practice, a method based on Mahalanobis distance was devised to select proper test data as input for the predictor. With leave-one-validation and independent test, after the Mahalanobis distance screening, our method achieved higher performance according to Matthews correlation coefficient (MCC), although only a part of test data could be predicted. These results indicate that data refinement is an efficient approach to improve protein-protein interaction site prediction. By further optimizing our method, it is hopeful to develop predictors of better performance and wide range of application. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. Detection of protein complex from protein-protein interaction network using Markov clustering

    International Nuclear Information System (INIS)

    Ochieng, P J; Kusuma, W A; Haryanto, T

    2017-01-01

    Detection of complexes, or groups of functionally related proteins, is an important challenge while analysing biological networks. However, existing algorithms to identify protein complexes are insufficient when applied to dense networks of experimentally derived interaction data. Therefore, we introduced a graph clustering method based on Markov clustering algorithm to identify protein complex within highly interconnected protein-protein interaction networks. Protein-protein interaction network was first constructed to develop geometrical network, the network was then partitioned using Markov clustering to detect protein complexes. The interest of the proposed method was illustrated by its application to Human Proteins associated to type II diabetes mellitus. Flow simulation of MCL algorithm was initially performed and topological properties of the resultant network were analysed for detection of the protein complex. The results indicated the proposed method successfully detect an overall of 34 complexes with 11 complexes consisting of overlapping modules and 20 non-overlapping modules. The major complex consisted of 102 proteins and 521 interactions with cluster modularity and density of 0.745 and 0.101 respectively. The comparison analysis revealed MCL out perform AP, MCODE and SCPS algorithms with high clustering coefficient (0.751) network density and modularity index (0.630). This demonstrated MCL was the most reliable and efficient graph clustering algorithm for detection of protein complexes from PPI networks. (paper)

  17. Efficient prediction of human protein-protein interactions at a global scale.

    Science.gov (United States)

    Schoenrock, Andrew; Samanfar, Bahram; Pitre, Sylvain; Hooshyar, Mohsen; Jin, Ke; Phillips, Charles A; Wang, Hui; Phanse, Sadhna; Omidi, Katayoun; Gui, Yuan; Alamgir, Md; Wong, Alex; Barrenäs, Fredrik; Babu, Mohan; Benson, Mikael; Langston, Michael A; Green, James R; Dehne, Frank; Golshani, Ashkan

    2014-12-10

    Our knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods. On the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3 months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments. The speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks (from genomic sequences alone) for personalized medicine.

  18. Detection of protein-protein interactions by ribosome display and protein in situ immobilisation.

    Science.gov (United States)

    He, Mingyue; Liu, Hong; Turner, Martin; Taussig, Michael J

    2009-12-31

    We describe a method for identification of protein-protein interactions by combining two cell-free protein technologies, namely ribosome display and protein in situ immobilisation. The method requires only PCR fragments as the starting material, the target proteins being made through cell-free protein synthesis, either associated with their encoding mRNA as ribosome complexes or immobilised on a solid surface. The use of ribosome complexes allows identification of interacting protein partners from their attached coding mRNA. To demonstrate the procedures, we have employed the lymphocyte signalling proteins Vav1 and Grb2 and confirmed the interaction between Grb2 and the N-terminal SH3 domain of Vav1. The method has promise for library screening of pairwise protein interactions, down to the analytical level of individual domain or motif mapping.

  19. 3DProIN: Protein-Protein Interaction Networks and Structure Visualization.

    Science.gov (United States)

    Li, Hui; Liu, Chunmei

    2014-06-14

    3DProIN is a computational tool to visualize protein-protein interaction networks in both two dimensional (2D) and three dimensional (3D) view. It models protein-protein interactions in a graph and explores the biologically relevant features of the tertiary structures of each protein in the network. Properties such as color, shape and name of each node (protein) of the network can be edited in either 2D or 3D views. 3DProIN is implemented using 3D Java and C programming languages. The internet crawl technique is also used to parse dynamically grasped protein interactions from protein data bank (PDB). It is a java applet component that is embedded in the web page and it can be used on different platforms including Linux, Mac and Window using web browsers such as Firefox, Internet Explorer, Chrome and Safari. It also was converted into a mac app and submitted to the App store as a free app. Mac users can also download the app from our website. 3DProIN is available for academic research at http://bicompute.appspot.com.

  20. Design, synthesis, and evaluation of an alpha-helix mimetic library targeting protein-protein interactions.

    Science.gov (United States)

    Shaginian, Alex; Whitby, Landon R; Hong, Sukwon; Hwang, Inkyu; Farooqi, Bilal; Searcey, Mark; Chen, Jiandong; Vogt, Peter K; Boger, Dale L

    2009-04-22

    The design and solution-phase synthesis of an alpha-helix mimetic library as an integral component of a small-molecule library targeting protein-protein interactions are described. The iterative design, synthesis, and evaluation of the candidate alpha-helix mimetic was initiated from a precedented triaryl template and refined by screening the designs for inhibition of MDM2/p53 binding. Upon identifying a chemically and biologically satisfactory design and consistent with the screening capabilities of academic collaborators, the corresponding complete library was assembled as 400 mixtures of 20 compounds (20 x 20 x 20-mix), where the added subunits are designed to mimic all possible permutations of the naturally occurring i, i + 4, i + 7 amino acid side chains of an alpha-helix. The library (8000 compounds) was prepared using a solution-phase synthetic protocol enlisting acid/base liquid-liquid extractions for purification on a scale that insures its long-term availability for screening campaigns. Screening of the library for inhibition of MDM2/p53 binding not only identified the lead alpha-helix mimetic upon which the library was based, but also suggests that a digestion of the initial screening results that accompany the use of such a comprehensive library can provide insights into the nature of the interaction (e.g., an alpha-helix mediated protein-protein interaction) and define the key residues and their characteristics responsible for recognition.

  1. Unravelling Protein-Protein Interaction Networks Linked to Aliphatic and Indole Glucosinolate Biosynthetic Pathways in Arabidopsis

    Directory of Open Access Journals (Sweden)

    Sebastian J. Nintemann

    2017-11-01

    Full Text Available Within the cell, biosynthetic pathways are embedded in protein-protein interaction networks. In Arabidopsis, the biosynthetic pathways of aliphatic and indole glucosinolate defense compounds are well-characterized. However, little is known about the spatial orchestration of these enzymes and their interplay with the cellular environment. To address these aspects, we applied two complementary, untargeted approaches—split-ubiquitin yeast 2-hybrid and co-immunoprecipitation screens—to identify proteins interacting with CYP83A1 and CYP83B1, two homologous enzymes specific for aliphatic and indole glucosinolate biosynthesis, respectively. Our analyses reveal distinct functional networks with substantial interconnection among the identified interactors for both pathway-specific markers, and add to our knowledge about how biochemical pathways are connected to cellular processes. Specifically, a group of protein interactors involved in cell death and the hypersensitive response provides a potential link between the glucosinolate defense compounds and defense against biotrophic pathogens, mediated by protein-protein interactions.

  2. Prediction of protein-protein interactions between viruses and human by an SVM model

    Directory of Open Access Journals (Sweden)

    Cui Guangyu

    2012-05-01

    Full Text Available Abstract Background Several computational methods have been developed to predict protein-protein interactions from amino acid sequences, but most of those methods are intended for the interactions within a species rather than for interactions across different species. Methods for predicting interactions between homogeneous proteins are not appropriate for finding those between heterogeneous proteins since they do not distinguish the interactions between proteins of the same species from those of different species. Results We developed a new method for representing a protein sequence of variable length in a frequency vector of fixed length, which encodes the relative frequency of three consecutive amino acids of a sequence. We built a support vector machine (SVM model to predict human proteins that interact with virus proteins. In two types of viruses, human papillomaviruses (HPV and hepatitis C virus (HCV, our SVM model achieved an average accuracy above 80%, which is higher than that of another SVM model with a different representation scheme. Using the SVM model and Gene Ontology (GO annotations of proteins, we predicted new interactions between virus proteins and human proteins. Conclusions Encoding the relative frequency of amino acid triplets of a protein sequence is a simple yet powerful representation method for predicting protein-protein interactions across different species. The representation method has several advantages: (1 it enables a prediction model to achieve a better performance than other representations, (2 it generates feature vectors of fixed length regardless of the sequence length, and (3 the same representation is applicable to different types of proteins.

  3. Designing Focused Chemical Libraries Enriched in Protein-Protein Interaction Inhibitors using Machine-Learning Methods

    Science.gov (United States)

    Reynès, Christelle; Host, Hélène; Camproux, Anne-Claude; Laconde, Guillaume; Leroux, Florence; Mazars, Anne; Deprez, Benoit; Fahraeus, Robin; Villoutreix, Bruno O.; Sperandio, Olivier

    2010-01-01

    Protein-protein interactions (PPIs) may represent one of the next major classes of therapeutic targets. So far, only a minute fraction of the estimated 650,000 PPIs that comprise the human interactome are known with a tiny number of complexes being drugged. Such intricate biological systems cannot be cost-efficiently tackled using conventional high-throughput screening methods. Rather, time has come for designing new strategies that will maximize the chance for hit identification through a rationalization of the PPI inhibitor chemical space and the design of PPI-focused compound libraries (global or target-specific). Here, we train machine-learning-based models, mainly decision trees, using a dataset of known PPI inhibitors and of regular drugs in order to determine a global physico-chemical profile for putative PPI inhibitors. This statistical analysis unravels two important molecular descriptors for PPI inhibitors characterizing specific molecular shapes and the presence of a privileged number of aromatic bonds. The best model has been transposed into a computer program, PPI-HitProfiler, that can output from any drug-like compound collection a focused chemical library enriched in putative PPI inhibitors. Our PPI inhibitor profiler is challenged on the experimental screening results of 11 different PPIs among which the p53/MDM2 interaction screened within our own CDithem platform, that in addition to the validation of our concept led to the identification of 4 novel p53/MDM2 inhibitors. Collectively, our tool shows a robust behavior on the 11 experimental datasets by correctly profiling 70% of the experimentally identified hits while removing 52% of the inactive compounds from the initial compound collections. We strongly believe that this new tool can be used as a global PPI inhibitor profiler prior to screening assays to reduce the size of the compound collections to be experimentally screened while keeping most of the true PPI inhibitors. PPI-HitProfiler is

  4. Designing focused chemical libraries enriched in protein-protein interaction inhibitors using machine-learning methods.

    Directory of Open Access Journals (Sweden)

    Christelle Reynès

    2010-03-01

    Full Text Available Protein-protein interactions (PPIs may represent one of the next major classes of therapeutic targets. So far, only a minute fraction of the estimated 650,000 PPIs that comprise the human interactome are known with a tiny number of complexes being drugged. Such intricate biological systems cannot be cost-efficiently tackled using conventional high-throughput screening methods. Rather, time has come for designing new strategies that will maximize the chance for hit identification through a rationalization of the PPI inhibitor chemical space and the design of PPI-focused compound libraries (global or target-specific. Here, we train machine-learning-based models, mainly decision trees, using a dataset of known PPI inhibitors and of regular drugs in order to determine a global physico-chemical profile for putative PPI inhibitors. This statistical analysis unravels two important molecular descriptors for PPI inhibitors characterizing specific molecular shapes and the presence of a privileged number of aromatic bonds. The best model has been transposed into a computer program, PPI-HitProfiler, that can output from any drug-like compound collection a focused chemical library enriched in putative PPI inhibitors. Our PPI inhibitor profiler is challenged on the experimental screening results of 11 different PPIs among which the p53/MDM2 interaction screened within our own CDithem platform, that in addition to the validation of our concept led to the identification of 4 novel p53/MDM2 inhibitors. Collectively, our tool shows a robust behavior on the 11 experimental datasets by correctly profiling 70% of the experimentally identified hits while removing 52% of the inactive compounds from the initial compound collections. We strongly believe that this new tool can be used as a global PPI inhibitor profiler prior to screening assays to reduce the size of the compound collections to be experimentally screened while keeping most of the true PPI inhibitors. PPI

  5. Designing focused chemical libraries enriched in protein-protein interaction inhibitors using machine-learning methods.

    Science.gov (United States)

    Reynès, Christelle; Host, Hélène; Camproux, Anne-Claude; Laconde, Guillaume; Leroux, Florence; Mazars, Anne; Deprez, Benoit; Fahraeus, Robin; Villoutreix, Bruno O; Sperandio, Olivier

    2010-03-05

    Protein-protein interactions (PPIs) may represent one of the next major classes of therapeutic targets. So far, only a minute fraction of the estimated 650,000 PPIs that comprise the human interactome are known with a tiny number of complexes being drugged. Such intricate biological systems cannot be cost-efficiently tackled using conventional high-throughput screening methods. Rather, time has come for designing new strategies that will maximize the chance for hit identification through a rationalization of the PPI inhibitor chemical space and the design of PPI-focused compound libraries (global or target-specific). Here, we train machine-learning-based models, mainly decision trees, using a dataset of known PPI inhibitors and of regular drugs in order to determine a global physico-chemical profile for putative PPI inhibitors. This statistical analysis unravels two important molecular descriptors for PPI inhibitors characterizing specific molecular shapes and the presence of a privileged number of aromatic bonds. The best model has been transposed into a computer program, PPI-HitProfiler, that can output from any drug-like compound collection a focused chemical library enriched in putative PPI inhibitors. Our PPI inhibitor profiler is challenged on the experimental screening results of 11 different PPIs among which the p53/MDM2 interaction screened within our own CDithem platform, that in addition to the validation of our concept led to the identification of 4 novel p53/MDM2 inhibitors. Collectively, our tool shows a robust behavior on the 11 experimental datasets by correctly profiling 70% of the experimentally identified hits while removing 52% of the inactive compounds from the initial compound collections. We strongly believe that this new tool can be used as a global PPI inhibitor profiler prior to screening assays to reduce the size of the compound collections to be experimentally screened while keeping most of the true PPI inhibitors. PPI-HitProfiler is

  6. Protein-protein interaction network-based detection of functionally similar proteins within species.

    Science.gov (United States)

    Song, Baoxing; Wang, Fen; Guo, Yang; Sang, Qing; Liu, Min; Li, Dengyun; Fang, Wei; Zhang, Deli

    2012-07-01

    Although functionally similar proteins across species have been widely studied, functionally similar proteins within species showing low sequence similarity have not been examined in detail. Identification of these proteins is of significant importance for understanding biological functions, evolution of protein families, progression of co-evolution, and convergent evolution and others which cannot be obtained by detection of functionally similar proteins across species. Here, we explored a method of detecting functionally similar proteins within species based on graph theory. After denoting protein-protein interaction networks using graphs, we split the graphs into subgraphs using the 1-hop method. Proteins with functional similarities in a species were detected using a method of modified shortest path to compare these subgraphs and to find the eligible optimal results. Using seven protein-protein interaction networks and this method, some functionally similar proteins with low sequence similarity that cannot detected by sequence alignment were identified. By analyzing the results, we found that, sometimes, it is difficult to separate homologous from convergent evolution. Evaluation of the performance of our method by gene ontology term overlap showed that the precision of our method was excellent. Copyright © 2012 Wiley Periodicals, Inc.

  7. A scalable double-barcode sequencing platform for characterization of dynamic protein-protein interactions.

    Science.gov (United States)

    Schlecht, Ulrich; Liu, Zhimin; Blundell, Jamie R; St Onge, Robert P; Levy, Sasha F

    2017-05-25

    Several large-scale efforts have systematically catalogued protein-protein interactions (PPIs) of a cell in a single environment. However, little is known about how the protein interactome changes across environmental perturbations. Current technologies, which assay one PPI at a time, are too low throughput to make it practical to study protein interactome dynamics. Here, we develop a highly parallel protein-protein interaction sequencing (PPiSeq) platform that uses a novel double barcoding system in conjunction with the dihydrofolate reductase protein-fragment complementation assay in Saccharomyces cerevisiae. PPiSeq detects PPIs at a rate that is on par with current assays and, in contrast with current methods, quantitatively scores PPIs with enough accuracy and sensitivity to detect changes across environments. Both PPI scoring and the bulk of strain construction can be performed with cell pools, making the assay scalable and easily reproduced across environments. PPiSeq is therefore a powerful new tool for large-scale investigations of dynamic PPIs.

  8. DUF581 is plant specific FCS-like zinc finger involved in protein-protein interaction.

    Directory of Open Access Journals (Sweden)

    Muhammed Jamsheer K

    Full Text Available Zinc fingers are a ubiquitous class of protein domain with considerable variation in structure and function. Zf-FCS is a highly diverged group of C2-C2 zinc finger which is present in animals, prokaryotes and viruses, but not in plants. In this study we identified that a plant specific domain of unknown function, DUF581 is a zf-FCS type zinc finger. Based on HMM-HMM comparison and signature motif similarity we named this domain as FCS-Like Zinc finger (FLZ domain. A genome wide survey identified that FLZ domain containing genes are bryophytic in origin and this gene family is expanded in spermatophytes. Expression analysis of selected FLZ gene family members of A. thaliana identified an overlapping expression pattern suggesting a possible redundancy in their function. Unlike the zf-FCS domain, the FLZ domain found to be highly conserved in sequence and structure. Using a combination of bioinformatic and protein-protein interaction tools, we identified that FLZ domain is involved in protein-protein interaction.

  9. Positive Selection and Centrality in the Yeast and Fly Protein-Protein Interaction Networks

    Directory of Open Access Journals (Sweden)

    Sandip Chakraborty

    2016-01-01

    Full Text Available Proteins within a molecular network are expected to be subject to different selective pressures depending on their relative hierarchical positions. However, it is not obvious what genes within a network should be more likely to evolve under positive selection. On one hand, only mutations at genes with a relatively high degree of control over adaptive phenotypes (such as those encoding highly connected proteins are expected to be “seen” by natural selection. On the other hand, a high degree of pleiotropy at these genes is expected to hinder adaptation. Previous analyses of the human protein-protein interaction network have shown that genes under long-term, recurrent positive selection (as inferred from interspecific comparisons tend to act at the periphery of the network. It is unknown, however, whether these trends apply to other organisms. Here, we show that long-term positive selection has preferentially targeted the periphery of the yeast interactome. Conversely, in flies, genes under positive selection encode significantly more connected and central proteins. These observations are not due to covariation of genes’ adaptability and centrality with confounding factors. Therefore, the distribution of proteins encoded by genes under recurrent positive selection across protein-protein interaction networks varies from one species to another.

  10. Dr. PIAS: an integrative system for assessing the druggability of protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Furuya Toshio

    2011-02-01

    Full Text Available Abstract Background The amount of data on protein-protein interactions (PPIs available in public databases and in the literature has rapidly expanded in recent years. PPI data can provide useful information for researchers in pharmacology and medicine as well as those in interactome studies. There is urgent need for a novel methodology or software allowing the efficient utilization of PPI data in pharmacology and medicine. Results To address this need, we have developed the 'Druggable Protein-protein Interaction Assessment System' (Dr. PIAS. Dr. PIAS has a meta-database that stores various types of information (tertiary structures, drugs/chemicals, and biological functions associated with PPIs retrieved from public sources. By integrating this information, Dr. PIAS assesses whether a PPI is druggable as a target for small chemical ligands by using a supervised machine-learning method, support vector machine (SVM. Dr. PIAS holds not only known druggable PPIs but also all PPIs of human, mouse, rat, and human immunodeficiency virus (HIV proteins identified to date. Conclusions The design concept of Dr. PIAS is distinct from other published PPI databases in that it focuses on selecting the PPIs most likely to make good drug targets, rather than merely collecting PPI data.

  11. Single-well monitoring of protein-protein interaction and phosphorylation-dephosphorylation events.

    Science.gov (United States)

    Arcand, Mathieu; Roby, Philippe; Bossé, Roger; Lipari, Francesco; Padrós, Jaime; Beaudet, Lucille; Marcil, Alexandre; Dahan, Sophie

    2010-04-20

    We combined oxygen channeling assays with two distinct chemiluminescent beads to detect simultaneously protein phosphorylation and interaction events that are usually monitored separately. This novel method was tested in the ERK1/2 MAP kinase pathway. It was first used to directly monitor dissociation of MAP kinase ERK2 from MEK1 upon phosphorylation and to evaluate MAP kinase phosphatase (MKP) selectivity and mechanism of action. In addition, MEK1 and ERK2 were probed with an ATP competitor and an allosteric MEK1 inhibitor, which generated distinct phosphorylation-interaction patterns. Simultaneous monitoring of protein-protein interactions and substrate phosphorylation can provide significant mechanistic insight into enzyme activity and small molecule action.

  12. Targeted in vivo inhibition of specific protein-protein interactions using recombinant antibodies.

    Directory of Open Access Journals (Sweden)

    Matej Zábrady

    Full Text Available With the growing availability of genomic sequence information, there is an increasing need for gene function analysis. Antibody-mediated "silencing" represents an intriguing alternative for the precise inhibition of a particular function of biomolecules. Here, we describe a method for selecting recombinant antibodies with a specific purpose in mind, which is to inhibit intrinsic protein-protein interactions in the cytosol of plant cells. Experimental procedures were designed for conveniently evaluating desired properties of recombinant antibodies in consecutive steps. Our selection method was successfully used to develop a recombinant antibody inhibiting the interaction of ARABIDOPSIS HISTIDINE PHOSPHOTRANSFER PROTEIN 3 with such of its upstream interaction partners as the receiver domain of CYTOKININ INDEPENDENT HISTIDINE KINASE 1. The specific down-regulation of the cytokinin signaling pathway in vivo demonstrates the validity of our approach. This selection method can serve as a prototype for developing unique recombinant antibodies able to interfere with virtually any biomolecule in the living cell.

  13. Reconstruction of the yeast protein-protein interaction network involved in nutrient sensing and global metabolic regulation

    DEFF Research Database (Denmark)

    Nandy, Subir Kumar; Jouhten, Paula; Nielsen, Jens

    2010-01-01

    proteins. Despite the value of BioGRID for studying protein-protein interactions, there is a need for manual curation of these interactions in order to remove false positives. RESULTS: Here we describe an annotated reconstruction of the protein-protein interactions around four key nutrient......) and for all the interactions between them (edges). The annotated information is readily available utilizing the functionalities of network modelling tools such as Cytoscape and CellDesigner. CONCLUSIONS: The reported fully annotated interaction model serves as a platform for integrated systems biology studies...

  14. Strong interaction phenomenology

    International Nuclear Information System (INIS)

    Giffon, M.

    1989-01-01

    A brief review of high energy hadronic data (Part I)is followed by an introduction to the standard (Weinberg Salam Glashow) model of electroweak interactions and its extension to the hadrons (Part II). Rudiments of QCD and of the parton model area given in Part III together with a quick review of the spectroscopy of heavy flavours whereas Part IV is devoted to the introduction to deep inelastic scattering and to the so-called EMC effects. (author)

  15. Prediction of the Ebola Virus Infection Related Human Genes Using Protein-Protein Interaction Network.

    Science.gov (United States)

    Cao, HuanHuan; Zhang, YuHang; Zhao, Jia; Zhu, Liucun; Wang, Yi; Li, JiaRui; Feng, Yuan-Ming; Zhang, Ning

    2017-01-01

    Ebola hemorrhagic fever (EHF) is caused by Ebola virus (EBOV). It is reported that human could be infected by EBOV with a high fatality rate. However, association factors between EBOV and host still tend to be ambiguous. According to the "guilt by association" (GBA) principle, proteins interacting with each other are very likely to function similarly or the same. Based on this assumption, we tried to obtain EBOV infection-related human genes in a protein-protein interaction network using Dijkstra algorithm. We hope it could contribute to the discovery of novel effective treatments. Finally, 15 genes were selected as potential EBOV infection-related human genes. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  16. Application of Machine Learning Approaches for Protein-protein Interactions Prediction.

    Science.gov (United States)

    Zhang, Mengying; Su, Qiang; Lu, Yi; Zhao, Manman; Niu, Bing

    2017-01-01

    Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics. In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed. SVM and RF are two commonly used methods. Nowadays, more researchers predict PPIs by combining more than two methods. This review presents the application of machine learning approaches in predicting PPI. Many examples of success in identification and prediction in the area of PPI prediction have been discussed, and the PPIs research is still in progress. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  17. Intragenic suppressor of Osiaa23 revealed a conserved tryptophan residue crucial for protein-protein interactions.

    Directory of Open Access Journals (Sweden)

    Jun Ni

    Full Text Available The Auxin/Indole-3-Acetic Acid (Aux/IAA and Auxin Response Factor (ARF are two important families that play key roles in auxin signal transduction. Both of the families contain a similar carboxyl-terminal domain (Domain III/IV that facilitates interactions between these two families. In spite of the importance of protein-protein interactions among these transcription factors, the mechanisms involved in these interactions are largely unknown. In this study, we isolated six intragenic suppressors of an auxin insensitive mutant, Osiaa23. Among these suppressors, Osiaa23-R5 successfully rescued all the defects of the mutant. Sequence analysis revealed that an amino acid substitution occurred in the Tryptophan (W residue in Domain IV of Osiaa23. Yeast two-hybrid experiments showed that the mutation in Domain IV prevents the protein-protein interactions between Osiaa23 and OsARFs. Phylogenetic analysis revealed that the W residue is conserved in both OsIAAs and OsARFs. Next, we performed site-specific amino acid substitutions within Domain IV of OsARFs, and the conserved W in Domain IV was exchanged by Serine (S. The mutated OsARF(WSs can be released from the inhibition of Osiaa23 and maintain the transcriptional activities. Expression of OsARF(WSs in Osiaa23 mutant rescued different defects of the mutant. Our results suggest a previously unknown importance of Domain IV in both families and provide an indirect way to investigate functions of OsARFs.

  18. Identification of structural protein-protein interactions of herpes simplex virus type 1.

    Science.gov (United States)

    Lee, Jin H; Vittone, Valerio; Diefenbach, Eve; Cunningham, Anthony L; Diefenbach, Russell J

    2008-09-01

    In this study we have defined protein-protein interactions between the structural proteins of herpes simplex virus type 1 (HSV-1) using a LexA yeast two-hybrid system. The majority of the capsid, tegument and envelope proteins of HSV-1 were screened in a matrix approach. A total of 40 binary interactions were detected including 9 out of 10 previously identified tegument-tegument interactions (Vittone, V., Diefenbach, E., Triffett, D., Douglas, M.W., Cunningham, A.L., and Diefenbach, R.J., 2005. Determination of interactions between tegument proteins of herpes simplex virus type 1. J. Virol. 79, 9566-9571). A total of 12 interactions involving the capsid protein pUL35 (VP26) and 11 interactions involving the tegument protein pUL46 (VP11/12) were identified. The most significant novel interactions detected in this study, which are likely to play a role in viral assembly, include pUL35-pUL37 (capsid-tegument), pUL46-pUL37 (tegument-tegument) and pUL49 (VP22)-pUS9 (tegument-envelope). This information will provide further insights into the pathways of HSV-1 assembly and the identified interactions are potential targets for new antiviral drugs.

  19. Design principles for cancer therapy guided by changes in complexity of protein-protein interaction networks.

    Science.gov (United States)

    Benzekry, Sebastian; Tuszynski, Jack A; Rietman, Edward A; Lakka Klement, Giannoula

    2015-05-28

    The ever-increasing expanse of online bioinformatics data is enabling new ways to, not only explore the visualization of these data, but also to apply novel mathematical methods to extract meaningful information for clinically relevant analysis of pathways and treatment decisions. One of the methods used for computing topological characteristics of a space at different spatial resolutions is persistent homology. This concept can also be applied to network theory, and more specifically to protein-protein interaction networks, where the number of rings in an individual cancer network represents a measure of complexity. We observed a linear correlation of R = -0.55 between persistent homology and 5-year survival of patients with a variety of cancers. This relationship was used to predict the proteins within a protein-protein interaction network with the most impact on cancer progression. By re-computing the persistent homology after computationally removing an individual node (protein) from the protein-protein interaction network, we were able to evaluate whether such an inhibition would lead to improvement in patient survival. The power of this approach lied in its ability to identify the effects of inhibition of multiple proteins and in the ability to expose whether the effect of a single inhibition may be amplified by inhibition of other proteins. More importantly, we illustrate specific examples of persistent homology calculations, which correctly predict the survival benefit observed effects in clinical trials using inhibitors of the identified molecular target. We propose that computational approaches such as persistent homology may be used in the future for selection of molecular therapies in clinic. The technique uses a mathematical algorithm to evaluate the node (protein) whose inhibition has the highest potential to reduce network complexity. The greater the drop in persistent homology, the greater reduction in network complexity, and thus a larger

  20. The role of exon shuffling in shaping protein-protein interaction networks

    Directory of Open Access Journals (Sweden)

    França Gustavo S

    2010-12-01

    Full Text Available Abstract Background Physical protein-protein interaction (PPI is a critical phenomenon for the function of most proteins in living organisms and a significant fraction of PPIs are the result of domain-domain interactions. Exon shuffling, intron-mediated recombination of exons from existing genes, is known to have been a major mechanism of domain shuffling in metazoans. Thus, we hypothesized that exon shuffling could have a significant influence in shaping the topology of PPI networks. Results We tested our hypothesis by compiling exon shuffling and PPI data from six eukaryotic species: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Cryptococcus neoformans and Arabidopsis thaliana. For all four metazoan species, genes enriched in exon shuffling events presented on average higher vertex degree (number of interacting partners in PPI networks. Furthermore, we verified that a set of protein domains that are simultaneously promiscuous (known to interact to multiple types of other domains, self-interacting (able to interact with another copy of themselves and abundant in the genomes presents a stronger signal for exon shuffling. Conclusions Exon shuffling appears to have been a recurrent mechanism for the emergence of new PPIs along metazoan evolution. In metazoan genomes, exon shuffling also promoted the expansion of some protein domains. We speculate that their promiscuous and self-interacting properties may have been decisive for that expansion.

  1. Protein-Protein Interactions Prediction Based on Iterative Clique Extension with Gene Ontology Filtering

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    Lei Yang

    2014-01-01

    Full Text Available Cliques (maximal complete subnets in protein-protein interaction (PPI network are an important resource used to analyze protein complexes and functional modules. Clique-based methods of predicting PPI complement the data defection from biological experiments. However, clique-based predicting methods only depend on the topology of network. The false-positive and false-negative interactions in a network usually interfere with prediction. Therefore, we propose a method combining clique-based method of prediction and gene ontology (GO annotations to overcome the shortcoming and improve the accuracy of predictions. According to different GO correcting rules, we generate two predicted interaction sets which guarantee the quality and quantity of predicted protein interactions. The proposed method is applied to the PPI network from the Database of Interacting Proteins (DIP and most of the predicted interactions are verified by another biological database, BioGRID. The predicted protein interactions are appended to the original protein network, which leads to clique extension and shows the significance of biological meaning.

  2. Utilizing knowledge base of amino acids structural neighborhoods to predict protein-protein interaction sites.

    Science.gov (United States)

    Jelínek, Jan; Škoda, Petr; Hoksza, David

    2017-12-06

    Protein-protein interactions (PPI) play a key role in an investigation of various biochemical processes, and their identification is thus of great importance. Although computational prediction of which amino acids take part in a PPI has been an active field of research for some time, the quality of in-silico methods is still far from perfect. We have developed a novel prediction method called INSPiRE which benefits from a knowledge base built from data available in Protein Data Bank. All proteins involved in PPIs were converted into labeled graphs with nodes corresponding to amino acids and edges to pairs of neighboring amino acids. A structural neighborhood of each node was then encoded into a bit string and stored in the knowledge base. When predicting PPIs, INSPiRE labels amino acids of unknown proteins as interface or non-interface based on how often their structural neighborhood appears as interface or non-interface in the knowledge base. We evaluated INSPiRE's behavior with respect to different types and sizes of the structural neighborhood. Furthermore, we examined the suitability of several different features for labeling the nodes. Our evaluations showed that INSPiRE clearly outperforms existing methods with respect to Matthews correlation coefficient. In this paper we introduce a new knowledge-based method for identification of protein-protein interaction sites called INSPiRE. Its knowledge base utilizes structural patterns of known interaction sites in the Protein Data Bank which are then used for PPI prediction. Extensive experiments on several well-established datasets show that INSPiRE significantly surpasses existing PPI approaches.

  3. Protein-protein interaction site predictions with three-dimensional probability distributions of interacting atoms on protein surfaces.

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    Ching-Tai Chen

    Full Text Available Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins and were tested on an independent dataset (consisting of 142 proteins. The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted

  4. Protein-Protein Interaction Site Predictions with Three-Dimensional Probability Distributions of Interacting Atoms on Protein Surfaces

    Science.gov (United States)

    Chen, Ching-Tai; Peng, Hung-Pin; Jian, Jhih-Wei; Tsai, Keng-Chang; Chang, Jeng-Yih; Yang, Ei-Wen; Chen, Jun-Bo; Ho, Shinn-Ying; Hsu, Wen-Lian; Yang, An-Suei

    2012-01-01

    Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with

  5. Can understanding the packing of side chains improve the design of protein-protein interactions?

    Science.gov (United States)

    Zhou, Alice; O'Hern, Corey; Regan, Lynne

    2011-03-01

    With the long-term goal to improve the design of protein-protein interactions, we have begun extensive computational studies to understand how side-chains of key residues of binding partners geometrically fit together at protein-peptide interfaces, e.g. the tetratrico-peptide repeat protein and its cognate peptide). We describe simple atomic-scale models of hydrophobic dipeptides, which include hard-core repulsion, bond length and angle constraints, and Van der Waals attraction. By completely enumerating all minimal energy structures in these systems, we are able to reproduce important features of the probability distributions of side chain dihedral angles of hydrophic residues in the protein data bank. These results are the crucial first step in developing computational models that can predict the side chain conformations of residues at protein-peptide interfaces. CSO acknowledges support from NSF grant no. CMMT-1006527.

  6. Specific protein-protein interactions of calsequestrin with junctional sarcoplasmic reticulum of skeletal muscle

    International Nuclear Information System (INIS)

    Damiani, E.; Margreth, A.

    1990-01-01

    Minor protein components of triads and of sarcoplasmic reticulum (SR) terminal cisternae (TC), i.e. 47 and 37 kDa peptides and 31-30 kDa and 26-25 kDa peptide doublets, were identified from their ability to bind 125 I calsequestrin (CS) in the presence of EGTA. The CS-binding peptides are specifically associated with the junctional membrane of TC, since they could not be detected in junctional transverse tubules and in longitudinal SR fragments. The 31-30 kDa peptide doublet, exclusively, did not bind CS in the presence of Ca 2+ . Thus, different types of protein-protein interactions appear to be involved in selective binding of CS to junctional TC

  7. HippDB: a database of readily targeted helical protein-protein interactions.

    Science.gov (United States)

    Bergey, Christina M; Watkins, Andrew M; Arora, Paramjit S

    2013-11-01

    HippDB catalogs every protein-protein interaction whose structure is available in the Protein Data Bank and which exhibits one or more helices at the interface. The Web site accepts queries on variables such as helix length and sequence, and it provides computational alanine scanning and change in solvent-accessible surface area values for every interfacial residue. HippDB is intended to serve as a starting point for structure-based small molecule and peptidomimetic drug development. HippDB is freely available on the web at http://www.nyu.edu/projects/arora/hippdb. The Web site is implemented in PHP, MySQL and Apache. Source code freely available for download at http://code.google.com/p/helidb, implemented in Perl and supported on Linux. arora@nyu.edu.

  8. Discovering disease-associated genes in weighted protein-protein interaction networks

    Science.gov (United States)

    Cui, Ying; Cai, Meng; Stanley, H. Eugene

    2018-04-01

    Although there have been many network-based attempts to discover disease-associated genes, most of them have not taken edge weight - which quantifies their relative strength - into consideration. We use connection weights in a protein-protein interaction (PPI) network to locate disease-related genes. We analyze the topological properties of both weighted and unweighted PPI networks and design an improved random forest classifier to distinguish disease genes from non-disease genes. We use a cross-validation test to confirm that weighted networks are better able to discover disease-associated genes than unweighted networks, which indicates that including link weight in the analysis of network properties provides a better model of complex genotype-phenotype associations.

  9. CellMap visualizes protein-protein interactions and subcellular localization

    Science.gov (United States)

    Dallago, Christian; Goldberg, Tatyana; Andrade-Navarro, Miguel Angel; Alanis-Lobato, Gregorio; Rost, Burkhard

    2018-01-01

    Many tools visualize protein-protein interaction (PPI) networks. The tool introduced here, CellMap, adds one crucial novelty by visualizing PPI networks in the context of subcellular localization, i.e. the location in the cell or cellular component in which a PPI happens. Users can upload images of cells and define areas of interest against which PPIs for selected proteins are displayed (by default on a cartoon of a cell). Annotations of localization are provided by the user or through our in-house database. The visualizer and server are written in JavaScript, making CellMap easy to customize and to extend by researchers and developers. PMID:29497493

  10. CombiMotif: A new algorithm for network motifs discovery in protein-protein interaction networks

    Science.gov (United States)

    Luo, Jiawei; Li, Guanghui; Song, Dan; Liang, Cheng

    2014-12-01

    Discovering motifs in protein-protein interaction networks is becoming a current major challenge in computational biology, since the distribution of the number of network motifs can reveal significant systemic differences among species. However, this task can be computationally expensive because of the involvement of graph isomorphic detection. In this paper, we present a new algorithm (CombiMotif) that incorporates combinatorial techniques to count non-induced occurrences of subgraph topologies in the form of trees. The efficiency of our algorithm is demonstrated by comparing the obtained results with the current state-of-the art subgraph counting algorithms. We also show major differences between unicellular and multicellular organisms. The datasets and source code of CombiMotif are freely available upon request.

  11. Hot spot-based design of small-molecule inhibitors for protein-protein interactions.

    Science.gov (United States)

    Guo, Wenxing; Wisniewski, John A; Ji, Haitao

    2014-06-01

    Protein-protein interactions (PPIs) are important targets for the development of chemical probes and therapeutic agents. From the initial discovery of the existence of hot spots at PPI interfaces, it has been proposed that hot spots might provide the key for developing small-molecule PPI inhibitors. However, there has been no review on the ways in which the knowledge of hot spots can be used to achieve inhibitor design, nor critical examination of successful examples. This Digest discusses the characteristics of hot spots and the identification of druggable hot spot pockets. An analysis of four examples of hot spot-based design reveals the importance of this strategy in discovering potent and selective PPI inhibitors. A general procedure for hot spot-based design of PPI inhibitors is outlined. Copyright © 2014 Elsevier Ltd. All rights reserved.

  12. Identifying Novel Candidate Genes Related to Apoptosis from a Protein-Protein Interaction Network

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    Baoman Wang

    2015-01-01

    Full Text Available Apoptosis is the process of programmed cell death (PCD that occurs in multicellular organisms. This process of normal cell death is required to maintain the balance of homeostasis. In addition, some diseases, such as obesity, cancer, and neurodegenerative diseases, can be cured through apoptosis, which produces few side effects. An effective comprehension of the mechanisms underlying apoptosis will be helpful to prevent and treat some diseases. The identification of genes related to apoptosis is essential to uncover its underlying mechanisms. In this study, a computational method was proposed to identify novel candidate genes related to apoptosis. First, protein-protein interaction information was used to construct a weighted graph. Second, a shortest path algorithm was applied to the graph to search for new candidate genes. Finally, the obtained genes were filtered by a permutation test. As a result, 26 genes were obtained, and we discuss their likelihood of being novel apoptosis-related genes by collecting evidence from published literature.

  13. HKC: An Algorithm to Predict Protein Complexes in Protein-Protein Interaction Networks

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    Xiaomin Wang

    2011-01-01

    Full Text Available With the availability of more and more genome-scale protein-protein interaction (PPI networks, research interests gradually shift to Systematic Analysis on these large data sets. A key topic is to predict protein complexes in PPI networks by identifying clusters that are densely connected within themselves but sparsely connected with the rest of the network. In this paper, we present a new topology-based algorithm, HKC, to detect protein complexes in genome-scale PPI networks. HKC mainly uses the concepts of highest k-core and cohesion to predict protein complexes by identifying overlapping clusters. The experiments on two data sets and two benchmarks show that our algorithm has relatively high F-measure and exhibits better performance compared with some other methods.

  14. Protein complex prediction in large ontology attributed protein-protein interaction networks.

    Science.gov (United States)

    Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian; Li, Yanpeng; Xu, Bo

    2013-01-01

    Protein complexes are important for unraveling the secrets of cellular organization and function. Many computational approaches have been developed to predict protein complexes in protein-protein interaction (PPI) networks. However, most existing approaches focus mainly on the topological structure of PPI networks, and largely ignore the gene ontology (GO) annotation information. In this paper, we constructed ontology attributed PPI networks with PPI data and GO resource. After constructing ontology attributed networks, we proposed a novel approach called CSO (clustering based on network structure and ontology attribute similarity). Structural information and GO attribute information are complementary in ontology attributed networks. CSO can effectively take advantage of the correlation between frequent GO annotation sets and the dense subgraph for protein complex prediction. Our proposed CSO approach was applied to four different yeast PPI data sets and predicted many well-known protein complexes. The experimental results showed that CSO was valuable in predicting protein complexes and achieved state-of-the-art performance.

  15. Analysis of intraviral protein-protein interactions of the SARS coronavirus ORFeome.

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    Albrecht von Brunn

    2007-05-01

    Full Text Available The severe acute respiratory syndrome coronavirus (SARS-CoV genome is predicted to encode 14 functional open reading frames, leading to the expression of up to 30 structural and non-structural protein products. The functions of a large number of viral ORFs are poorly understood or unknown. In order to gain more insight into functions and modes of action and interaction of the different proteins, we cloned the viral ORFeome and performed a genome-wide analysis for intraviral protein interactions and for intracellular localization. 900 pairwise interactions were tested by yeast-two-hybrid matrix analysis, and more than 65 positive non-redundant interactions, including six self interactions, were identified. About 38% of interactions were subsequently confirmed by CoIP in mammalian cells. Nsp2, nsp8 and ORF9b showed a wide range of interactions with other viral proteins. Nsp8 interacts with replicase proteins nsp2, nsp5, nsp6, nsp7, nsp8, nsp9, nsp12, nsp13 and nsp14, indicating a crucial role as a major player within the replication complex machinery. It was shown by others that nsp8 is essential for viral replication in vitro, whereas nsp2 is not. We show that also accessory protein ORF9b does not play a pivotal role for viral replication, as it can be deleted from the virus displaying normal plaque sizes and growth characteristics in Vero cells. However, it can be expected to be important for the virus-host interplay and for pathogenicity, due to its large number of interactions, by enhancing the global stability of the SARS proteome network, or play some unrealized role in regulating protein-protein interactions. The interactions identified provide valuable material for future studies.

  16. C2 Domains as Protein-Protein Interaction Modules in the Ciliary Transition Zone

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    Kim Remans

    2014-07-01

    Full Text Available RPGR-interacting protein 1 (RPGRIP1 is mutated in the eye disease Leber congenital amaurosis (LCA and its structural homolog, RPGRIP1-like (RPGRIP1L, is mutated in many different ciliopathies. Both are multidomain proteins that are predicted to interact with retinitis pigmentosa G-protein regulator (RPGR. RPGR is mutated in X-linked retinitis pigmentosa and is located in photoreceptors and primary cilia. We solved the crystal structure of the complex between the RPGR-interacting domain (RID of RPGRIP1 and RPGR and demonstrate that RPGRIP1L binds to RPGR similarly. RPGRIP1 binding to RPGR affects the interaction with PDEδ, the cargo shuttling factor for prenylated ciliary proteins. RPGRIP1-RID is a C2 domain with a canonical β sandwich structure that does not bind Ca2+ and/or phospholipids and thus constitutes a unique type of protein-protein interaction module. Judging from the large number of C2 domains in most of the ciliary transition zone proteins identified thus far, the structure presented here seems to constitute a cilia-specific module that is present in multiprotein transition zone complexes.

  17. Noise reduction in protein-protein interaction graphs by the implementation of a novel weighting scheme

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    Moschopoulos Charalampos

    2011-06-01

    Full Text Available Abstract Background Recent technological advances applied to biology such as yeast-two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of protein interaction networks. These interaction networks represent a rich, yet noisy, source of data that could be used to extract meaningful information, such as protein complexes. Several interaction network weighting schemes have been proposed so far in the literature in order to eliminate the noise inherent in interactome data. In this paper, we propose a novel weighting scheme and apply it to the S. cerevisiae interactome. Complex prediction rates are improved by up to 39%, depending on the clustering algorithm applied. Results We adopt a two step procedure. During the first step, by applying both novel and well established protein-protein interaction (PPI weighting methods, weights are introduced to the original interactome graph based on the confidence level that a given interaction is a true-positive one. The second step applies clustering using established algorithms in the field of graph theory, as well as two variations of Spectral clustering. The clustered interactome networks are also cross-validated against the confirmed protein complexes present in the MIPS database. Conclusions The results of our experimental work demonstrate that interactome graph weighting methods clearly improve the clustering results of several clustering algorithms. Moreover, our proposed weighting scheme outperforms other approaches of PPI graph weighting.

  18. BIPS: BIANA Interolog Prediction Server. A tool for protein-protein interaction inference.

    Science.gov (United States)

    Garcia-Garcia, Javier; Schleker, Sylvia; Klein-Seetharaman, Judith; Oliva, Baldo

    2012-07-01

    Protein-protein interactions (PPIs) play a crucial role in biology, and high-throughput experiments have greatly increased the coverage of known interactions. Still, identification of complete inter- and intraspecies interactomes is far from being complete. Experimental data can be complemented by the prediction of PPIs within an organism or between two organisms based on the known interactions of the orthologous genes of other organisms (interologs). Here, we present the BIANA (Biologic Interactions and Network Analysis) Interolog Prediction Server (BIPS), which offers a web-based interface to facilitate PPI predictions based on interolog information. BIPS benefits from the capabilities of the framework BIANA to integrate the several PPI-related databases. Additional metadata can be used to improve the reliability of the predicted interactions. Sensitivity and specificity of the server have been calculated using known PPIs from different interactomes using a leave-one-out approach. The specificity is between 72 and 98%, whereas sensitivity varies between 1 and 59%, depending on the sequence identity cut-off used to calculate similarities between sequences. BIPS is freely accessible at http://sbi.imim.es/BIPS.php.

  19. Transcription Factor Functional Protein-Protein Interactions in Plant Defense Responses

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    Murilo S. Alves

    2014-03-01

    Full Text Available Responses to biotic stress in plants lead to dramatic reprogramming of gene expression, favoring stress responses at the expense of normal cellular functions. Transcription factors are master regulators of gene expression at the transcriptional level, and controlling the activity of these factors alters the transcriptome of the plant, leading to metabolic and phenotypic changes in response to stress. The functional analysis of interactions between transcription factors and other proteins is very important for elucidating the role of these transcriptional regulators in different signaling cascades. In this review, we present an overview of protein-protein interactions for the six major families of transcription factors involved in plant defense: basic leucine zipper containing domain proteins (bZIP, amino-acid sequence WRKYGQK (WRKY, myelocytomatosis related proteins (MYC, myeloblastosis related proteins (MYB, APETALA2/ ETHYLENE-RESPONSIVE ELEMENT BINDING FACTORS (AP2/EREBP and no apical meristem (NAM, Arabidopsis transcription activation factor (ATAF, and cup-shaped cotyledon (CUC (NAC. We describe the interaction partners of these transcription factors as molecular responses during pathogen attack and the key components of signal transduction pathways that take place during plant defense responses. These interactions determine the activation or repression of response pathways and are crucial to understanding the regulatory networks that modulate plant defense responses.

  20. Molecular tweezers modulate 14-3-3 protein-protein interactions

    Science.gov (United States)

    Bier, David; Rose, Rolf; Bravo-Rodriguez, Kenny; Bartel, Maria; Ramirez-Anguita, Juan Manuel; Dutt, Som; Wilch, Constanze; Klärner, Frank-Gerrit; Sanchez-Garcia, Elsa; Schrader, Thomas; Ottmann, Christian

    2013-03-01

    Supramolecular chemistry has recently emerged as a promising way to modulate protein functions, but devising molecules that will interact with a protein in the desired manner is difficult as many competing interactions exist in a biological environment (with solvents, salts or different sites for the target biomolecule). We now show that lysine-specific molecular tweezers bind to a 14-3-3 adapter protein and modulate its interaction with partner proteins. The tweezers inhibit binding between the 14-3-3 protein and two partner proteins—a phosphorylated (C-Raf) protein and an unphosphorylated one (ExoS)—in a concentration-dependent manner. Protein crystallography shows that this effect arises from the binding of the tweezers to a single surface-exposed lysine (Lys214) of the 14-3-3 protein in the proximity of its central channel, which normally binds the partner proteins. A combination of structural analysis and computer simulations provides rules for the tweezers' binding preferences, thus allowing us to predict their influence on this type of protein-protein interactions.

  1. Protein-protein interactions in paralogues: Electrostatics modulates specificity on a conserved steric scaffold.

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    Stefan M Ivanov

    Full Text Available An improved knowledge of protein-protein interactions is essential for better understanding of metabolic and signaling networks, and cellular function. Progress tends to be based on structure determination and predictions using known structures, along with computational methods based on evolutionary information or detailed atomistic descriptions. We hypothesized that for the case of interactions across a common interface, between proteins from a pair of paralogue families or within a family of paralogues, a relatively simple interface description could distinguish between binding and non-binding pairs. Using binding data for several systems, and large-scale comparative modeling based on known template complex structures, it is found that charge-charge interactions (for groups bearing net charge are generally a better discriminant than buried non-polar surface. This is particularly the case for paralogue families that are less divergent, with more reliable comparative modeling. We suggest that electrostatic interactions are major determinants of specificity in such systems, an observation that could be used to predict binding partners.

  2. Protein-protein interactions in paralogues: Electrostatics modulates specificity on a conserved steric scaffold.

    Science.gov (United States)

    Ivanov, Stefan M; Cawley, Andrew; Huber, Roland G; Bond, Peter J; Warwicker, Jim

    2017-01-01

    An improved knowledge of protein-protein interactions is essential for better understanding of metabolic and signaling networks, and cellular function. Progress tends to be based on structure determination and predictions using known structures, along with computational methods based on evolutionary information or detailed atomistic descriptions. We hypothesized that for the case of interactions across a common interface, between proteins from a pair of paralogue families or within a family of paralogues, a relatively simple interface description could distinguish between binding and non-binding pairs. Using binding data for several systems, and large-scale comparative modeling based on known template complex structures, it is found that charge-charge interactions (for groups bearing net charge) are generally a better discriminant than buried non-polar surface. This is particularly the case for paralogue families that are less divergent, with more reliable comparative modeling. We suggest that electrostatic interactions are major determinants of specificity in such systems, an observation that could be used to predict binding partners.

  3. Combined chemical shift changes and amino acid specific chemical shift mapping of protein-protein interactions

    Energy Technology Data Exchange (ETDEWEB)

    Schumann, Frank H.; Riepl, Hubert [University of Regensburg, Institute of Biophysics and Physical Biochemistry (Germany); Maurer, Till [Boehringer Ingelheim Pharma GmbH and Co. KG, Analytical Sciences Department (Germany); Gronwald, Wolfram [University of Regensburg, Institute of Biophysics and Physical Biochemistry (Germany); Neidig, Klaus-Peter [Bruker BioSpin GmbH, Software Department (Germany); Kalbitzer, Hans Robert [University of Regensburg, Institute of Biophysics and Physical Biochemistry (Germany)], E-mail: hans-robert.kalbitzer@biologie.uni-regensburg.de

    2007-12-15

    Protein-protein interactions are often studied by chemical shift mapping using solution NMR spectroscopy. When heteronuclear data are available the interaction interface is usually predicted by combining the chemical shift changes of different nuclei to a single quantity, the combined chemical shift perturbation {delta}{delta}{sub comb}. In this paper different procedures (published and non-published) to calculate {delta}{delta}{sub comb} are examined that include a variety of different functional forms and weighting factors for each nucleus. The predictive power of all shift mapping methods depends on the magnitude of the overlap of the chemical shift distributions of interacting and non-interacting residues and the cut-off criterion used. In general, the quality of the prediction on the basis of chemical shift changes alone is rather unsatisfactory but the combination of chemical shift changes on the basis of the Hamming or the Euclidian distance can improve the result. The corrected standard deviation to zero of the combined chemical shift changes can provide a reasonable cut-off criterion. As we show combined chemical shifts can also be applied for a more reliable quantitative evaluation of titration data.

  4. Extraction of Protein-Protein Interaction from Scientific Articles by Predicting Dominant Keywords.

    Science.gov (United States)

    Koyabu, Shun; Phan, Thi Thanh Thuy; Ohkawa, Takenao

    2015-01-01

    For the automatic extraction of protein-protein interaction information from scientific articles, a machine learning approach is useful. The classifier is generated from training data represented using several features to decide whether a protein pair in each sentence has an interaction. Such a specific keyword that is directly related to interaction as "bind" or "interact" plays an important role for training classifiers. We call it a dominant keyword that affects the capability of the classifier. Although it is important to identify the dominant keywords, whether a keyword is dominant depends on the context in which it occurs. Therefore, we propose a method for predicting whether a keyword is dominant for each instance. In this method, a keyword that derives imbalanced classification results is tentatively assumed to be a dominant keyword initially. Then the classifiers are separately trained from the instance with and without the assumed dominant keywords. The validity of the assumed dominant keyword is evaluated based on the classification results of the generated classifiers. The assumption is updated by the evaluation result. Repeating this process increases the prediction accuracy of the dominant keyword. Our experimental results using five corpora show the effectiveness of our proposed method with dominant keyword prediction.

  5. Efficient extraction of protein-protein interactions from full-text articles.

    Science.gov (United States)

    Hakenberg, Jörg; Leaman, Robert; Vo, Nguyen Ha; Jonnalagadda, Siddhartha; Sullivan, Ryan; Miller, Christopher; Tari, Luis; Baral, Chitta; Gonzalez, Graciela

    2010-01-01

    Proteins and their interactions govern virtually all cellular processes, such as regulation, signaling, metabolism, and structure. Most experimental findings pertaining to such interactions are discussed in research papers, which, in turn, get curated by protein interaction databases. Authors, editors, and publishers benefit from efforts to alleviate the tasks of searching for relevant papers, evidence for physical interactions, and proper identifiers for each protein involved. The BioCreative II.5 community challenge addressed these tasks in a competition-style assessment to evaluate and compare different methodologies, to make aware of the increasing accuracy of automated methods, and to guide future implementations. In this paper, we present our approaches for protein-named entity recognition, including normalization, and for extraction of protein-protein interactions from full text. Our overall goal is to identify efficient individual components, and we compare various compositions to handle a single full-text article in between 10 seconds and 2 minutes. We propose strategies to transfer document-level annotations to the sentence-level, which allows for the creation of a more fine-grained training corpus; we use this corpus to automatically derive around 5,000 patterns. We rank sentences by relevance to the task of finding novel interactions with physical evidence, using a sentence classifier built from this training corpus. Heuristics for paraphrasing sentences help to further remove unnecessary information that might interfere with patterns, such as additional adjectives, clauses, or bracketed expressions. In BioCreative II.5, we achieved an f-score of 22 percent for finding protein interactions, and 43 percent for mapping proteins to UniProt IDs; disregarding species, f-scores are 30 percent and 55 percent, respectively. On average, our best-performing setup required around 2 minutes per full text. All data and pattern sets as well as Java classes that

  6. N-way FRET microscopy of multiple protein-protein interactions in live cells.

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    Adam D Hoppe

    Full Text Available Fluorescence Resonance Energy Transfer (FRET microscopy has emerged as a powerful tool to visualize nanoscale protein-protein interactions while capturing their microscale organization and millisecond dynamics. Recently, FRET microscopy was extended to imaging of multiple donor-acceptor pairs, thereby enabling visualization of multiple biochemical events within a single living cell. These methods require numerous equations that must be defined on a case-by-case basis. Here, we present a universal multispectral microscopy method (N-Way FRET to enable quantitative imaging for any number of interacting and non-interacting FRET pairs. This approach redefines linear unmixing to incorporate the excitation and emission couplings created by FRET, which cannot be accounted for in conventional linear unmixing. Experiments on a three-fluorophore system using blue, yellow and red fluorescent proteins validate the method in living cells. In addition, we propose a simple linear algebra scheme for error propagation from input data to estimate the uncertainty in the computed FRET images. We demonstrate the strength of this approach by monitoring the oligomerization of three FP-tagged HIV Gag proteins whose tight association in the viral capsid is readily observed. Replacement of one FP-Gag molecule with a lipid raft-targeted FP allowed direct observation of Gag oligomerization with no association between FP-Gag and raft-targeted FP. The N-Way FRET method provides a new toolbox for capturing multiple molecular processes with high spatial and temporal resolution in living cells.

  7. A lanthipeptide library used to identify a protein-protein interaction inhibitor.

    Science.gov (United States)

    Yang, Xiao; Lennard, Katherine R; He, Chang; Walker, Mark C; Ball, Andrew T; Doigneaux, Cyrielle; Tavassoli, Ali; van der Donk, Wilfred A

    2018-04-01

    In this article we describe the production and screening of a genetically encoded library of 10 6 lanthipeptides in Escherichia coli using the substrate-tolerant lanthipeptide synthetase ProcM. This plasmid-encoded library was combined with a bacterial reverse two-hybrid system for the interaction of the HIV p6 protein with the UEV domain of the human TSG101 protein, which is a critical protein-protein interaction for HIV budding from infected cells. Using this approach, we identified an inhibitor of this interaction from the lanthipeptide library, whose activity was verified in vitro and in cell-based virus-like particle-budding assays. Given the variety of lanthipeptide backbone scaffolds that may be produced with ProcM, this method may be used for the generation of genetically encoded libraries of natural product-like lanthipeptides containing substantial structural diversity. Such libraries may be combined with any cell-based assay to identify lanthipeptides with new biological activities.

  8. Annotating activation/inhibition relationships to protein-protein interactions using gene ontology relations.

    Science.gov (United States)

    Yim, Soorin; Yu, Hasun; Jang, Dongjin; Lee, Doheon

    2018-04-11

    Signaling pathways can be reconstructed by identifying 'effect types' (i.e. activation/inhibition) of protein-protein interactions (PPIs). Effect types are composed of 'directions' (i.e. upstream/downstream) and 'signs' (i.e. positive/negative), thereby requiring directions as well as signs of PPIs to predict signaling events from PPI networks. Here, we propose a computational method for systemically annotating effect types to PPIs using relations between functional information of proteins. We used regulates, positively regulates, and negatively regulates relations in Gene Ontology (GO) to predict directions and signs of PPIs. These relations indicate both directions and signs between GO terms so that we can project directions and signs between relevant GO terms to PPIs. Independent test results showed that our method is effective for predicting both directions and signs of PPIs. Moreover, our method outperformed a previous GO-based method that did not consider the relations between GO terms. We annotated effect types to human PPIs and validated several highly confident effect types against literature. The annotated human PPIs are available in Additional file 2 to aid signaling pathway reconstruction and network biology research. We annotated effect types to PPIs by using regulates, positively regulates, and negatively regulates relations in GO. We demonstrated that those relations are effective for predicting not only signs, but also directions of PPIs. The usefulness of those relations suggests their potential applications to other types of interactions such as protein-DNA interactions.

  9. Intracellular antibody capture: A molecular biology approach to inhibitors of protein-protein interactions.

    Science.gov (United States)

    Zhang, Jing; Rabbitts, Terence H

    2014-11-01

    Many proteins of interest in basic biology, translational research studies and for clinical targeting in diseases reside inside the cell and function by interacting with other macromolecules. Protein complexes control basic processes such as development and cell division but also abnormal cell growth when mutations occur such as found in cancer. Interfering with protein-protein interactions is an important aspiration in both basic and disease biology but small molecule inhibitors have been difficult and expensive to isolate. Recently, we have adapted molecular biology techniques to develop a simple set of protocols for isolation of high affinity antibody fragments (in the form of single VH domains) that function within the reducing environment of higher organism cells and can bind to their target molecules. The method called Intracellular Antibody Capture (IAC) has been used to develop inhibitory anti-RAS and anti-LMO2 single domains that have been used for target validation of these antigens in pre-clinical cancer models and illustrate the efficacy of the IAC approach to generation of drug surrogates. Future use of inhibitory VH antibody fragments as drugs in their own right (we term these macrodrugs to distinguish them from small molecule drugs) requires their delivery to target cells in vivo but they can also be templates for small molecule drug development that emulate the binding sites of the antibody fragments. This article is part of a Special Issue entitled: Recent advances in molecular engineering of antibody. Copyright © 2014 Elsevier B.V. All rights reserved.

  10. Detection of Locally Over-Represented GO Terms in Protein-Protein Interaction Networks

    Science.gov (United States)

    LAVALLÉE-ADAM, MATHIEU; COULOMBE, BENOIT; BLANCHETTE, MATHIEU

    2015-01-01

    High-throughput methods for identifying protein-protein interactions produce increasingly complex and intricate interaction networks. These networks are extremely rich in information, but extracting biologically meaningful hypotheses from them and representing them in a human-readable manner is challenging. We propose a method to identify Gene Ontology terms that are locally over-represented in a subnetwork of a given biological network. Specifically, we propose several methods to evaluate the degree of clustering of proteins associated to a particular GO term in both weighted and unweighted PPI networks, and describe efficient methods to estimate the statistical significance of the observed clustering. We show, using Monte Carlo simulations, that our best approximation methods accurately estimate the true p-value, for random scale-free graphs as well as for actual yeast and human networks. When applied to these two biological networks, our approach recovers many known complexes and pathways, but also suggests potential functions for many subnetworks. Online Supplementary Material is available at www.liebertonline.com. PMID:20377456

  11. Prediction of Protein-Protein Interaction By Metasample-Based Sparse Representation

    Directory of Open Access Journals (Sweden)

    Xiuquan Du

    2015-01-01

    Full Text Available Protein-protein interactions (PPIs play key roles in many cellular processes such as transcription regulation, cell metabolism, and endocrine function. Understanding these interactions takes a great promotion to the pathogenesis and treatment of various diseases. A large amount of data has been generated by experimental techniques; however, most of these data are usually incomplete or noisy, and the current biological experimental techniques are always very time-consuming and expensive. In this paper, we proposed a novel method (metasample-based sparse representation classification, MSRC for PPIs prediction. A group of metasamples are extracted from the original training samples and then use the l1-regularized least square method to express a new testing sample as the linear combination of these metasamples. PPIs prediction is achieved by using a discrimination function defined in the representation coefficients. The MSRC is applied to PPIs dataset; it achieves 84.9% sensitivity, and 94.55% specificity, which is slightly lower than support vector machine (SVM and much higher than naive Bayes (NB, neural networks (NN, and k-nearest neighbor (KNN. The result shows that the MSRC is efficient for PPIs prediction.

  12. Targeting protein-protein interaction between MLL1 and reciprocal proteins for leukemia therapy.

    Science.gov (United States)

    Wang, Zhi-Hui; Li, Dong-Dong; Chen, Wei-Lin; You, Qi-Dong; Guo, Xiao-Ke

    2018-01-15

    The mixed lineage leukemia protein-1 (MLL1), as a lysine methyltransferase, predominantly regulates the methylation of histone H3 lysine 4 (H3K4) and functions in hematopoietic stem cell (HSC) self-renewal. MLL1 gene fuses with partner genes that results in the generation of MLL1 fusion proteins (MLL1-FPs), which are frequently detected in acute leukemia. In the progress of leukemogenesis, a great deal of proteins cooperate with MLL1 to form multiprotein complexes serving for the dysregulation of H3K4 methylation, the overexpression of homeobox (HOX) cluster genes, and the consequent generation of leukemia. Hence, disrupting the interactions between MLL1 and the reciprocal proteins has been considered to be a new treatment strategy for leukemia. Here, we reviewed potential protein-protein interactions (PPIs) between MLL1 and its reciprocal proteins, and summarized the inhibitors to target MLL1 PPIs. The druggability of MLL1 PPIs for leukemia were also discussed. Copyright © 2017. Published by Elsevier Ltd.

  13. Predicting Protein-Protein Interaction Sites with a Novel Membership Based Fuzzy SVM Classifier.

    Science.gov (United States)

    Sriwastava, Brijesh K; Basu, Subhadip; Maulik, Ujjwal

    2015-01-01

    Predicting residues that participate in protein-protein interactions (PPI) helps to identify, which amino acids are located at the interface. In this paper, we show that the performance of the classical support vector machine (SVM) algorithm can further be improved with the use of a custom-designed fuzzy membership function, for the partner-specific PPI interface prediction problem. We evaluated the performances of both classical SVM and fuzzy SVM (F-SVM) on the PPI databases of three different model proteomes of Homo sapiens, Escherichia coli and Saccharomyces Cerevisiae and calculated the statistical significance of the developed F-SVM over classical SVM algorithm. We also compared our performance with the available state-of-the-art fuzzy methods in this domain and observed significant performance improvements. To predict interaction sites in protein complexes, local composition of amino acids together with their physico-chemical characteristics are used, where the F-SVM based prediction method exploits the membership function for each pair of sequence fragments. The average F-SVM performance (area under ROC curve) on the test samples in 10-fold cross validation experiment are measured as 77.07, 78.39, and 74.91 percent for the aforementioned organisms respectively. Performances on independent test sets are obtained as 72.09, 73.24 and 82.74 percent respectively. The software is available for free download from http://code.google.com/p/cmater-bioinfo.

  14. Radioresistance related genes screened by protein-protein interaction network analysis in nasopharyngeal carcinoma

    International Nuclear Information System (INIS)

    Zhu Xiaodong; Guo Ya; Qu Song; Li Ling; Huang Shiting; Li Danrong; Zhang Wei

    2012-01-01

    Objective: To discover radioresistance associated molecular biomarkers and its mechanism in nasopharyngeal carcinoma by protein-protein interaction network analysis. Methods: Whole genome expression microarray was applied to screen out differentially expressed genes in two cell lines CNE-2R and CNE-2 with different radiosensitivity. Four differentially expressed genes were randomly selected for further verification by the semi-quantitative RT-PCR analysis with self-designed primers. The common differentially expressed genes from two experiments were analyzed with the SNOW online database in order to find out the central node related to the biomarkers of nasopharyngeal carcinoma radioresistance. The expression of STAT1 in CNE-2R and CNE-2 cells was measured by Western blot. Results: Compared with CNE-2 cells, 374 genes in CNE-2R cells were differentially expressed while 197 genes showed significant differences. Four randomly selected differentially expressed genes were verified by RT-PCR and had same change trend in consistent with the results of chip assay. Analysis with the SNOW database demonstrated that those 197 genes could form a complicated interaction network where STAT1 and JUN might be two key nodes. Indeed, the STAT1-α expression in CNE-2R was higher than that in CNE-2 (t=4.96, P<0.05). Conclusions: The key nodes of STAT1 and JUN may be the molecular biomarkers leading to radioresistance in nasopharyngeal carcinoma, and STAT1-α might have close relationship with radioresistance. (authors)

  15. P-Finder: Reconstruction of Signaling Networks from Protein-Protein Interactions and GO Annotations.

    Science.gov (United States)

    Young-Rae Cho; Yanan Xin; Speegle, Greg

    2015-01-01

    Because most complex genetic diseases are caused by defects of cell signaling, illuminating a signaling cascade is essential for understanding their mechanisms. We present three novel computational algorithms to reconstruct signaling networks between a starting protein and an ending protein using genome-wide protein-protein interaction (PPI) networks and gene ontology (GO) annotation data. A signaling network is represented as a directed acyclic graph in a merged form of multiple linear pathways. An advanced semantic similarity metric is applied for weighting PPIs as the preprocessing of all three methods. The first algorithm repeatedly extends the list of nodes based on path frequency towards an ending protein. The second algorithm repeatedly appends edges based on the occurrence of network motifs which indicate the link patterns more frequently appearing in a PPI network than in a random graph. The last algorithm uses the information propagation technique which iteratively updates edge orientations based on the path strength and merges the selected directed edges. Our experimental results demonstrate that the proposed algorithms achieve higher accuracy than previous methods when they are tested on well-studied pathways of S. cerevisiae. Furthermore, we introduce an interactive web application tool, called P-Finder, to visualize reconstructed signaling networks.

  16. A Novel Approach for Protein-Named Entity Recognition and Protein-Protein Interaction Extraction

    Directory of Open Access Journals (Sweden)

    Meijing Li

    2015-01-01

    Full Text Available Many researchers focus on developing protein-named entity recognition (Protein-NER or PPI extraction systems. However, the studies about these two topics cannot be merged well; then existing PPI extraction systems’ Protein-NER still needs to improve. In this paper, we developed the protein-protein interaction extraction system named PPIMiner based on Support Vector Machine (SVM and parsing tree. PPIMiner consists of three main models: natural language processing (NLP model, Protein-NER model, and PPI discovery model. The Protein-NER model, which is named ProNER, identifies the protein names based on two methods: dictionary-based method and machine learning-based method. ProNER is capable of identifying more proteins than dictionary-based Protein-NER model in other existing systems. The final discovered PPIs extracted via PPI discovery model are represented in detail because we showed the protein interaction types and the occurrence frequency through two different methods. In the experiments, the result shows that the performances achieved by our ProNER and PPI discovery model are better than other existing tools. PPIMiner applied this protein-named entity recognition approach and parsing tree based PPI extraction method to improve the performance of PPI extraction. We also provide an easy-to-use interface to access PPIs database and an online system for PPIs extraction and Protein-NER.

  17. Protein-Protein Interactions Prediction Using a Novel Local Conjoint Triad Descriptor of Amino Acid Sequences

    Directory of Open Access Journals (Sweden)

    Jun Wang

    2017-11-01

    Full Text Available Protein-protein interactions (PPIs play crucial roles in almost all cellular processes. Although a large amount of PPIs have been verified by high-throughput techniques in the past decades, currently known PPIs pairs are still far from complete. Furthermore, the wet-lab experiments based techniques for detecting PPIs are time-consuming and expensive. Hence, it is urgent and essential to develop automatic computational methods to efficiently and accurately predict PPIs. In this paper, a sequence-based approach called DNN-LCTD is developed by combining deep neural networks (DNNs and a novel local conjoint triad description (LCTD feature representation. LCTD incorporates the advantage of local description and conjoint triad, thus, it is capable to account for the interactions between residues in both continuous and discontinuous regions of amino acid sequences. DNNs can not only learn suitable features from the data by themselves, but also learn and discover hierarchical representations of data. When performing on the PPIs data of Saccharomyces cerevisiae, DNN-LCTD achieves superior performance with accuracy as 93.12%, precision as 93.75%, sensitivity as 93.83%, area under the receiver operating characteristic curve (AUC as 97.92%, and it only needs 718 s. These results indicate DNN-LCTD is very promising for predicting PPIs. DNN-LCTD can be a useful supplementary tool for future proteomics study.

  18. Femtosecond UV-laser pulses to unveil protein-protein interactions in living cells.

    Science.gov (United States)

    Itri, Francesco; Monti, Daria M; Della Ventura, Bartolomeo; Vinciguerra, Roberto; Chino, Marco; Gesuele, Felice; Lombardi, Angelina; Velotta, Raffaele; Altucci, Carlo; Birolo, Leila; Piccoli, Renata; Arciello, Angela

    2016-02-01

    A hallmark to decipher bioprocesses is to characterize protein-protein interactions in living cells. To do this, the development of innovative methodologies, which do not alter proteins and their natural environment, is particularly needed. Here, we report a method (LUCK, Laser UV Cross-linKing) to in vivo cross-link proteins by UV-laser irradiation of living cells. Upon irradiation of HeLa cells under controlled conditions, cross-linked products of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) were detected, whose yield was found to be a linear function of the total irradiation energy. We demonstrated that stable dimers of GAPDH were formed through intersubunit cross-linking, as also observed when the pure protein was irradiated by UV-laser in vitro. We proposed a defined patch of aromatic residues located at the enzyme subunit interface as the cross-linking sites involved in dimer formation. Hence, by this technique, UV-laser is able to photofix protein surfaces that come in direct contact. Due to the ultra-short time scale of UV-laser-induced cross-linking, this technique could be extended to weld even transient protein interactions in their native context.

  19. PPI finder: a mining tool for human protein-protein interactions.

    Directory of Open Access Journals (Sweden)

    Min He

    Full Text Available BACKGROUND: The exponential increase of published biomedical literature prompts the use of text mining tools to manage the information overload automatically. One of the most common applications is to mine protein-protein interactions (PPIs from PubMed abstracts. Currently, most tools in mining PPIs from literature are using co-occurrence-based approaches or rule-based approaches. Hybrid methods (frame-based approaches by combining these two methods may have better performance in predicting PPIs. However, the predicted PPIs from these methods are rarely evaluated by known PPI databases and co-occurred terms in Gene Ontology (GO database. METHODOLOGY/PRINCIPAL FINDINGS: We here developed a web-based tool, PPI Finder, to mine human PPIs from PubMed abstracts based on their co-occurrences and interaction words, followed by evidences in human PPI databases and shared terms in GO database. Only 28% of the co-occurred pairs in PubMed abstracts appeared in any of the commonly used human PPI databases (HPRD, BioGRID and BIND. On the other hand, of the known PPIs in HPRD, 69% showed co-occurrences in the literature, and 65% shared GO terms. CONCLUSIONS: PPI Finder provides a useful tool for biologists to uncover potential novel PPIs. It is freely accessible at http://liweilab.genetics.ac.cn/tm/.

  20. Influence of degree correlations on network structure and stability in protein-protein interaction networks

    Directory of Open Access Journals (Sweden)

    Zimmer Ralf

    2007-08-01

    Full Text Available Abstract Background The existence of negative correlations between degrees of interacting proteins is being discussed since such negative degree correlations were found for the large-scale yeast protein-protein interaction (PPI network of Ito et al. More recent studies observed no such negative correlations for high-confidence interaction sets. In this article, we analyzed a range of experimentally derived interaction networks to understand the role and prevalence of degree correlations in PPI networks. We investigated how degree correlations influence the structure of networks and their tolerance against perturbations such as the targeted deletion of hubs. Results For each PPI network, we simulated uncorrelated, positively and negatively correlated reference networks. Here, a simple model was developed which can create different types of degree correlations in a network without changing the degree distribution. Differences in static properties associated with degree correlations were compared by analyzing the network characteristics of the original PPI and reference networks. Dynamics were compared by simulating the effect of a selective deletion of hubs in all networks. Conclusion Considerable differences between the network types were found for the number of components in the original networks. Negatively correlated networks are fragmented into significantly less components than observed for positively correlated networks. On the other hand, the selective deletion of hubs showed an increased structural tolerance to these deletions for the positively correlated networks. This results in a lower rate of interaction loss in these networks compared to the negatively correlated networks and a decreased disintegration rate. Interestingly, real PPI networks are most similar to the randomly correlated references with respect to all properties analyzed. Thus, although structural properties of networks can be modified considerably by degree

  1. DomPep--a general method for predicting modular domain-mediated protein-protein interactions.

    Directory of Open Access Journals (Sweden)

    Lei Li

    Full Text Available Protein-protein interactions (PPIs are frequently mediated by the binding of a modular domain in one protein to a short, linear peptide motif in its partner. The advent of proteomic methods such as peptide and protein arrays has led to the accumulation of a wealth of interaction data for modular interaction domains. Although several computational programs have been developed to predict modular domain-mediated PPI events, they are often restricted to a given domain type. We describe DomPep, a method that can potentially be used to predict PPIs mediated by any modular domains. DomPep combines proteomic data with sequence information to achieve high accuracy and high coverage in PPI prediction. Proteomic binding data were employed to determine a simple yet novel parameter Ligand-Binding Similarity which, in turn, is used to calibrate Domain Sequence Identity and Position-Weighted-Matrix distance, two parameters that are used in constructing prediction models. Moreover, DomPep can be used to predict PPIs for both domains with experimental binding data and those without. Using the PDZ and SH2 domain families as test cases, we show that DomPep can predict PPIs with accuracies superior to existing methods. To evaluate DomPep as a discovery tool, we deployed DomPep to identify interactions mediated by three human PDZ domains. Subsequent in-solution binding assays validated the high accuracy of DomPep in predicting authentic PPIs at the proteome scale. Because DomPep makes use of only interaction data and the primary sequence of a domain, it can be readily expanded to include other types of modular domains.

  2. Computational prediction of protein-protein interactions in Leishmania predicted proteomes.

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    Antonio M Rezende

    Full Text Available The Trypanosomatids parasites Leishmania braziliensis, Leishmania major and Leishmania infantum are important human pathogens. Despite of years of study and genome availability, effective vaccine has not been developed yet, and the chemotherapy is highly toxic. Therefore, it is clear just interdisciplinary integrated studies will have success in trying to search new targets for developing of vaccines and drugs. An essential part of this rationale is related to protein-protein interaction network (PPI study which can provide a better understanding of complex protein interactions in biological system. Thus, we modeled PPIs for Trypanosomatids through computational methods using sequence comparison against public database of protein or domain interaction for interaction prediction (Interolog Mapping and developed a dedicated combined system score to address the predictions robustness. The confidence evaluation of network prediction approach was addressed using gold standard positive and negative datasets and the AUC value obtained was 0.94. As result, 39,420, 43,531 and 45,235 interactions were predicted for L. braziliensis, L. major and L. infantum respectively. For each predicted network the top 20 proteins were ranked by MCC topological index. In addition, information related with immunological potential, degree of protein sequence conservation among orthologs and degree of identity compared to proteins of potential parasite hosts was integrated. This information integration provides a better understanding and usefulness of the predicted networks that can be valuable to select new potential biological targets for drug and vaccine development. Network modularity which is a key when one is interested in destabilizing the PPIs for drug or vaccine purposes along with multiple alignments of the predicted PPIs were performed revealing patterns associated with protein turnover. In addition, around 50% of hypothetical protein present in the networks

  3. Utilizing Mechanistic Cross-Linking Technology to Study Protein-Protein Interactions: An Experiment Designed for an Undergraduate Biochemistry Lab

    Science.gov (United States)

    Finzel, Kara; Beld, Joris; Burkart, Michael D.; Charkoudian, Louise K.

    2017-01-01

    Over the past decade, mechanistic cross-linking probes have been used to study protein-protein interactions in natural product biosynthetic pathways. This approach is highly interdisciplinary, combining elements of protein biochemistry, organic chemistry, and computational docking. Herein, we described the development of an experiment to engage…

  4. Binding specificity and in vivo targets of the EH domain, a novel protein-protein interaction module

    DEFF Research Database (Denmark)

    Salcini, A E; Confalonieri, S; Doria, M

    1997-01-01

    EH is a recently identified protein-protein interaction domain found in the signal transducers Eps15 and Eps15R and several other proteins of yeast nematode. We show that EH domains from Eps15 and Eps15R bind in vitro to peptides containing an asparagine-proline-phenylalanine (NPF) motif. Direct...

  5. Prediction and characterization of protein-protein interaction networks in swine

    Directory of Open Access Journals (Sweden)

    Wang Fen

    2012-01-01

    Full Text Available Abstract Background Studying the large-scale protein-protein interaction (PPI network is important in understanding biological processes. The current research presents the first PPI map of swine, which aims to give new insights into understanding their biological processes. Results We used three methods, Interolog-based prediction of porcine PPI network, domain-motif interactions from structural topology-based prediction of porcine PPI network and motif-motif interactions from structural topology-based prediction of porcine PPI network, to predict porcine protein interactions among 25,767 porcine proteins. We predicted 20,213, 331,484, and 218,705 porcine PPIs respectively, merged the three results into 567,441 PPIs, constructed four PPI networks, and analyzed the topological properties of the porcine PPI networks. Our predictions were validated with Pfam domain annotations and GO annotations. Averages of 70, 10,495, and 863 interactions were related to the Pfam domain-interacting pairs in iPfam database. For comparison, randomized networks were generated, and averages of only 4.24, 66.79, and 44.26 interactions were associated with Pfam domain-interacting pairs in iPfam database. In GO annotations, we found 52.68%, 75.54%, 27.20% of the predicted PPIs sharing GO terms respectively. However, the number of PPI pairs sharing GO terms in the 10,000 randomized networks reached 52.68%, 75.54%, 27.20% is 0. Finally, we determined the accuracy and precision of the methods. The methods yielded accuracies of 0.92, 0.53, and 0.50 at precisions of about 0.93, 0.74, and 0.75, respectively. Conclusion The results reveal that the predicted PPI networks are considerably reliable. The present research is an important pioneering work on protein function research. The porcine PPI data set, the confidence score of each interaction and a list of related data are available at (http://pppid.biositemap.com/.

  6. Inference of gene-phenotype associations via protein-protein interaction and orthology.

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    Panwen Wang

    Full Text Available One of the fundamental goals of genetics is to understand gene functions and their associated phenotypes. To achieve this goal, in this study we developed a computational algorithm that uses orthology and protein-protein interaction information to infer gene-phenotype associations for multiple species. Furthermore, we developed a web server that provides genome-wide phenotype inference for six species: fly, human, mouse, worm, yeast, and zebrafish. We evaluated our inference method by comparing the inferred results with known gene-phenotype associations. The high Area Under the Curve values suggest a significant performance of our method. By applying our method to two human representative diseases, Type 2 Diabetes and Breast Cancer, we demonstrated that our method is able to identify related Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways. The web server can be used to infer functions and putative phenotypes of a gene along with the candidate genes of a phenotype, and thus aids in disease candidate gene discovery. Our web server is available at http://jjwanglab.org/PhenoPPIOrth.

  7. Stapled peptides as a new technology to investigate protein-protein interactions in human platelets.

    Science.gov (United States)

    Iegre, Jessica; Ahmed, Niaz S; Gaynord, Josephine S; Wu, Yuteng; Herlihy, Kara M; Tan, Yaw Sing; Lopes-Pires, Maria E; Jha, Rupam; Lau, Yu Heng; Sore, Hannah F; Verma, Chandra; O' Donovan, Daniel H; Pugh, Nicholas; Spring, David R

    2018-05-28

    Platelets are blood cells with numerous crucial pathophysiological roles in hemostasis, cardiovascular thrombotic events and cancer metastasis. Platelet activation requires the engagement of intracellular signalling pathways that involve protein-protein interactions (PPIs). A better understanding of these pathways is therefore crucial for the development of selective anti-platelet drugs. New strategies for studying PPIs in human platelets are required to overcome limitations associated with conventional platelet research methods. For example, small molecule inhibitors can lack selectivity and are often difficult to design and synthesise. Additionally, development of transgenic animal models is costly and time-consuming and conventional recombinant techniques are ineffective due to the lack of a nucleus in platelets. Herein, we describe the generation of a library of novel, functionalised stapled peptides and their first application in the investigation of platelet PPIs. Moreover, the use of platelet-permeable stapled Bim BH3 peptides confirms the part of Bim in phosphatidyl-serine (PS) exposure and reveals a role for the Bim protein in platelet activatory processes. Our work demonstrates that functionalised stapled peptides are a complementary alternative to conventional platelet research methods, and could make a significant contribution to the understanding of platelet signalling pathways and hence to the development of anti-platelet drugs.

  8. Classification of protein-protein interaction full-text documents using text and citation network features.

    Science.gov (United States)

    Kolchinsky, Artemy; Abi-Haidar, Alaa; Kaur, Jasleen; Hamed, Ahmed Abdeen; Rocha, Luis M

    2010-01-01

    We participated (as Team 9) in the Article Classification Task of the Biocreative II.5 Challenge: binary classification of full-text documents relevant for protein-protein interaction. We used two distinct classifiers for the online and offline challenges: 1) the lightweight Variable Trigonometric Threshold (VTT) linear classifier we successfully introduced in BioCreative 2 for binary classification of abstracts and 2) a novel Naive Bayes classifier using features from the citation network of the relevant literature. We supplemented the supplied training data with full-text documents from the MIPS database. The lightweight VTT classifier was very competitive in this new full-text scenario: it was a top-performing submission in this task, taking into account the rank product of the Area Under the interpolated precision and recall Curve, Accuracy, Balanced F-Score, and Matthew's Correlation Coefficient performance measures. The novel citation network classifier for the biomedical text mining domain, while not a top performing classifier in the challenge, performed above the central tendency of all submissions, and therefore indicates a promising new avenue to investigate further in bibliome informatics.

  9. Sequence-based prediction of protein protein interaction using a deep-learning algorithm.

    Science.gov (United States)

    Sun, Tanlin; Zhou, Bo; Lai, Luhua; Pei, Jianfeng

    2017-05-25

    Protein-protein interactions (PPIs) are critical for many biological processes. It is therefore important to develop accurate high-throughput methods for identifying PPI to better understand protein function, disease occurrence, and therapy design. Though various computational methods for predicting PPI have been developed, their robustness for prediction with external datasets is unknown. Deep-learning algorithms have achieved successful results in diverse areas, but their effectiveness for PPI prediction has not been tested. We used a stacked autoencoder, a type of deep-learning algorithm, to study the sequence-based PPI prediction. The best model achieved an average accuracy of 97.19% with 10-fold cross-validation. The prediction accuracies for various external datasets ranged from 87.99% to 99.21%, which are superior to those achieved with previous methods. To our knowledge, this research is the first to apply a deep-learning algorithm to sequence-based PPI prediction, and the results demonstrate its potential in this field.

  10. Adaptive compressive learning for prediction of protein-protein interactions from primary sequence.

    Science.gov (United States)

    Zhang, Ya-Nan; Pan, Xiao-Yong; Huang, Yan; Shen, Hong-Bin

    2011-08-21

    Protein-protein interactions (PPIs) play an important role in biological processes. Although much effort has been devoted to the identification of novel PPIs by integrating experimental biological knowledge, there are still many difficulties because of lacking enough protein structural and functional information. It is highly desired to develop methods based only on amino acid sequences for predicting PPIs. However, sequence-based predictors are often struggling with the high-dimensionality causing over-fitting and high computational complexity problems, as well as the redundancy of sequential feature vectors. In this paper, a novel computational approach based on compressed sensing theory is proposed to predict yeast Saccharomyces cerevisiae PPIs from primary sequence and has achieved promising results. The key advantage of the proposed compressed sensing algorithm is that it can compress the original high-dimensional protein sequential feature vector into a much lower but more condensed space taking the sparsity property of the original signal into account. What makes compressed sensing much more attractive in protein sequence analysis is its compressed signal can be reconstructed from far fewer measurements than what is usually considered necessary in traditional Nyquist sampling theory. Experimental results demonstrate that proposed compressed sensing method is powerful for analyzing noisy biological data and reducing redundancy in feature vectors. The proposed method represents a new strategy of dealing with high-dimensional protein discrete model and has great potentiality to be extended to deal with many other complicated biological systems. Copyright © 2011 Elsevier Ltd. All rights reserved.

  11. A domain-based approach to predict protein-protein interactions

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    Resat Haluk

    2007-06-01

    Full Text Available Abstract Background Knowing which proteins exist in a certain organism or cell type and how these proteins interact with each other are necessary for the understanding of biological processes at the whole cell level. The determination of the protein-protein interaction (PPI networks has been the subject of extensive research. Despite the development of reasonably successful methods, serious technical difficulties still exist. In this paper we present DomainGA, a quantitative computational approach that uses the information about the domain-domain interactions to predict the interactions between proteins. Results DomainGA is a multi-parameter optimization method in which the available PPI information is used to derive a quantitative scoring scheme for the domain-domain pairs. Obtained domain interaction scores are then used to predict whether a pair of proteins interacts. Using the yeast PPI data and a series of tests, we show the robustness and insensitivity of the DomainGA method to the selection of the parameter sets, score ranges, and detection rules. Our DomainGA method achieves very high explanation ratios for the positive and negative PPIs in yeast. Based on our cross-verification tests on human PPIs, comparison of the optimized scores with the structurally observed domain interactions obtained from the iPFAM database, and sensitivity and specificity analysis; we conclude that our DomainGA method shows great promise to be applicable across multiple organisms. Conclusion We envision the DomainGA as a first step of a multiple tier approach to constructing organism specific PPIs. As it is based on fundamental structural information, the DomainGA approach can be used to create potential PPIs and the accuracy of the constructed interaction template can be further improved using complementary methods. Explanation ratios obtained in the reported test case studies clearly show that the false prediction rates of the template networks constructed

  12. Comparing human-Salmonella with plant-Salmonella protein-protein interaction predictions

    Directory of Open Access Journals (Sweden)

    Sylvia eSchleker

    2015-01-01

    Full Text Available Salmonellosis is the most frequent food-borne disease world-wide and can be transmitted to humans by a variety of routes, especially via animal and plant products. Salmonella bacteria are believed to use not only animal and human but also plant hosts despite their evolutionary distance. This raises the question if Salmonella employs similar mechanisms in infection of these diverse hosts. Given that most of our understanding comes from its interaction with human hosts, we investigate here to what degree knowledge of Salmonella-human interactions can be transferred to the Salmonella-plant system. Reviewed are recent publications on analysis and prediction of Salmonella-host interactomes. Putative protein-protein interactions (PPIs between Salmonella and its human and Arabidopsis hosts were retrieved utilizing purely interolog-based approaches in which predictions were inferred based on available sequence and domain information of known PPIs, and machine learning approaches that integrate a larger set of useful information from different sources. Transfer learning is an especially suitable machine learning technique to predict plant host targets from the knowledge of human host targets. A comparison of the prediction results with transcriptomic data shows a clear overlap between the host proteins predicted to be targeted by PPIs and their gene ontology enrichment in both host species and regulation of gene expression. In particular, the cellular processes Salmonella interferes with in plants and humans are catabolic processes. The details of how these processes are targeted, however, are quite different between the two organisms, as expected based on their evolutionary and habitat differences. Possible implications of this observation on evolution of host-pathogen communication are discussed.

  13. Functional mapping of protein-protein interactions in an enzyme complex by directed evolution.

    Directory of Open Access Journals (Sweden)

    Kathrin Roderer

    Full Text Available The shikimate pathway enzyme chorismate mutase converts chorismate into prephenate, a precursor of Tyr and Phe. The intracellular chorismate mutase (MtCM of Mycobacterium tuberculosis is poorly active on its own, but becomes >100-fold more efficient upon formation of a complex with the first enzyme of the shikimate pathway, 3-deoxy-d-arabino-heptulosonate-7-phosphate synthase (MtDS. The crystal structure of the enzyme complex revealed involvement of C-terminal MtCM residues with the MtDS interface. Here we employed evolutionary strategies to probe the tolerance to substitution of the C-terminal MtCM residues from positions 84-90. Variants with randomized positions were subjected to stringent selection in vivo requiring productive interactions with MtDS for survival. Sequence patterns identified in active library members coincide with residue conservation in natural chorismate mutases of the AroQδ subclass to which MtCM belongs. An Arg-Gly dyad at positions 85 and 86, invariant in AroQδ sequences, was intolerant to mutation, whereas Leu88 and Gly89 exhibited a preference for small and hydrophobic residues in functional MtCM-MtDS complexes. In the absence of MtDS, selection under relaxed conditions identifies positions 84-86 as MtCM integrity determinants, suggesting that the more C-terminal residues function in the activation by MtDS. Several MtCM variants, purified using a novel plasmid-based T7 RNA polymerase gene expression system, showed that a diminished ability to physically interact with MtDS correlates with reduced activatability and feedback regulatory control by Tyr and Phe. Mapping critical protein-protein interaction sites by evolutionary strategies may pinpoint promising targets for drugs that interfere with the activity of protein complexes.

  14. Functional mapping of protein-protein interactions in an enzyme complex by directed evolution.

    Science.gov (United States)

    Roderer, Kathrin; Neuenschwander, Martin; Codoni, Giosiana; Sasso, Severin; Gamper, Marianne; Kast, Peter

    2014-01-01

    The shikimate pathway enzyme chorismate mutase converts chorismate into prephenate, a precursor of Tyr and Phe. The intracellular chorismate mutase (MtCM) of Mycobacterium tuberculosis is poorly active on its own, but becomes >100-fold more efficient upon formation of a complex with the first enzyme of the shikimate pathway, 3-deoxy-d-arabino-heptulosonate-7-phosphate synthase (MtDS). The crystal structure of the enzyme complex revealed involvement of C-terminal MtCM residues with the MtDS interface. Here we employed evolutionary strategies to probe the tolerance to substitution of the C-terminal MtCM residues from positions 84-90. Variants with randomized positions were subjected to stringent selection in vivo requiring productive interactions with MtDS for survival. Sequence patterns identified in active library members coincide with residue conservation in natural chorismate mutases of the AroQδ subclass to which MtCM belongs. An Arg-Gly dyad at positions 85 and 86, invariant in AroQδ sequences, was intolerant to mutation, whereas Leu88 and Gly89 exhibited a preference for small and hydrophobic residues in functional MtCM-MtDS complexes. In the absence of MtDS, selection under relaxed conditions identifies positions 84-86 as MtCM integrity determinants, suggesting that the more C-terminal residues function in the activation by MtDS. Several MtCM variants, purified using a novel plasmid-based T7 RNA polymerase gene expression system, showed that a diminished ability to physically interact with MtDS correlates with reduced activatability and feedback regulatory control by Tyr and Phe. Mapping critical protein-protein interaction sites by evolutionary strategies may pinpoint promising targets for drugs that interfere with the activity of protein complexes.

  15. Towards a rigorous network of protein-protein interactions of the model sulfate reducer Desulfovibrio vulgaris Hildenborough.

    Directory of Open Access Journals (Sweden)

    Swapnil R Chhabra

    Full Text Available Protein-protein interactions offer an insight into cellular processes beyond what may be obtained by the quantitative functional genomics tools of proteomics and transcriptomics. The aforementioned tools have been extensively applied to study Escherichia coli and other aerobes and more recently to study the stress response behavior of Desulfovibrio vulgaris Hildenborough, a model obligate anaerobe and sulfate reducer and the subject of this study. Here we carried out affinity purification followed by mass spectrometry to reconstruct an interaction network among 12 chromosomally encoded bait and 90 prey proteins based on 134 bait-prey interactions identified to be of high confidence. Protein-protein interaction data are often plagued by the lack of adequate controls and replication analyses necessary to assess confidence in the results, including identification of potential false positives. We addressed these issues through the use of biological replication, exponentially modified protein abundance indices, results from an experimental negative control, and a statistical test to assign confidence to each putative interacting pair applicable to small interaction data studies. We discuss the biological significance of metabolic features of D. vulgaris revealed by these protein-protein interaction data and the observed protein modifications. These include the distinct role of the putative carbon monoxide-induced hydrogenase, unique electron transfer routes associated with different oxidoreductases, and the possible role of methylation in regulating sulfate reduction.

  16. Strong interactions at high energy

    International Nuclear Information System (INIS)

    Anselmino, M.

    1995-01-01

    Spin effects in strong interaction high energy processes are subtle phenomena which involve both short and long distance physics and test perturbative and non perturbative aspects of QCD. Moreover, depending on quantities like interferences between different amplitudes and relative phases, spin observables always test a theory at a fundamental quantum mechanical level; it is then no surprise that spin data are often difficult to accommodate within the existing models. A report is made on the main issues and contributions discussed in the parallel Session on the open-quote open-quote Strong interactions at high energy close-quote close-quote in this Conference. copyright 1995 American Institute of Physics

  17. Prediction of Protein-Protein Interactions by NanoLuc-Based Protein-Fragment Complementation Assay | Office of Cancer Genomics

    Science.gov (United States)

    The CTD2 Center at Emory has developed a new NanoLuc®-based protein-fragment complementation assay (NanoPCA) which allows the detection of novel protein-protein interactions (PPI). NanoPCA allows the study of PPI dynamics with reversible interactions.  Read the abstract. Experimental Approaches Read the detailed Experimetnal Approaches. 

  18. Relative quantification of protein-protein interactions using a dual luciferase reporter pull-down assay system.

    Directory of Open Access Journals (Sweden)

    Shuaizheng Jia

    Full Text Available The identification and quantitative analysis of protein-protein interactions are essential to the functional characterization of proteins in the post-proteomics era. The methods currently available are generally time-consuming, technically complicated, insensitive and/or semi-quantitative. The lack of simple, sensitive approaches to precisely quantify protein-protein interactions still prevents our understanding of the functions of many proteins. Here, we develop a novel dual luciferase reporter pull-down assay by combining a biotinylated Firefly luciferase pull-down assay with a dual luciferase reporter assay. The biotinylated Firefly luciferase-tagged protein enables rapid and efficient isolation of a putative Renilla luciferase-tagged binding protein from a relatively small amount of sample. Both of these proteins can be quantitatively detected using the dual luciferase reporter assay system. Protein-protein interactions, including Fos-Jun located in the nucleus; MAVS-TRAF3 in cytoplasm; inducible IRF3 dimerization; viral protein-regulated interactions, such as MAVS-MAVS and MAVS-TRAF3; IRF3 dimerization; and protein interaction domain mapping, are studied using this novel assay system. Herein, we demonstrate that this dual luciferase reporter pull-down assay enables the quantification of the relative amounts of interacting proteins that bind to streptavidin-coupled beads for protein purification. This study provides a simple, rapid, sensitive, and efficient approach to identify and quantify relative protein-protein interactions. Importantly, the dual luciferase reporter pull-down method will facilitate the functional determination of proteins.

  19. The colours of strong interaction

    International Nuclear Information System (INIS)

    1995-01-01

    The aim of this session is to draw a consistent framework about the different ways to consider strong interaction. A large part is dedicated to theoretical work and the latest experimental results obtained at the first electron collider HERA are discussed. (A.C.)

  20. Construction and analysis of protein-protein interaction networks based on proteomics data of prostate cancer

    Science.gov (United States)

    CHEN, CHEN; SHEN, HONG; ZHANG, LI-GUO; LIU, JIAN; CAO, XIAO-GE; YAO, AN-LIANG; KANG, SHAO-SAN; GAO, WEI-XING; HAN, HUI; CAO, FENG-HONG; LI, ZHI-GUO

    2016-01-01

    Currently, using human prostate cancer (PCa) tissue samples to conduct proteomics research has generated a large amount of data; however, only a very small amount has been thoroughly investigated. In this study, we manually carried out the mining of the full text of proteomics literature that involved comparisons between PCa and normal or benign tissue and identified 41 differentially expressed proteins verified or reported more than 2 times from different research studies. We regarded these proteins as seed proteins to construct a protein-protein interaction (PPI) network. The extended network included one giant network, which consisted of 1,264 nodes connected via 1,744 edges, and 3 small separate components. The backbone network was then constructed, which was derived from key nodes and the subnetwork consisting of the shortest path between seed proteins. Topological analyses of these networks were conducted to identify proteins essential for the genesis of PCa. Solute carrier family 2 (facilitated glucose transporter), member 4 (SLC2A4) had the highest closeness centrality located in the center of each network, and the highest betweenness centrality and largest degree in the backbone network. Tubulin, beta 2C (TUBB2C) had the largest degree in the giant network and subnetwork. In addition, using module analysis of the whole PPI network, we obtained a densely connected region. Functional annotation indicated that the Ras protein signal transduction biological process, mitogen-activated protein kinase (MAPK), neurotrophin and the gonadotropin-releasing hormone (GnRH) signaling pathway may play an important role in the genesis and development of PCa. Further investigation of the SLC2A4, TUBB2C proteins, and these biological processes and pathways may therefore provide a potential target for the diagnosis and treatment of PCa. PMID:27121963

  1. A comprehensive benchmark of kernel methods to extract protein-protein interactions from literature.

    Directory of Open Access Journals (Sweden)

    Domonkos Tikk

    Full Text Available The most important way of conveying new findings in biomedical research is scientific publication. Extraction of protein-protein interactions (PPIs reported in scientific publications is one of the core topics of text mining in the life sciences. Recently, a new class of such methods has been proposed - convolution kernels that identify PPIs using deep parses of sentences. However, comparing published results of different PPI extraction methods is impossible due to the use of different evaluation corpora, different evaluation metrics, different tuning procedures, etc. In this paper, we study whether the reported performance metrics are robust across different corpora and learning settings and whether the use of deep parsing actually leads to an increase in extraction quality. Our ultimate goal is to identify the one method that performs best in real-life scenarios, where information extraction is performed on unseen text and not on specifically prepared evaluation data. We performed a comprehensive benchmarking of nine different methods for PPI extraction that use convolution kernels on rich linguistic information. Methods were evaluated on five different public corpora using cross-validation, cross-learning, and cross-corpus evaluation. Our study confirms that kernels using dependency trees generally outperform kernels based on syntax trees. However, our study also shows that only the best kernel methods can compete with a simple rule-based approach when the evaluation prevents information leakage between training and test corpora. Our results further reveal that the F-score of many approaches drops significantly if no corpus-specific parameter optimization is applied and that methods reaching a good AUC score often perform much worse in terms of F-score. We conclude that for most kernels no sensible estimation of PPI extraction performance on new text is possible, given the current heterogeneity in evaluation data. Nevertheless, our study

  2. Analysis of protein-protein docking decoys using interaction fingerprints: application to the reconstruction of CaM-ligand complexes

    Directory of Open Access Journals (Sweden)

    Uchikoga Nobuyuki

    2010-05-01

    Full Text Available Abstract Background Protein-protein docking for proteins with large conformational changes was analyzed by using interaction fingerprints, one of the scales for measuring similarities among complex structures, utilized especially for searching near-native protein-ligand or protein-protein complex structures. Here, we have proposed a combined method for analyzing protein-protein docking by taking large conformational changes into consideration. This combined method consists of ensemble soft docking with multiple protein structures, refinement of complexes, and cluster analysis using interaction fingerprints and energy profiles. Results To test for the applicability of this combined method, various CaM-ligand complexes were reconstructed from the NMR structures of unbound CaM. For the purpose of reconstruction, we used three known CaM-ligands, namely, the CaM-binding peptides of cyclic nucleotide gateway (CNG, CaM kinase kinase (CaMKK and the plasma membrane Ca2+ ATPase pump (PMCA, and thirty-one structurally diverse CaM conformations. For each ligand, 62000 CaM-ligand complexes were generated in the docking step and the relationship between their energy profiles and structural similarities to the native complex were analyzed using interaction fingerprint and RMSD. Near-native clusters were obtained in the case of CNG and CaMKK. Conclusions The interaction fingerprint method discriminated near-native structures better than the RMSD method in cluster analysis. We showed that a combined method that includes the interaction fingerprint is very useful for protein-protein docking analysis of certain cases.

  3. BioPlex Display: An Interactive Suite for Large-Scale AP-MS Protein-Protein Interaction Data.

    Science.gov (United States)

    Schweppe, Devin K; Huttlin, Edward L; Harper, J Wade; Gygi, Steven P

    2018-01-05

    The development of large-scale data sets requires a new means to display and disseminate research studies to large audiences. Knowledge of protein-protein interaction (PPI) networks has become a principle interest of many groups within the field of proteomics. At the confluence of technologies, such as cross-linking mass spectrometry, yeast two-hybrid, protein cofractionation, and affinity purification mass spectrometry (AP-MS), detection of PPIs can uncover novel biological inferences at a high-throughput. Thus new platforms to provide community access to large data sets are necessary. To this end, we have developed a web application that enables exploration and dissemination of the growing BioPlex interaction network. BioPlex is a large-scale interactome data set based on AP-MS of baits from the human ORFeome. The latest BioPlex data set release (BioPlex 2.0) contains 56 553 interactions from 5891 AP-MS experiments. To improve community access to this vast compendium of interactions, we developed BioPlex Display, which integrates individual protein querying, access to empirical data, and on-the-fly annotation of networks within an easy-to-use and mobile web application. BioPlex Display enables rapid acquisition of data from BioPlex and development of hypotheses based on protein interactions.

  4. Protein Charge and Mass Contribute to the Spatio-temporal Dynamics of Protein-Protein Interactions in a Minimal Proteome

    Science.gov (United States)

    Xu, Yu; Wang, Hong; Nussinov, Ruth; Ma, Buyong

    2013-01-01

    We constructed and simulated a ‘minimal proteome’ model using Langevin dynamics. It contains 206 essential protein types which were compiled from the literature. For comparison, we generated six proteomes with randomized concentrations. We found that the net charges and molecular weights of the proteins in the minimal genome are not random. The net charge of a protein decreases linearly with molecular weight, with small proteins being mostly positively charged and large proteins negatively charged. The protein copy numbers in the minimal genome have the tendency to maximize the number of protein-protein interactions in the network. Negatively charged proteins which tend to have larger sizes can provide large collision cross-section allowing them to interact with other proteins; on the other hand, the smaller positively charged proteins could have higher diffusion speed and are more likely to collide with other proteins. Proteomes with random charge/mass populations form less stable clusters than those with experimental protein copy numbers. Our study suggests that ‘proper’ populations of negatively and positively charged proteins are important for maintaining a protein-protein interaction network in a proteome. It is interesting to note that the minimal genome model based on the charge and mass of E. Coli may have a larger protein-protein interaction network than that based on the lower organism M. pneumoniae. PMID:23420643

  5. The effects of non-synonymous single nucleotide polymorphisms (nsSNPs) on protein-protein interactions.

    Science.gov (United States)

    Yates, Christopher M; Sternberg, Michael J E

    2013-11-01

    Non-synonymous single nucleotide polymorphisms (nsSNPs) are single base changes leading to a change to the amino acid sequence of the encoded protein. Many of these variants are associated with disease, so nsSNPs have been well studied, with studies looking at the effects of nsSNPs on individual proteins, for example, on stability and enzyme active sites. In recent years, the impact of nsSNPs upon protein-protein interactions has also been investigated, giving a greater insight into the mechanisms by which nsSNPs can lead to disease. In this review, we summarize these studies, looking at the various mechanisms by which nsSNPs can affect protein-protein interactions. We focus on structural changes that can impair interaction, changes to disorder, gain of interaction, and post-translational modifications before looking at some examples of nsSNPs at human-pathogen protein-protein interfaces and the analysis of nsSNPs from a network perspective. © 2013.

  6. HitPredict version 4: comprehensive reliability scoring of physical protein-protein interactions from more than 100 species.

    Science.gov (United States)

    López, Yosvany; Nakai, Kenta; Patil, Ashwini

    2015-01-01

    HitPredict is a consolidated resource of experimentally identified, physical protein-protein interactions with confidence scores to indicate their reliability. The study of genes and their inter-relationships using methods such as network and pathway analysis requires high quality protein-protein interaction information. Extracting reliable interactions from most of the existing databases is challenging because they either contain only a subset of the available interactions, or a mixture of physical, genetic and predicted interactions. Automated integration of interactions is further complicated by varying levels of accuracy of database content and lack of adherence to standard formats. To address these issues, the latest version of HitPredict provides a manually curated dataset of 398 696 physical associations between 70 808 proteins from 105 species. Manual confirmation was used to resolve all issues encountered during data integration. For improved reliability assessment, this version combines a new score derived from the experimental information of the interactions with the original score based on the features of the interacting proteins. The combined interaction score performs better than either of the individual scores in HitPredict as well as the reliability score of another similar database. HitPredict provides a web interface to search proteins and visualize their interactions, and the data can be downloaded for offline analysis. Data usability has been enhanced by mapping protein identifiers across multiple reference databases. Thus, the latest version of HitPredict provides a significantly larger, more reliable and usable dataset of protein-protein interactions from several species for the study of gene groups. Database URL: http://hintdb.hgc.jp/htp. © The Author(s) 2015. Published by Oxford University Press.

  7. Strongly interacting light dark matter

    International Nuclear Information System (INIS)

    Bruggisser, Sebastian; Riva, Francesco; Urbano, Alfredo

    2016-07-01

    In the presence of approximate global symmetries that forbid relevant interactions, strongly coupled light Dark Matter (DM) can appear weakly coupled at small-energy and generate a sizable relic abundance. Fundamental principles like unitarity restrict these symmetries to a small class, where the leading interactions are captured by effective operators up to dimension-8. Chiral symmetry, spontaneously broken global symmetries and non-linearly realized supersymmetry are examples of this. Their DM candidates (composite fermions, pseudo-Nambu-Goldstone Bosons and Goldstini) are interesting targets for LHC missing-energy searches.

  8. Scalar strong interaction hadron theory

    CERN Document Server

    Hoh, Fang Chao

    2015-01-01

    The scalar strong interaction hadron theory, SSI, is a first principles' and nonlocal theory at quantum mechanical level that provides an alternative to low energy QCD and Higgs related part of the standard model. The quark-quark interaction is scalar rather than color-vectorial. A set of equations of motion for mesons and another set for baryons have been constructed. This book provides an account of the present state of a theory supposedly still at its early stage of development. This work will facilitate researchers interested in entering into this field and serve as a basis for possible future development of this theory.

  9. Identification of polycystic ovary syndrome potential drug targets based on pathobiological similarity in the protein-protein interaction network

    OpenAIRE

    Huang, Hao; He, Yuehan; Li, Wan; Wei, Wenqing; Li, Yiran; Xie, Ruiqiang; Guo, Shanshan; Wang, Yahui; Jiang, Jing; Chen, Binbin; Lv, Junjie; Zhang, Nana; Chen, Lina; He, Weiming

    2016-01-01

    Polycystic ovary syndrome (PCOS) is one of the most common endocrinological disorders in reproductive aged women. PCOS and Type 2 Diabetes (T2D) are closely linked in multiple levels and possess high pathobiological similarity. Here, we put forward a new computational approach based on the pathobiological similarity to identify PCOS potential drug target modules (PPDT-Modules) and PCOS potential drug targets in the protein-protein interaction network (PPIN). From the systems level and biologi...

  10. A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data

    Directory of Open Access Journals (Sweden)

    Li Min

    2012-03-01

    Full Text Available Abstract Background Identification of essential proteins is always a challenging task since it requires experimental approaches that are time-consuming and laborious. With the advances in high throughput technologies, a large number of protein-protein interactions are available, which have produced unprecedented opportunities for detecting proteins' essentialities from the network level. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. However, the network topology-based centrality measures are very sensitive to the robustness of network. Therefore, a new robust essential protein discovery method would be of great value. Results In this paper, we propose a new centrality measure, named PeC, based on the integration of protein-protein interaction and gene expression data. The performance of PeC is validated based on the protein-protein interaction network of Saccharomyces cerevisiae. The experimental results show that the predicted precision of PeC clearly exceeds that of the other fifteen previously proposed centrality measures: Degree Centrality (DC, Betweenness Centrality (BC, Closeness Centrality (CC, Subgraph Centrality (SC, Eigenvector Centrality (EC, Information Centrality (IC, Bottle Neck (BN, Density of Maximum Neighborhood Component (DMNC, Local Average Connectivity-based method (LAC, Sum of ECC (SoECC, Range-Limited Centrality (RL, L-index (LI, Leader Rank (LR, Normalized α-Centrality (NC, and Moduland-Centrality (MC. Especially, the improvement of PeC over the classic centrality measures (BC, CC, SC, EC, and BN is more than 50% when predicting no more than 500 proteins. Conclusions We demonstrate that the integration of protein-protein interaction network and gene expression data can help improve the precision of predicting essential proteins. The new centrality measure, PeC, is an effective essential protein discovery method.

  11. Protein-protein interaction networks identify targets which rescue the MPP+ cellular model of Parkinson’s disease

    Science.gov (United States)

    Keane, Harriet; Ryan, Brent J.; Jackson, Brendan; Whitmore, Alan; Wade-Martins, Richard

    2015-11-01

    Neurodegenerative diseases are complex multifactorial disorders characterised by the interplay of many dysregulated physiological processes. As an exemplar, Parkinson’s disease (PD) involves multiple perturbed cellular functions, including mitochondrial dysfunction and autophagic dysregulation in preferentially-sensitive dopamine neurons, a selective pathophysiology recapitulated in vitro using the neurotoxin MPP+. Here we explore a network science approach for the selection of therapeutic protein targets in the cellular MPP+ model. We hypothesised that analysis of protein-protein interaction networks modelling MPP+ toxicity could identify proteins critical for mediating MPP+ toxicity. Analysis of protein-protein interaction networks constructed to model the interplay of mitochondrial dysfunction and autophagic dysregulation (key aspects of MPP+ toxicity) enabled us to identify four proteins predicted to be key for MPP+ toxicity (P62, GABARAP, GBRL1 and GBRL2). Combined, but not individual, knockdown of these proteins increased cellular susceptibility to MPP+ toxicity. Conversely, combined, but not individual, over-expression of the network targets provided rescue of MPP+ toxicity associated with the formation of autophagosome-like structures. We also found that modulation of two distinct proteins in the protein-protein interaction network was necessary and sufficient to mitigate neurotoxicity. Together, these findings validate our network science approach to multi-target identification in complex neurological diseases.

  12. Construction and analysis of protein-protein interaction network correlated with ankylosing spondylitis.

    Science.gov (United States)

    Kanwal, Attiya; Fazal, Sahar

    2018-01-05

    Ankylosing spondylitis, a systemic illness is a foundation of progressing joint swelling that for the most part influences the spine. However, it frequently causes aggravation in different joints far from the spine, and in addition organs, for example, the eyes, heart, lungs, and kidneys. It's an immune system ailment that may be activated by specific sorts of bacterial or viral diseases that initiate an invulnerable reaction that don't close off after the contamination is recuperated. The particular reason for ankylosing spondylitis is obscure, yet hereditary qualities assume a huge part in this condition. The rising apparatuses of network medicine offer a stage to investigate an unpredictable illness at framework level. In this study, we meant to recognize the key proteins and the biological regulator pathways including in AS and further investigating the molecular connectivity between these pathways by the topological examination of the Protein-protein communication (PPI) system. The extended network including of 93 nodes and have 199 interactions respectively scanned from STRING database and some separated small networks. 24 proteins with high BC at the threshold of 0.01 and 55 proteins with large degree at the threshold of 1 have been identified. CD4 with highest BC and Closeness centrality located in the centre of the network. The backbone network derived from high BC proteins presents a clear and visual overview which shows all important regulatory pathways for AS and the crosstalk between them. The finding of this research suggests that AS variation is orchestrated by an integrated PPI network centered on CD4 out of 93 nodes. Ankylosing spondylitis, a systemic disease is an establishment of advancing joint swelling that generally impacts the spine. Be that as it may, it as often as possible causes disturbance in various joints a long way from the spine, and what's more organs. It's a resistant framework affliction that might be actuated by particular sorts

  13. PPI-IRO: A two-stage method for protein-protein interaction extraction based on interaction relation ontology

    KAUST Repository

    Li, Chuanxi

    2014-01-01

    Mining Protein-Protein Interactions (PPIs) from the fast-growing biomedical literature resources has been proven as an effective approach for the identifi cation of biological regulatory networks. This paper presents a novel method based on the idea of Interaction Relation Ontology (IRO), which specifi es and organises words of various proteins interaction relationships. Our method is a two-stage PPI extraction method. At fi rst, IRO is applied in a binary classifi er to determine whether sentences contain a relation or not. Then, IRO is taken to guide PPI extraction by building sentence dependency parse tree. Comprehensive and quantitative evaluations and detailed analyses are used to demonstrate the signifi cant performance of IRO on relation sentences classifi cation and PPI extraction. Our PPI extraction method yielded a recall of around 80% and 90% and an F1 of around 54% and 66% on corpora of AIMed and Bioinfer, respectively, which are superior to most existing extraction methods. Copyright © 2014 Inderscience Enterprises Ltd.

  14. Stapled Voltage-Gated Calcium Channel (CaV) α-Interaction Domain (AID) Peptides Act As Selective Protein-Protein Interaction Inhibitors of CaV Function.

    Science.gov (United States)

    Findeisen, Felix; Campiglio, Marta; Jo, Hyunil; Abderemane-Ali, Fayal; Rumpf, Christine H; Pope, Lianne; Rossen, Nathan D; Flucher, Bernhard E; DeGrado, William F; Minor, Daniel L

    2017-06-21

    For many voltage-gated ion channels (VGICs), creation of a properly functioning ion channel requires the formation of specific protein-protein interactions between the transmembrane pore-forming subunits and cystoplasmic accessory subunits. Despite the importance of such protein-protein interactions in VGIC function and assembly, their potential as sites for VGIC modulator development has been largely overlooked. Here, we develop meta-xylyl (m-xylyl) stapled peptides that target a prototypic VGIC high affinity protein-protein interaction, the interaction between the voltage-gated calcium channel (Ca V ) pore-forming subunit α-interaction domain (AID) and cytoplasmic β-subunit (Ca V β). We show using circular dichroism spectroscopy, X-ray crystallography, and isothermal titration calorimetry that the m-xylyl staples enhance AID helix formation are structurally compatible with native-like AID:Ca V β interactions and reduce the entropic penalty associated with AID binding to Ca V β. Importantly, electrophysiological studies reveal that stapled AID peptides act as effective inhibitors of the Ca V α 1 :Ca V β interaction that modulate Ca V function in an Ca V β isoform-selective manner. Together, our studies provide a proof-of-concept demonstration of the use of protein-protein interaction inhibitors to control VGIC function and point to strategies for improved AID-based Ca V modulator design.

  15. Protein-protein interactions within the ensemble, eukaryotic V-ATPase, and its concerted interactions with cellular machineries.

    Science.gov (United States)

    Balakrishna, Asha Manikkoth; Manimekalai, Malathy Sony Subramanian; Grüber, Gerhard

    2015-10-01

    The V1VO-ATPase (V-ATPase) is the important proton-pump in eukaryotic cells, responsible for pH-homeostasis, pH-sensing and amino acid sensing, and therefore essential for cell growths and metabolism. ATP-cleavage in the catalytic A3B3-hexamer of V1 has to be communicated via several so-called central and peripheral stalk units to the proton-pumping VO-part, which is membrane-embedded. A unique feature of V1VO-ATPase regulation is its reversible disassembly of the V1 and VO domain. Actin provides a network to hold the V1 in proximity to the VO, enabling effective V1VO-assembly to occur. Besides binding to actin, the 14-subunit V-ATPase interacts with multi-subunit machineries to form cellular sensors, which regulate the pH in cellular compartments or amino acid signaling in lysosomes. Here we describe a variety of subunit-subunit interactions within the V-ATPase enzyme during catalysis and its protein-protein assembling with key cellular machineries, essential for cellular function. Copyright © 2015 Elsevier Ltd. All rights reserved.

  16. Effective lagrangian for strong interactions

    International Nuclear Information System (INIS)

    Jain, P.

    1988-01-01

    We attempt to construct a realistic phenomenological Lagrangian in order to describe strong interactions. This is in general a very complicated problem and we shall explore its various aspects. We first include the vector mesons by writing down the most general chiral invariant terms proportional to the Levi-Civita symbol ε μναβ . These terms involve three unknown coefficients, which are calculated by using the experimental results of strong interaction processes. We then calculate the static nucleon properties by finding the solitonic excitations of this model. The results turn out to be, as is also the case for most other vector-pseudoscalar Lagrangians, better than the Skyrme model but are still somewhat different from the experiments. Another aspect that we shall study is the incorporation of scale anomaly of QCD into the Skyrme model. We thus introduce a scalar glueball in our Lagrangian. Here we find an interesting result that the effective glue field dynamically forms a bag for the soliton. Depending on the values of the parameters, we get either a deep bag or a shallow bag. However by including the scalar meson, we find that to get realistic scalar sector we must have the shallow bag. Finally we show some intriguing connections between the chiral quark model, in which the nucleon is described as a solitonic excitation, and the ordinary potential binding quark model

  17. A Library of Plasmodium vivax Recombinant Merozoite Proteins Reveals New Vaccine Candidates and Protein-Protein Interactions

    Science.gov (United States)

    Hostetler, Jessica B.; Sharma, Sumana; Bartholdson, S. Josefin; Wright, Gavin J.; Fairhurst, Rick M.; Rayner, Julian C.

    2015-01-01

    Background A vaccine targeting Plasmodium vivax will be an essential component of any comprehensive malaria elimination program, but major gaps in our understanding of P. vivax biology, including the protein-protein interactions that mediate merozoite invasion of reticulocytes, hinder the search for candidate antigens. Only one ligand-receptor interaction has been identified, that between P. vivax Duffy Binding Protein (PvDBP) and the erythrocyte Duffy Antigen Receptor for Chemokines (DARC), and strain-specific immune responses to PvDBP make it a complex vaccine target. To broaden the repertoire of potential P. vivax merozoite-stage vaccine targets, we exploited a recent breakthrough in expressing full-length ectodomains of Plasmodium proteins in a functionally-active form in mammalian cells and initiated a large-scale study of P. vivax merozoite proteins that are potentially involved in reticulocyte binding and invasion. Methodology/Principal Findings We selected 39 P. vivax proteins that are predicted to localize to the merozoite surface or invasive secretory organelles, some of which show homology to P. falciparum vaccine candidates. Of these, we were able to express 37 full-length protein ectodomains in a mammalian expression system, which has been previously used to express P. falciparum invasion ligands such as PfRH5. To establish whether the expressed proteins were correctly folded, we assessed whether they were recognized by antibodies from Cambodian patients with acute vivax malaria. IgG from these samples showed at least a two-fold change in reactivity over naïve controls in 27 of 34 antigens tested, and the majority showed heat-labile IgG immunoreactivity, suggesting the presence of conformation-sensitive epitopes and native tertiary protein structures. Using a method specifically designed to detect low-affinity, extracellular protein-protein interactions, we confirmed a predicted interaction between P. vivax 6-cysteine proteins P12 and P41, further

  18. Toward a rigorous network of protein-protein interactions of the model sulfate reducer Desulfovibrio vulgaris Hildenborough

    Energy Technology Data Exchange (ETDEWEB)

    Chhabra, S.R.; Joachimiak, M.P.; Petzold, C.J.; Zane, G.M.; Price, M.N.; Gaucher, S.; Reveco, S.A.; Fok, V.; Johanson, A.R.; Batth, T.S.; Singer, M.; Chandonia, J.M.; Joyner, D.; Hazen, T.C.; Arkin, A.P.; Wall, J.D.; Singh, A.K.; Keasling, J.D.

    2011-05-01

    Protein–protein interactions offer an insight into cellular processes beyond what may be obtained by the quantitative functional genomics tools of proteomics and transcriptomics. The aforementioned tools have been extensively applied to study E. coli and other aerobes and more recently to study the stress response behavior of Desulfovibrio 5 vulgaris Hildenborough, a model anaerobe and sulfate reducer. In this paper we present the first attempt to identify protein-protein interactions in an obligate anaerobic bacterium. We used suicide vector-assisted chromosomal modification of 12 open reading frames encoded by this sulfate reducer to append an eight amino acid affinity tag to the carboxy-terminus of the chosen proteins. Three biological replicates of the 10 ‘pulled-down’ proteins were separated and analyzed using liquid chromatography-mass spectrometry. Replicate agreement ranged between 35% and 69%. An interaction network among 12 bait and 90 prey proteins was reconstructed based on 134 bait-prey interactions computationally identified to be of high confidence. We discuss the biological significance of several unique metabolic features of D. vulgaris revealed by this protein-protein interaction data 15 and protein modifications that were observed. These include the distinct role of the putative carbon monoxide-induced hydrogenase, unique electron transfer routes associated with different oxidoreductases, and the possible role of methylation in regulating sulfate reduction.

  19. Genome-wide analysis of protein-protein interactions and involvement of viral proteins in SARS-CoV replication.

    Directory of Open Access Journals (Sweden)

    Ji'an Pan

    Full Text Available Analyses of viral protein-protein interactions are an important step to understand viral protein functions and their underlying molecular mechanisms. In this study, we adopted a mammalian two-hybrid system to screen the genome-wide intraviral protein-protein interactions of SARS coronavirus (SARS-CoV and therefrom revealed a number of novel interactions which could be partly confirmed by in vitro biochemical assays. Three pairs of the interactions identified were detected in both directions: non-structural protein (nsp 10 and nsp14, nsp10 and nsp16, and nsp7 and nsp8. The interactions between the multifunctional nsp10 and nsp14 or nsp16, which are the unique proteins found in the members of Nidovirales with large RNA genomes including coronaviruses and toroviruses, may have important implication for the mechanisms of replication/transcription complex assembly and functions of these viruses. Using a SARS-CoV replicon expressing a luciferase reporter under the control of a transcription regulating sequence, it has been shown that several viral proteins (N, X and SUD domains of nsp3, and nsp12 provided in trans stimulated the replicon reporter activity, indicating that these proteins may regulate coronavirus replication and transcription. Collectively, our findings provide a basis and platform for further characterization of the functions and mechanisms of coronavirus proteins.

  20. Insight into bacterial virulence mechanisms against host immune response via the Yersinia pestis-human protein-protein interaction network.

    Science.gov (United States)

    Yang, Huiying; Ke, Yuehua; Wang, Jian; Tan, Yafang; Myeni, Sebenzile K; Li, Dong; Shi, Qinghai; Yan, Yanfeng; Chen, Hui; Guo, Zhaobiao; Yuan, Yanzhi; Yang, Xiaoming; Yang, Ruifu; Du, Zongmin

    2011-11-01

    A Yersinia pestis-human protein interaction network is reported here to improve our understanding of its pathogenesis. Up to 204 interactions between 66 Y. pestis bait proteins and 109 human proteins were identified by yeast two-hybrid assay and then combined with 23 previously published interactions to construct a protein-protein interaction network. Topological analysis of the interaction network revealed that human proteins targeted by Y. pestis were significantly enriched in the proteins that are central in the human protein-protein interaction network. Analysis of this network showed that signaling pathways important for host immune responses were preferentially targeted by Y. pestis, including the pathways involved in focal adhesion, regulation of cytoskeleton, leukocyte transendoepithelial migration, and Toll-like receptor (TLR) and mitogen-activated protein kinase (MAPK) signaling. Cellular pathways targeted by Y. pestis are highly relevant to its pathogenesis. Interactions with host proteins involved in focal adhesion and cytoskeketon regulation pathways could account for resistance of Y. pestis to phagocytosis. Interference with TLR and MAPK signaling pathways by Y. pestis reflects common characteristics of pathogen-host interaction that bacterial pathogens have evolved to evade host innate immune response by interacting with proteins in those signaling pathways. Interestingly, a large portion of human proteins interacting with Y. pestis (16/109) also interacted with viral proteins (Epstein-Barr virus [EBV] and hepatitis C virus [HCV]), suggesting that viral and bacterial pathogens attack common cellular functions to facilitate infections. In addition, we identified vasodilator-stimulated phosphoprotein (VASP) as a novel interaction partner of YpkA and showed that YpkA could inhibit in vitro actin assembly mediated by VASP.

  1. Strongly interacting W's and Z's

    International Nuclear Information System (INIS)

    Gaillard, M.K.

    1984-01-01

    The study focussed primarily on the dynamics of a strongly interacting W, Z(SIW) sector, with the aim of sharpening predictions for total W, Z yield and W, Z multiplicities expected from WW fusion for various scenarios. Specific issues raised in the context of the general problem of modeling SIW included the specificity of the technicolor (or, equivalently, QCD) model, whether or not a composite scalar model can be evaded, and whether the standard model necessarily implies an I = J = O state (≅ Higgs particle) that is relatively ''light'' (M ≤ hundreds of TeV). The consensus on the last issue was that existing arguments are inconclusive. While the author shall briefly address compositeness and alternatives to the technicolor model, quantitative estimates will be of necessity based on technicolor or an extrapolation of pion data

  2. Fragment-Based Protein-Protein Interaction Antagonists of a Viral Dimeric Protease.

    Science.gov (United States)

    Gable, Jonathan E; Lee, Gregory M; Acker, Timothy M; Hulce, Kaitlin R; Gonzalez, Eric R; Schweigler, Patrick; Melkko, Samu; Farady, Christopher J; Craik, Charles S

    2016-04-19

    Fragment-based drug discovery has shown promise as an approach for challenging targets such as protein-protein interfaces. We developed and applied an activity-based fragment screen against dimeric Kaposi's sarcoma-associated herpesvirus protease (KSHV Pr) using an optimized fluorogenic substrate. Dose-response determination was performed as a confirmation screen, and NMR spectroscopy was used to map fragment inhibitor binding to KSHV Pr. Kinetic assays demonstrated that several initial hits also inhibit human cytomegalovirus protease (HCMV Pr). Binding of these hits to HCMV Pr was also confirmed by NMR spectroscopy. Despite the use of a target-agnostic fragment library, more than 80 % of confirmed hits disrupted dimerization and bound to a previously reported pocket at the dimer interface of KSHV Pr, not to the active site. One class of fragments, an aminothiazole scaffold, was further explored using commercially available analogues. These compounds demonstrated greater than 100-fold improvement of inhibition. This study illustrates the power of fragment-based screening for these challenging enzymatic targets and provides an example of the potential druggability of pockets at protein-protein interfaces. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  3. Structural deformation upon protein-protein interaction: a structural alphabet approach.

    Science.gov (United States)

    Martin, Juliette; Regad, Leslie; Lecornet, Hélène; Camproux, Anne-Claude

    2008-02-28

    In a number of protein-protein complexes, the 3D structures of bound and unbound partners significantly differ, supporting the induced fit hypothesis for protein-protein binding. In this study, we explore the induced fit modifications on a set of 124 proteins available in both bound and unbound forms, in terms of local structure. The local structure is described thanks to a structural alphabet of 27 structural letters that allows a detailed description of the backbone. Using a control set to distinguish induced fit from experimental error and natural protein flexibility, we show that the fraction of structural letters modified upon binding is significantly greater than in the control set (36% versus 28%). This proportion is even greater in the interface regions (41%). Interface regions preferentially involve coils. Our analysis further reveals that some structural letters in coil are not favored in the interface. We show that certain structural letters in coil are particularly subject to modifications at the interface, and that the severity of structural change also varies. These information are used to derive a structural letter substitution matrix that summarizes the local structural changes observed in our data set. We also illustrate the usefulness of our approach to identify common binding motifs in unrelated proteins. Our study provides qualitative information about induced fit. These results could be of help for flexible docking.

  4. Structural deformation upon protein-protein interaction: A structural alphabet approach

    Directory of Open Access Journals (Sweden)

    Lecornet Hélène

    2008-02-01

    Full Text Available Abstract Background In a number of protein-protein complexes, the 3D structures of bound and unbound partners significantly differ, supporting the induced fit hypothesis for protein-protein binding. Results In this study, we explore the induced fit modifications on a set of 124 proteins available in both bound and unbound forms, in terms of local structure. The local structure is described thanks to a structural alphabet of 27 structural letters that allows a detailed description of the backbone. Using a control set to distinguish induced fit from experimental error and natural protein flexibility, we show that the fraction of structural letters modified upon binding is significantly greater than in the control set (36% versus 28%. This proportion is even greater in the interface regions (41%. Interface regions preferentially involve coils. Our analysis further reveals that some structural letters in coil are not favored in the interface. We show that certain structural letters in coil are particularly subject to modifications at the interface, and that the severity of structural change also varies. These information are used to derive a structural letter substitution matrix that summarizes the local structural changes observed in our data set. We also illustrate the usefulness of our approach to identify common binding motifs in unrelated proteins. Conclusion Our study provides qualitative information about induced fit. These results could be of help for flexible docking.

  5. Super-resolution imaging and tracking of protein-protein interactions in sub-diffraction cellular space

    Science.gov (United States)

    Liu, Zhen; Xing, Dong; Su, Qian Peter; Zhu, Yun; Zhang, Jiamei; Kong, Xinyu; Xue, Boxin; Wang, Sheng; Sun, Hao; Tao, Yile; Sun, Yujie

    2014-07-01

    Imaging the location and dynamics of individual interacting protein pairs is essential but often difficult because of the fluorescent background from other paired and non-paired molecules, particularly in the sub-diffraction cellular space. Here we develop a new method combining bimolecular fluorescence complementation and photoactivated localization microscopy for super-resolution imaging and single-molecule tracking of specific protein-protein interactions. The method is used to study the interaction of two abundant proteins, MreB and EF-Tu, in Escherichia coli cells. The super-resolution imaging shows interesting distribution and domain sizes of interacting MreB-EF-Tu pairs as a subpopulation of total EF-Tu. The single-molecule tracking of MreB, EF-Tu and MreB-EF-Tu pairs reveals intriguing localization-dependent heterogonous dynamics and provides valuable insights to understanding the roles of MreB-EF-Tu interactions.

  6. Identification of novel direct protein-protein interactions by irradiating living cells with femtosecond UV laser pulses.

    Science.gov (United States)

    Itri, Francesco; Monti, Daria Maria; Chino, Marco; Vinciguerra, Roberto; Altucci, Carlo; Lombardi, Angela; Piccoli, Renata; Birolo, Leila; Arciello, Angela

    2017-10-07

    The identification of protein-protein interaction networks in living cells is becoming increasingly fundamental to elucidate main biological processes and to understand disease molecular bases on a system-wide level. We recently described a method (LUCK, Laser UV Cross-linKing) to cross-link interacting protein surfaces in living cells by UV laser irradiation. By using this innovative methodology, that does not require any protein modification or cell engineering, here we demonstrate that, upon UV laser irradiation of HeLa cells, a direct interaction between GAPDH and alpha-enolase was "frozen" by a cross-linking event. We validated the occurrence of this direct interaction by co-immunoprecipitation and Immuno-FRET analyses. This represents a proof of principle of the LUCK capability to reveal direct protein interactions in their physiological environment. Copyright © 2017 Elsevier Inc. All rights reserved.

  7. Prediction of heterodimeric protein complexes from weighted protein-protein interaction networks using novel features and kernel functions.

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    Peiying Ruan

    Full Text Available Since many proteins express their functional activity by interacting with other proteins and forming protein complexes, it is very useful to identify sets of proteins that form complexes. For that purpose, many prediction methods for protein complexes from protein-protein interactions have been developed such as MCL, MCODE, RNSC, PCP, RRW, and NWE. These methods have dealt with only complexes with size of more than three because the methods often are based on some density of subgraphs. However, heterodimeric protein complexes that consist of two distinct proteins occupy a large part according to several comprehensive databases of known complexes. In this paper, we propose several feature space mappings from protein-protein interaction data, in which each interaction is weighted based on reliability. Furthermore, we make use of prior knowledge on protein domains to develop feature space mappings, domain composition kernel and its combination kernel with our proposed features. We perform ten-fold cross-validation computational experiments. These results suggest that our proposed kernel considerably outperforms the naive Bayes-based method, which is the best existing method for predicting heterodimeric protein complexes.

  8. Prediction of protein-protein interactions in dengue virus coat proteins guided by low resolution cryoEM structures

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    Srinivasan Narayanaswamy

    2010-06-01

    Full Text Available Abstract Background Dengue virus along with the other members of the flaviviridae family has reemerged as deadly human pathogens. Understanding the mechanistic details of these infections can be highly rewarding in developing effective antivirals. During maturation of the virus inside the host cell, the coat proteins E and M undergo conformational changes, altering the morphology of the viral coat. However, due to low resolution nature of the available 3-D structures of viral assemblies, the atomic details of these changes are still elusive. Results In the present analysis, starting from Cα positions of low resolution cryo electron microscopic structures the residue level details of protein-protein interaction interfaces of dengue virus coat proteins have been predicted. By comparing the preexisting structures of virus in different phases of life cycle, the changes taking place in these predicted protein-protein interaction interfaces were followed as a function of maturation process of the virus. Besides changing the current notion about the presence of only homodimers in the mature viral coat, the present analysis indicated presence of a proline-rich motif at the protein-protein interaction interface of the coat protein. Investigating the conservation status of these seemingly functionally crucial residues across other members of flaviviridae family enabled dissecting common mechanisms used for infections by these viruses. Conclusions Thus, using computational approach the present analysis has provided better insights into the preexisting low resolution structures of virus assemblies, the findings of which can be made use of in designing effective antivirals against these deadly human pathogens.

  9. GRIP: A web-based system for constructing Gold Standard datasets for protein-protein interaction prediction

    Directory of Open Access Journals (Sweden)

    Zheng Huiru

    2009-01-01

    Full Text Available Abstract Background Information about protein interaction networks is fundamental to understanding protein function and cellular processes. Interaction patterns among proteins can suggest new drug targets and aid in the design of new therapeutic interventions. Efforts have been made to map interactions on a proteomic-wide scale using both experimental and computational techniques. Reference datasets that contain known interacting proteins (positive cases and non-interacting proteins (negative cases are essential to support computational prediction and validation of protein-protein interactions. Information on known interacting and non interacting proteins are usually stored within databases. Extraction of these data can be both complex and time consuming. Although, the automatic construction of reference datasets for classification is a useful resource for researchers no public resource currently exists to perform this task. Results GRIP (Gold Reference dataset constructor from Information on Protein complexes is a web-based system that provides researchers with the functionality to create reference datasets for protein-protein interaction prediction in Saccharomyces cerevisiae. Both positive and negative cases for a reference dataset can be extracted, organised and downloaded by the user. GRIP also provides an upload facility whereby users can submit proteins to determine protein complex membership. A search facility is provided where a user can search for protein complex information in Saccharomyces cerevisiae. Conclusion GRIP is developed to retrieve information on protein complex, cellular localisation, and physical and genetic interactions in Saccharomyces cerevisiae. Manual construction of reference datasets can be a time consuming process requiring programming knowledge. GRIP simplifies and speeds up this process by allowing users to automatically construct reference datasets. GRIP is free to access at http://rosalind.infj.ulst.ac.uk/GRIP/.

  10. PIPE: a protein-protein interaction prediction engine based on the re-occurring short polypeptide sequences between known interacting protein pairs

    Directory of Open Access Journals (Sweden)

    Greenblatt Jack

    2006-07-01

    Full Text Available Abstract Background Identification of protein interaction networks has received considerable attention in the post-genomic era. The currently available biochemical approaches used to detect protein-protein interactions are all time and labour intensive. Consequently there is a growing need for the development of computational tools that are capable of effectively identifying such interactions. Results Here we explain the development and implementation of a novel Protein-Protein Interaction Prediction Engine termed PIPE. This tool is capable of predicting protein-protein interactions for any target pair of the yeast Saccharomyces cerevisiae proteins from their primary structure and without the need for any additional information or predictions about the proteins. PIPE showed a sensitivity of 61% for detecting any yeast protein interaction with 89% specificity and an overall accuracy of 75%. This rate of success is comparable to those associated with the most commonly used biochemical techniques. Using PIPE, we identified a novel interaction between YGL227W (vid30 and YMR135C (gid8 yeast proteins. This lead us to the identification of a novel yeast complex that here we term vid30 complex (vid30c. The observed interaction was confirmed by tandem affinity purification (TAP tag, verifying the ability of PIPE to predict novel protein-protein interactions. We then used PIPE analysis to investigate the internal architecture of vid30c. It appeared from PIPE analysis that vid30c may consist of a core and a secondary component. Generation of yeast gene deletion strains combined with TAP tagging analysis indicated that the deletion of a member of the core component interfered with the formation of vid30c, however, deletion of a member of the secondary component had little effect (if any on the formation of vid30c. Also, PIPE can be used to analyse yeast proteins for which TAP tagging fails, thereby allowing us to predict protein interactions that are not

  11. Substantial conformational change mediated by charge-triad residues of the death effector domain in protein-protein interactions.

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    Edward C Twomey

    Full Text Available Protein conformational changes are commonly associated with the formation of protein complexes. The non-catalytic death effector domains (DEDs mediate protein-protein interactions in a variety of cellular processes, including apoptosis, proliferation and migration, and glucose metabolism. Here, using NMR residual dipolar coupling (RDC data, we report a conformational change in the DED of the phosphoprotein enriched in astrocytes, 15 kDa (PEA-15 protein in the complex with a mitogen-activated protein (MAP kinase, extracellular regulated kinase 2 (ERK2, which is essential in regulating ERK2 cellular distribution and function in cell proliferation and migration. The most significant conformational change in PEA-15 happens at helices α2, α3, and α4, which also possess the highest flexibility among the six-helix bundle of the DED. This crucial conformational change is modulated by the D/E-RxDL charge-triad motif, one of the prominent structural features of DEDs, together with a number of other electrostatic and hydrogen bonding interactions on the protein surface. Charge-triad motif promotes the optimal orientation of key residues and expands the binding interface to accommodate protein-protein interactions. However, the charge-triad residues are not directly involved in the binding interface between PEA-15 and ERK2.

  12. InSilico Proteomics System: Integration and Application of Protein and Protein-Protein Interaction Data using Microsoft .NET

    Directory of Open Access Journals (Sweden)

    Straßer Wolfgang

    2006-12-01

    Full Text Available In the last decades, biological databases became the major knowledge resource for researchers in the field of molecular biology. The distribution of information among these databases is one of the major problems. An overview about the subject area of data access and representation of protein and protein-protein interaction data within public biological databases is described. For a comprehensive and consistent way of searching and analysing integrated protein and protein-protein interaction data, the InSilico Proteomics (ISP project has been initiated. Its three main objectives are (1 to provide an integrated knowledge pool for data investigation and global network analysis functions for a better understanding of a cell’s interactome, (2 employment of public data for plausibility analysis and validation of in-house experimental data and (3 testing the applicability of Microsoft’s .NET architecture for bioinformatics applications. Data integrated into the ISP database can be queried through the Web portal PRIMOS (PRotein Interaction and MOlecule Search which is freely available at http://biomis.fh-hagenberg.at/isp/primos.

  13. Surfing the Protein-Protein Interaction Surface Using Docking Methods: Application to the Design of PPI Inhibitors

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    Rushikesh Sable

    2015-06-01

    Full Text Available Blocking protein-protein interactions (PPI using small molecules or peptides modulates biochemical pathways and has therapeutic significance. PPI inhibition for designing drug-like molecules is a new area that has been explored extensively during the last decade. Considering the number of available PPI inhibitor databases and the limited number of 3D structures available for proteins, docking and scoring methods play a major role in designing PPI inhibitors as well as stabilizers. Docking methods are used in the design of PPI inhibitors at several stages of finding a lead compound, including modeling the protein complex, screening for hot spots on the protein-protein interaction interface and screening small molecules or peptides that bind to the PPI interface. There are three major challenges to the use of docking on the relatively flat surfaces of PPI. In this review we will provide some examples of the use of docking in PPI inhibitor design as well as its limitations. The combination of experimental and docking methods with improved scoring function has thus far resulted in few success stories of PPI inhibitors for therapeutic purposes. Docking algorithms used for PPI are in the early stages, however, and as more data are available docking will become a highly promising area in the design of PPI inhibitors or stabilizers.

  14. Surfing the Protein-Protein Interaction Surface Using Docking Methods: Application to the Design of PPI Inhibitors.

    Science.gov (United States)

    Sable, Rushikesh; Jois, Seetharama

    2015-06-23

    Blocking protein-protein interactions (PPI) using small molecules or peptides modulates biochemical pathways and has therapeutic significance. PPI inhibition for designing drug-like molecules is a new area that has been explored extensively during the last decade. Considering the number of available PPI inhibitor databases and the limited number of 3D structures available for proteins, docking and scoring methods play a major role in designing PPI inhibitors as well as stabilizers. Docking methods are used in the design of PPI inhibitors at several stages of finding a lead compound, including modeling the protein complex, screening for hot spots on the protein-protein interaction interface and screening small molecules or peptides that bind to the PPI interface. There are three major challenges to the use of docking on the relatively flat surfaces of PPI. In this review we will provide some examples of the use of docking in PPI inhibitor design as well as its limitations. The combination of experimental and docking methods with improved scoring function has thus far resulted in few success stories of PPI inhibitors for therapeutic purposes. Docking algorithms used for PPI are in the early stages, however, and as more data are available docking will become a highly promising area in the design of PPI inhibitors or stabilizers.

  15. MOLECULAR DOCKING AND DYNAMICS STUDIES ON THE PROTEIN-PROTEIN INTERACTIONS OF ELECTRICALLY ACTIVE PILIN NANOWIRES OF GEOBACTER SULFURREDUCENS.

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    D. Jeya Sundara Sharmila1 *

    2017-06-01

    Full Text Available Molecular interactions are key aspects in biological recognitions applicable in nano/micro systems. Bacterial nanowires are pilus filament based structures that can conduct electrons. The transport of electron is proposed to be facilitated by filamentous fibers made up of polymeric assemblies of proteins called pilin. Geobacter sulfurreducens is capable of delivering electrons through extracellular electron transport (EET by employing conductive nanowires, which are pilin proteins composed of type IV subunit PilA. Protein-protein interactions play an important role in the stabilization of the pilin nanowire assembly complex and it contains transmembrane (TM domain. In current study, protein-protein docking and multiple molecular dynamic (MD simulations were performed to understand the binding mode of pilin nanowires. The MD result explains the conformational behavior and folding of pilin nanowires in water environment in different time scale duration 20, 5, 5, 10 and 20ns (total of 60ns. Direct hydrogen bonds and water mediated hydrogen bonds that play a crucial role during the simulation were investigated. The conformational state, folding, end-toend distance profile and hydrogen bonding behavior had indicated that the Geobacter sulfurreducens pilin nanowires have electrical conductivity properties.

  16. Adding biological meaning to human protein-protein interactions identified by yeast two-hybrid screenings: A guide through bioinformatics tools.

    Science.gov (United States)

    Felgueiras, Juliana; Silva, Joana Vieira; Fardilha, Margarida

    2018-01-16

    "A man is known by the company he keeps" is a popular expression that perfectly fits proteins. A common approach to characterize the function of a target protein is to identify its interacting partners and thus infer its roles based on the known functions of the interactors. Protein-protein interaction networks (PPINs) have been created for several organisms, including humans, primarily as results of high-throughput screenings, such as yeast two-hybrid (Y2H). Their unequivocal use to understand events underlying human pathophysiology is promising in identifying genes and proteins associated with diseases. Therefore, numerous opportunities have emerged for PPINs as tools for clinical management of diseases: network-based disease classification systems, discovery of biomarkers and identification of therapeutic targets. Despite the great advantages of PPINs, their use is still unrecognised by several researchers who generate high-throughput data to generally characterize interactions in a certain model or to select an interaction to study in detail. We strongly believe that both approaches are not exclusive and that we can use PPINs as a complementary methodology and rich-source of information to the initial study proposal. Here, we suggest a pipeline to deal with Y2H results using bioinformatics tools freely available for academics. Yeast two-hybrid is widely-used to identify protein-protein interactions. Conventionally, the positive clones that result from a yeast two-hybrid screening are sequenced to identify the interactors of the protein of interest (also known as bait protein), and few interactions, thought as potentially relevant for the model in study, are selected for further validation using biochemical methods (e.g. co-immunoprecipitation and co-localization). The huge amount of data that is potentially lost during this conservative approach motivated us to write this tutorial-like review, so that researchers feel encouraged to take advantage of

  17. Strongly interacting photons and atoms

    International Nuclear Information System (INIS)

    Alge, W.

    1999-05-01

    This thesis contains the main results of the research topics I have pursued during the my PhD studies at the University of Innsbruck and partly in collaboration with the Institut d' Optique in Orsay, France. It is divided into three parts. The first and largest part discusses the possibility of using strong standing waves as a tool to cool and trap neutral atoms in optical cavities. This is very important in the field of nonlinear optics where several successful experiments with cold atoms in cavities have been performed recently. A discussion of the optical parametric oscillator in a regime where the nonlinearity dominates the evolution is the topic of the second part. We investigated mainly the statistical properties of the cavity output of the three interactive cavity modes. Very recently a system has been proposed which promises fantastic properties. It should exhibit a giant Kerr nonlinearity with negligible absorption thus leading to a photonic turnstile device based on cold atoms in cavity. We have shown that this model suffers from overly simplistic assumptions and developed several more comprehensive approaches to study the behavior of this system. Apart from the division into three parts of different contents the thesis is divided into publications, supplements and invisible stuff. The intention of the supplements is to reach researchers which work in related areas and provide them with more detailed information about the concepts and the numerical tools we used. It is written especially for diploma and PhD students to give them a chance to use the third part of our work which is actually the largest one. They consist of a large number of computer programs we wrote to investigate the behavior of the systems in parameter regions where no hope exists to solve the equations analytically. (author)

  18. Arabidopsis mRNA polyadenylation machinery: comprehensive analysis of protein-protein interactions and gene expression profiling

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    Mo Min

    2008-05-01

    Full Text Available Abstract Background The polyadenylation of mRNA is one of the critical processing steps during expression of almost all eukaryotic genes. It is tightly integrated with transcription, particularly its termination, as well as other RNA processing events, i.e. capping and splicing. The poly(A tail protects the mRNA from unregulated degradation, and it is required for nuclear export and translation initiation. In recent years, it has been demonstrated that the polyadenylation process is also involved in the regulation of gene expression. The polyadenylation process requires two components, the cis-elements on the mRNA and a group of protein factors that recognize the cis-elements and produce the poly(A tail. Here we report a comprehensive pairwise protein-protein interaction mapping and gene expression profiling of the mRNA polyadenylation protein machinery in Arabidopsis. Results By protein sequence homology search using human and yeast polyadenylation factors, we identified 28 proteins that may be components of Arabidopsis polyadenylation machinery. To elucidate the protein network and their functions, we first tested their protein-protein interaction profiles. Out of 320 pair-wise protein-protein interaction assays done using the yeast two-hybrid system, 56 (~17% showed positive interactions. 15 of these interactions were further tested, and all were confirmed by co-immunoprecipitation and/or in vitro co-purification. These interactions organize into three distinct hubs involving the Arabidopsis polyadenylation factors. These hubs are centered around AtCPSF100, AtCLPS, and AtFIPS. The first two are similar to complexes seen in mammals, while the third one stands out as unique to plants. When comparing the gene expression profiles extracted from publicly available microarray datasets, some of the polyadenylation related genes showed tissue-specific expression, suggestive of potential different polyadenylation complex configurations. Conclusion An

  19. DiffSLC: A graph centrality method to detect essential proteins of a protein-protein interaction network.

    Science.gov (United States)

    Mistry, Divya; Wise, Roger P; Dickerson, Julie A

    2017-01-01

    Identification of central genes and proteins in biomolecular networks provides credible candidates for pathway analysis, functional analysis, and essentiality prediction. The DiffSLC centrality measure predicts central and essential genes and proteins using a protein-protein interaction network. Network centrality measures prioritize nodes and edges based on their importance to the network topology. These measures helped identify critical genes and proteins in biomolecular networks. The proposed centrality measure, DiffSLC, combines the number of interactions of a protein and the gene coexpression values of genes from which those proteins were translated, as a weighting factor to bias the identification of essential proteins in a protein interaction network. Potentially essential proteins with low node degree are promoted through eigenvector centrality. Thus, the gene coexpression values are used in conjunction with the eigenvector of the network's adjacency matrix and edge clustering coefficient to improve essentiality prediction. The outcome of this prediction is shown using three variations: (1) inclusion or exclusion of gene co-expression data, (2) impact of different coexpression measures, and (3) impact of different gene expression data sets. For a total of seven networks, DiffSLC is compared to other centrality measures using Saccharomyces cerevisiae protein interaction networks and gene expression data. Comparisons are also performed for the top ranked proteins against the known essential genes from the Saccharomyces Gene Deletion Project, which show that DiffSLC detects more essential proteins and has a higher area under the ROC curve than other compared methods. This makes DiffSLC a stronger alternative to other centrality methods for detecting essential genes using a protein-protein interaction network that obeys centrality-lethality principle. DiffSLC is implemented using the igraph package in R, and networkx package in Python. The python package can be

  20. Analysis of hepatocellular carcinoma and metastatic hepatic carcinoma via functional modules in a protein-protein interaction network

    Directory of Open Access Journals (Sweden)

    Jun Pan

    2014-01-01

    Full Text Available Introduction: This study aims to identify protein clusters with potential functional relevance in the pathogenesis of hepatocellular carcinoma (HCC and metastatic hepatic carcinoma using network analysis. Materials and Methods: We used human protein interaction data to build a protein-protein interaction network with Cytoscape and then derived functional clusters using MCODE. Combining the gene expression profiles, we calculated the functional scores for the clusters and selected statistically significant clusters. Meanwhile, Gene Ontology was used to assess the functionality of these clusters. Finally, a support vector machine was trained on the gold standard data sets. Results: The differentially expressed genes of HCC were mainly involved in metabolic and signaling processes. We acquired 13 significant modules from the gene expression profiles. The area under the curve value based on the differentially expressed modules were 98.31%, which outweighed the classification with DEGs. Conclusions: Differentially expressed modules are valuable to screen biomarkers combined with functional modules.

  1. A Versatile Platform to Analyze Low-Affinity and Transient Protein-Protein Interactions in Living Cells in Real Time

    Directory of Open Access Journals (Sweden)

    Yao-Cheng Li

    2014-12-01

    Full Text Available Summary: Protein-protein interactions (PPIs play central roles in orchestrating biological processes. While some PPIs are stable, many important ones are transient and hard to detect with conventional approaches. We developed ReBiL, a recombinase enhanced bimolecular luciferase complementation platform, to enable detection of weak PPIs in living cells. ReBiL readily identified challenging transient interactions between an E3 ubiquitin ligase and an E2 ubiquitin-conjugating enzyme. ReBiL’s ability to rapidly interrogate PPIs in diverse conditions revealed that some stapled α-helical peptides, a class of PPI antagonists, induce target-independent cytosolic leakage and cytotoxicity that is antagonized by serum. These results explain the requirement for serum-free conditions to detect stapled peptide activity, and define a required parameter to evaluate for peptide antagonist approaches. ReBiL’s ability to expedite PPI analysis, assess target specificity and cell permeability, and reveal off-target effects of PPI modifiers should facilitate the development of effective, cell-permeable PPI therapeutics and the elaboration of diverse biological mechanisms. : Li et al. developed a recombinase-enhanced bimolecular luciferase complementation platform, termed ReBiL, to evaluate low-affinity protein-protein interactions (PPIs that are not detectable by other methods and to analyze PPI antagonists in living cells. ReBiL showed that small-molecule p53-Mdm2 antagonists disrupt their intended targets effectively in cells, whereas stapled peptides did not. Stapled peptides unexpectedly induced cell membrane disruption resulting in p53-independent death associated with cytoplasmic leakage. ReBiL is also valuable for high-throughput screening and for deciphering signaling mechanisms mediated by protein interactions.

  2. Exploration of the omics evidence landscape: adding qualitative labels to predicted protein-protein interactions

    NARCIS (Netherlands)

    Noort, V. van; Snel, B.; Huynen, M.A.

    2007-01-01

    ABSTRACT: BACKGROUND: In the post-genomic era various functional genomics, proteomics and computational techniques have been developed to elucidate the protein interaction network. While some of these techniques are specific for a certain type of interaction, most predict a mixture of interactions.

  3. Exploration of the omics evidence landscape: adding qualitative labels to predicted protein-protein interactions.

    NARCIS (Netherlands)

    Noort, V. van; Snel, B.; Huynen, M.A.

    2007-01-01

    BACKGROUND: In the post-genomic era various functional genomics, proteomics and computational techniques have been developed to elucidate the protein interaction network. While some of these techniques are specific for a certain type of interaction, most predict a mixture of interactions.

  4. MEGADOCK-Web: an integrated database of high-throughput structure-based protein-protein interaction predictions.

    Science.gov (United States)

    Hayashi, Takanori; Matsuzaki, Yuri; Yanagisawa, Keisuke; Ohue, Masahito; Akiyama, Yutaka

    2018-05-08

    Protein-protein interactions (PPIs) play several roles in living cells, and computational PPI prediction is a major focus of many researchers. The three-dimensional (3D) structure and binding surface are important for the design of PPI inhibitors. Therefore, rigid body protein-protein docking calculations for two protein structures are expected to allow elucidation of PPIs different from known complexes in terms of 3D structures because known PPI information is not explicitly required. We have developed rapid PPI prediction software based on protein-protein docking, called MEGADOCK. In order to fully utilize the benefits of computational PPI predictions, it is necessary to construct a comprehensive database to gather prediction results and their predicted 3D complex structures and to make them easily accessible. Although several databases exist that provide predicted PPIs, the previous databases do not contain a sufficient number of entries for the purpose of discovering novel PPIs. In this study, we constructed an integrated database of MEGADOCK PPI predictions, named MEGADOCK-Web. MEGADOCK-Web provides more than 10 times the number of PPI predictions than previous databases and enables users to conduct PPI predictions that cannot be found in conventional PPI prediction databases. In MEGADOCK-Web, there are 7528 protein chains and 28,331,628 predicted PPIs from all possible combinations of those proteins. Each protein structure is annotated with PDB ID, chain ID, UniProt AC, related KEGG pathway IDs, and known PPI pairs. Additionally, MEGADOCK-Web provides four powerful functions: 1) searching precalculated PPI predictions, 2) providing annotations for each predicted protein pair with an experimentally known PPI, 3) visualizing candidates that may interact with the query protein on biochemical pathways, and 4) visualizing predicted complex structures through a 3D molecular viewer. MEGADOCK-Web provides a huge amount of comprehensive PPI predictions based on

  5. Electrostatic similarities between protein and small molecule ligands facilitate the design of protein-protein interaction inhibitors.

    Directory of Open Access Journals (Sweden)

    Arnout Voet

    Full Text Available One of the underlying principles in drug discovery is that a biologically active compound is complimentary in shape and molecular recognition features to its receptor. This principle infers that molecules binding to the same receptor may share some common features. Here, we have investigated whether the electrostatic similarity can be used for the discovery of small molecule protein-protein interaction inhibitors (SMPPIIs. We have developed a method that can be used to evaluate the similarity of electrostatic potentials between small molecules and known protein ligands. This method was implemented in a software called EleKit. Analyses of all available (at the time of research SMPPII structures indicate that SMPPIIs bear some similarities of electrostatic potential with the ligand proteins of the same receptor. This is especially true for the more polar SMPPIIs. Retrospective analysis of several successful SMPPIIs has shown the applicability of EleKit in the design of new SMPPIIs.

  6. Detecting protein complexes based on a combination of topological and biological properties in protein-protein interaction network

    Directory of Open Access Journals (Sweden)

    Pooja Sharma

    2018-06-01

    Full Text Available Protein complexes are known to play a major role in controlling cellular activity in a living being. Identifying complexes from raw protein protein interactions (PPIs is an important area of research. Earlier work has been limited mostly to yeast. Such protein complex identification methods, when applied to large human PPIs often give poor performance. We introduce a novel method called CSC to detect protein complexes. The method is evaluated in terms of positive predictive value, sensitivity and accuracy using the datasets of the model organism, yeast and humans. CSC outperforms several other competing algorithms for both organisms. Further, we present a framework to establish the usefulness of CSC in analyzing the influence of a given disease gene in a complex topologically as well as biologically considering eight major association factors. Keywords: Protein complex, Connectivity, Semantic similarity, Contribution

  7. The TRPC2 channel forms protein-protein interactions with Homer and RTP in the rat vomeronasal organ

    Directory of Open Access Journals (Sweden)

    Brann Jessica H

    2010-05-01

    Full Text Available Abstract Background The signal transduction cascade operational in the vomeronasal organ (VNO of the olfactory system detects odorants important for prey localization, mating, and social recognition. While the protein machinery transducing these external cues has been individually well characterized, little attention has been paid to the role of protein-protein interactions among these molecules. Development of an in vitro expression system for the transient receptor potential 2 channel (TRPC2, which establishes the first electrical signal in the pheromone transduction pathway, led to the discovery of two protein partners that couple with the channel in the native VNO. Results Homer family proteins were expressed in both male and female adult VNO, particularly Homer 1b/c and Homer 3. In addition to this family of scaffolding proteins, the chaperones receptor transporting protein 1 (RTP1 and receptor expression enhancing protein 1 (REEP1 were also expressed. RTP1 was localized broadly across the VNO sensory epithelium, goblet cells, and the soft palate. Both Homer and RTP1 formed protein-protein interactions with TRPC2 in native reciprocal pull-down assays and RTP1 increased surface expression of TRPC2 in in vitro assays. The RTP1-dependent TRPC2 surface expression was paralleled with an increase in ATP-stimulated whole-cell current in an in vitro patch-clamp electrophysiological assay. Conclusions TRPC2 expression and channel activity is regulated by chaperone- and scaffolding-associated proteins, which could modulate the transduction of chemosignals. The developed in vitro expression system, as described here, will be advantageous for detailed investigations into TRPC2 channel activity and cell signalling, for a channel protein that was traditionally difficult to physiologically assess.

  8. Direct protein-protein interaction between PLCγ1 and the bradykinin B2 receptor-Importance of growth conditions

    International Nuclear Information System (INIS)

    Duchene, Johan; Chauhan, Sharmila D.; Lopez, Frederic; Pecher, Christiane; Esteve, Jean-Pierre; Girolami, Jean-Pierre; Bascands, Jean-Loup; Schanstra, Joost P.

    2005-01-01

    Recently, we have described a novel protein-protein interaction between the G-protein coupled bradykinin B2 receptor and tyrosine phosphatase SHP-2 via an immunoreceptor tyrosine-based inhibition motif (ITIM) sequence located in the C-terminal part of the B2 receptor and the Src homology (SH2) domains of SHP-2. Here we show that phospholipase C (PLC)γ1, another SH2 domain containing protein, can also interact with this ITIM sequence. Using surface plasmon resonance analysis, we observed that PLCγ1 interacted with a peptide containing the phosphorylated form of the bradykinin B2 receptor ITIM sequence. In CHO cells expressing the wild-type B2 receptor, bradykinin-induced transient recruitment and activation of PLCγ1. Interestingly, this interaction was only observed in quiescent and not in proliferating cells. Mutation of the key ITIM residue abolished this interaction with and activation of PLCγ1. Finally we also identified bradykinin-induced PLCγ1 recruitment and activation in primary culture renal mesangial cells

  9. Cooperative DNA Recognition Modulated by an Interplay between Protein-Protein Interactions and DNA-Mediated Allostery.

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    Felipe Merino

    2015-06-01

    Full Text Available Highly specific transcriptional regulation depends on the cooperative association of transcription factors into enhanceosomes. Usually, their DNA-binding cooperativity originates from either direct interactions or DNA-mediated allostery. Here, we performed unbiased molecular simulations followed by simulations of protein-DNA unbinding and free energy profiling to study the cooperative DNA recognition by OCT4 and SOX2, key components of enhanceosomes in pluripotent cells. We found that SOX2 influences the orientation and dynamics of the DNA-bound configuration of OCT4. In addition SOX2 modifies the unbinding free energy profiles of both DNA-binding domains of OCT4, the POU specific and POU homeodomain, despite interacting directly only with the first. Thus, we demonstrate that the OCT4-SOX2 cooperativity is modulated by an interplay between protein-protein interactions and DNA-mediated allostery. Further, we estimated the change in OCT4-DNA binding free energy due to the cooperativity with SOX2, observed a good agreement with experimental measurements, and found that SOX2 affects the relative DNA-binding strength of the two OCT4 domains. Based on these findings, we propose that available interaction partners in different biological contexts modulate the DNA exploration routes of multi-domain transcription factors such as OCT4. We consider the OCT4-SOX2 cooperativity as a paradigm of how specificity of transcriptional regulation is achieved through concerted modulation of protein-DNA recognition by different types of interactions.

  10. Improving analytical methods for protein-protein interaction through implementation of chemically inducible dimerization

    DEFF Research Database (Denmark)

    Andersen, Tonni Grube; Nintemann, Sebastian; Marek, Magdalena

    2016-01-01

    When investigating interactions between two proteins with complementary reporter tags in yeast two-hybrid or split GFP assays, it remains troublesome to discriminate true-from false-negative results and challenging to compare the level of interaction across experiments. This leads to decreased se...

  11. Strong interactions in low dimensions

    CERN Document Server

    Baeriswyl, D

    2007-01-01

    This book provides an attempt to convey the colorful facets of condensed matter systems with reduced dimensionality. Some of the specific features predicted for interacting one-dimensional electron systems, such as charge- and spin-density waves, have been observed in many quasi-one-dimensional materials. The two-dimensional world is even richer: besides d-wave superconductivity and the Quantum Hall Effect - perhaps the most spectacular phases explored during the last two decades - many collective charge and spin states have captured the interest of researchers, such as charge stripes or spont

  12. Predicting protein-protein interactions from multimodal biological data sources via nonnegative matrix tri-factorization.

    Science.gov (United States)

    Wang, Hua; Huang, Heng; Ding, Chris; Nie, Feiping

    2013-04-01

    Protein interactions are central to all the biological processes and structural scaffolds in living organisms, because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. Several high-throughput methods, for example, yeast two-hybrid system and mass spectrometry method, can help determine protein interactions, which, however, suffer from high false-positive rates. Moreover, many protein interactions predicted by one method are not supported by another. Therefore, computational methods are necessary and crucial to complete the interactome expeditiously. In this work, we formulate the problem of predicting protein interactions from a new mathematical perspective--sparse matrix completion, and propose a novel nonnegative matrix factorization (NMF)-based matrix completion approach to predict new protein interactions from existing protein interaction networks. Through using manifold regularization, we further develop our method to integrate different biological data sources, such as protein sequences, gene expressions, protein structure information, etc. Extensive experimental results on four species, Saccharomyces cerevisiae, Drosophila melanogaster, Homo sapiens, and Caenorhabditis elegans, have shown that our new methods outperform related state-of-the-art protein interaction prediction methods.

  13. PreBIND and Textomy – mining the biomedical literature for protein-protein interactions using a support vector machine

    Directory of Open Access Journals (Sweden)

    Baskin Berivan

    2003-03-01

    Full Text Available Abstract Background The majority of experimentally verified molecular interaction and biological pathway data are present in the unstructured text of biomedical journal articles where they are inaccessible to computational methods. The Biomolecular interaction network database (BIND seeks to capture these data in a machine-readable format. We hypothesized that the formidable task-size of backfilling the database could be reduced by using Support Vector Machine technology to first locate interaction information in the literature. We present an information extraction system that was designed to locate protein-protein interaction data in the literature and present these data to curators and the public for review and entry into BIND. Results Cross-validation estimated the support vector machine's test-set precision, accuracy and recall for classifying abstracts describing interaction information was 92%, 90% and 92% respectively. We estimated that the system would be able to recall up to 60% of all non-high throughput interactions present in another yeast-protein interaction database. Finally, this system was applied to a real-world curation problem and its use was found to reduce the task duration by 70% thus saving 176 days. Conclusions Machine learning methods are useful as tools to direct interaction and pathway database back-filling; however, this potential can only be realized if these techniques are coupled with human review and entry into a factual database such as BIND. The PreBIND system described here is available to the public at http://bind.ca. Current capabilities allow searching for human, mouse and yeast protein-interaction information.

  14. Large-scale protein-protein interaction analysis in Arabidopsis mesophyll protoplasts by split firefly luciferase complementation.

    Science.gov (United States)

    Li, Jian-Feng; Bush, Jenifer; Xiong, Yan; Li, Lei; McCormack, Matthew

    2011-01-01

    Protein-protein interactions (PPIs) constitute the regulatory network that coordinates diverse cellular functions. There are growing needs in plant research for creating protein interaction maps behind complex cellular processes and at a systems biology level. However, only a few approaches have been successfully used for large-scale surveys of PPIs in plants, each having advantages and disadvantages. Here we present split firefly luciferase complementation (SFLC) as a highly sensitive and noninvasive technique for in planta PPI investigation. In this assay, the separate halves of a firefly luciferase can come into close proximity and transiently restore its catalytic activity only when their fusion partners, namely the two proteins of interest, interact with each other. This assay was conferred with quantitativeness and high throughput potential when the Arabidopsis mesophyll protoplast system and a microplate luminometer were employed for protein expression and luciferase measurement, respectively. Using the SFLC assay, we could monitor the dynamics of rapamycin-induced and ascomycin-disrupted interaction between Arabidopsis FRB and human FKBP proteins in a near real-time manner. As a proof of concept for large-scale PPI survey, we further applied the SFLC assay to testing 132 binary PPIs among 8 auxin response factors (ARFs) and 12 Aux/IAA proteins from Arabidopsis. Our results demonstrated that the SFLC assay is ideal for in vivo quantitative PPI analysis in plant cells and is particularly powerful for large-scale binary PPI screens.

  15. Large-scale protein-protein interaction analysis in Arabidopsis mesophyll protoplasts by split firefly luciferase complementation.

    Directory of Open Access Journals (Sweden)

    Jian-Feng Li

    Full Text Available Protein-protein interactions (PPIs constitute the regulatory network that coordinates diverse cellular functions. There are growing needs in plant research for creating protein interaction maps behind complex cellular processes and at a systems biology level. However, only a few approaches have been successfully used for large-scale surveys of PPIs in plants, each having advantages and disadvantages. Here we present split firefly luciferase complementation (SFLC as a highly sensitive and noninvasive technique for in planta PPI investigation. In this assay, the separate halves of a firefly luciferase can come into close proximity and transiently restore its catalytic activity only when their fusion partners, namely the two proteins of interest, interact with each other. This assay was conferred with quantitativeness and high throughput potential when the Arabidopsis mesophyll protoplast system and a microplate luminometer were employed for protein expression and luciferase measurement, respectively. Using the SFLC assay, we could monitor the dynamics of rapamycin-induced and ascomycin-disrupted interaction between Arabidopsis FRB and human FKBP proteins in a near real-time manner. As a proof of concept for large-scale PPI survey, we further applied the SFLC assay to testing 132 binary PPIs among 8 auxin response factors (ARFs and 12 Aux/IAA proteins from Arabidopsis. Our results demonstrated that the SFLC assay is ideal for in vivo quantitative PPI analysis in plant cells and is particularly powerful for large-scale binary PPI screens.

  16. Studying Protein-Protein Interactions by Biotin AP-Tagged Pulldown and LTQ-Orbitrap Mass Spectrometry.

    Science.gov (United States)

    Xie, Zhongqiu; Jia, Yuemeng; Li, Hui

    2017-01-01

    The study of protein-protein interactions represents a key aspect of biological research. Identifying unknown protein binding partners using mass spectrometry (MS)-based proteomics has evolved into an indispensable strategy in drug discovery. The classic approach of immunoprecipitation with specific antibodies against the proteins of interest has limitations, such as the need for immunoprecipitation-qualified antibody. The biotin AP-tag pull-down system has the advantage of high specificity, ease of use, and no requirement for antibody. It is based on the high specificity, high affinity interaction between biotin and streptavidin. After pulldown, in-gel tryptic digestion and tandem mass spectrometry (MS/MS) analysis of sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) protein bands can be performed. In this work, we provide protocols that can be used for the identification of proteins that interact with FOXM1, a protein that has recently emerged as a potential biomarker and drug target in oncotherapy, as an example. We focus on the pull-down procedure and assess the efficacy of the pulldown with known FOXM1 interactors such as β-catenin. We use a high performance LTQ Orbitrap MSn system that combines rapid LTQ ion trap data acquisition with high mass accuracy Orbitrap analysis to identify the interacting proteins.

  17. Surface dynamics in allosteric regulation of protein-protein interactions: modulation of calmodulin functions by Ca2+.

    Directory of Open Access Journals (Sweden)

    Yosef Y Kuttner

    2013-04-01

    Full Text Available Knowledge of the structural basis of protein-protein interactions (PPI is of fundamental importance for understanding the organization and functioning of biological networks and advancing the design of therapeutics which target PPI. Allosteric modulators play an important role in regulating such interactions by binding at site(s orthogonal to the complex interface and altering the protein's propensity for complex formation. In this work, we apply an approach recently developed by us for analyzing protein surfaces based on steered molecular dynamics simulation (SMD to the study of the dynamic properties of functionally distinct conformations of a model protein, calmodulin (CaM, whose ability to interact with target proteins is regulated by the presence of the allosteric modulator Ca(2+. Calmodulin is a regulatory protein that acts as an intracellular Ca(2+ sensor to control a wide variety of cellular processes. We demonstrate that SMD analysis is capable of pinpointing CaM surfaces implicated in the recognition of both the allosteric modulator Ca(2+ and target proteins. Our analysis of changes in the dynamic properties of the CaM backbone elicited by Ca(2+ binding yielded new insights into the molecular mechanism of allosteric regulation of CaM-target interactions.

  18. Macrocyclic peptide inhibitors for the protein-protein interaction of Zaire Ebola virus protein 24 and karyopherin alpha 5.

    Science.gov (United States)

    Song, Xiao; Lu, Lu-Yi; Passioura, Toby; Suga, Hiroaki

    2017-06-21

    Ebola virus infection leads to severe hemorrhagic fever in human and non-human primates with an average case fatality rate of 50%. To date, numerous potential therapies are in development, but FDA-approved drugs or vaccines are yet unavailable. Ebola viral protein 24 (VP24) is a multifunctional protein that plays critical roles in the pathogenesis of Ebola virus infection, e.g. innate immune suppression by blocking the interaction between KPNA and PY-STAT1. Here we report macrocyclic peptide inhibitors of the VP24-KPNA5 protein-protein interaction (PPI) by means of the RaPID (Random non-standard Peptides Integrated Discovery) system. These macrocyclic peptides showed remarkably high affinity to recombinant Zaire Ebola virus VP24 (eVP24), with a dissociation constant in the single digit nanomolar range, and could also successfully disrupt the eVP24-KPNA interaction. This work provides for the first time a chemical probe capable of modulating this PPI interaction and is the starting point for the development of unique anti-viral drugs against the Ebola virus.

  19. Prediction of protein-protein interaction sites in sequences and 3D structures by random forests.

    Directory of Open Access Journals (Sweden)

    Mile Sikić

    2009-01-01

    Full Text Available Identifying interaction sites in proteins provides important clues to the function of a protein and is becoming increasingly relevant in topics such as systems biology and drug discovery. Although there are numerous papers on the prediction of interaction sites using information derived from structure, there are only a few case reports on the prediction of interaction residues based solely on protein sequence. Here, a sliding window approach is combined with the Random Forests method to predict protein interaction sites using (i a combination of sequence- and structure-derived parameters and (ii sequence information alone. For sequence-based prediction we achieved a precision of 84% with a 26% recall and an F-measure of 40%. When combined with structural information, the prediction performance increases to a precision of 76% and a recall of 38% with an F-measure of 51%. We also present an attempt to rationalize the sliding window size and demonstrate that a nine-residue window is the most suitable for predictor construction. Finally, we demonstrate the applicability of our prediction methods by modeling the Ras-Raf complex using predicted interaction sites as target binding interfaces. Our results suggest that it is possible to predict protein interaction sites with quite a high accuracy using only sequence information.

  20. Determining effects of non-synonymous SNPs on protein-protein interactions using supervised and semi-supervised learning.

    Directory of Open Access Journals (Sweden)

    Nan Zhao

    2014-05-01

    Full Text Available Single nucleotide polymorphisms (SNPs are among the most common types of genetic variation in complex genetic disorders. A growing number of studies link the functional role of SNPs with the networks and pathways mediated by the disease-associated genes. For example, many non-synonymous missense SNPs (nsSNPs have been found near or inside the protein-protein interaction (PPI interfaces. Determining whether such nsSNP will disrupt or preserve a PPI is a challenging task to address, both experimentally and computationally. Here, we present this task as three related classification problems, and develop a new computational method, called the SNP-IN tool (non-synonymous SNP INteraction effect predictor. Our method predicts the effects of nsSNPs on PPIs, given the interaction's structure. It leverages supervised and semi-supervised feature-based classifiers, including our new Random Forest self-learning protocol. The classifiers are trained based on a dataset of comprehensive mutagenesis studies for 151 PPI complexes, with experimentally determined binding affinities of the mutant and wild-type interactions. Three classification problems were considered: (1 a 2-class problem (strengthening/weakening PPI mutations, (2 another 2-class problem (mutations that disrupt/preserve a PPI, and (3 a 3-class classification (detrimental/neutral/beneficial mutation effects. In total, 11 different supervised and semi-supervised classifiers were trained and assessed resulting in a promising performance, with the weighted f-measure ranging from 0.87 for Problem 1 to 0.70 for the most challenging Problem 3. By integrating prediction results of the 2-class classifiers into the 3-class classifier, we further improved its performance for Problem 3. To demonstrate the utility of SNP-IN tool, it was applied to study the nsSNP-induced rewiring of two disease-centered networks. The accurate and balanced performance of SNP-IN tool makes it readily available to study the

  1. Determining Effects of Non-synonymous SNPs on Protein-Protein Interactions using Supervised and Semi-supervised Learning

    Science.gov (United States)

    Zhao, Nan; Han, Jing Ginger; Shyu, Chi-Ren; Korkin, Dmitry

    2014-01-01

    Single nucleotide polymorphisms (SNPs) are among the most common types of genetic variation in complex genetic disorders. A growing number of studies link the functional role of SNPs with the networks and pathways mediated by the disease-associated genes. For example, many non-synonymous missense SNPs (nsSNPs) have been found near or inside the protein-protein interaction (PPI) interfaces. Determining whether such nsSNP will disrupt or preserve a PPI is a challenging task to address, both experimentally and computationally. Here, we present this task as three related classification problems, and develop a new computational method, called the SNP-IN tool (non-synonymous SNP INteraction effect predictor). Our method predicts the effects of nsSNPs on PPIs, given the interaction's structure. It leverages supervised and semi-supervised feature-based classifiers, including our new Random Forest self-learning protocol. The classifiers are trained based on a dataset of comprehensive mutagenesis studies for 151 PPI complexes, with experimentally determined binding affinities of the mutant and wild-type interactions. Three classification problems were considered: (1) a 2-class problem (strengthening/weakening PPI mutations), (2) another 2-class problem (mutations that disrupt/preserve a PPI), and (3) a 3-class classification (detrimental/neutral/beneficial mutation effects). In total, 11 different supervised and semi-supervised classifiers were trained and assessed resulting in a promising performance, with the weighted f-measure ranging from 0.87 for Problem 1 to 0.70 for the most challenging Problem 3. By integrating prediction results of the 2-class classifiers into the 3-class classifier, we further improved its performance for Problem 3. To demonstrate the utility of SNP-IN tool, it was applied to study the nsSNP-induced rewiring of two disease-centered networks. The accurate and balanced performance of SNP-IN tool makes it readily available to study the rewiring of

  2. Remnants of strong tidal interactions

    International Nuclear Information System (INIS)

    Mcglynn, T.A.

    1990-01-01

    This paper examines the properties of stellar systems that have recently undergone a strong tidal shock, i.e., a shock which removes a significant fraction of the particles in the system, and where the shocked system has a much smaller mass than the producer of the tidal field. N-body calculations of King models shocked in a variety of ways are performed, and the consequences of the shocks are investigated. The results confirm the prediction of Jaffe for shocked systems. Several models are also run where the tidal forces on the system are constant, simulating a circular orbit around a primary, and the development of tidal radii under these static conditions appears to be a mild process which does not dramatically affect material that is not stripped. The tidal radii are about twice as large as classical formulas would predict. Remnant density profiles are compared with a sample of elliptical galaxies, and the implications of the results for the development of stellar populations and galaxies are considered. 38 refs

  3. Computational Approaches for Prediction of Pathogen-Host Protein-Protein Interactions

    Directory of Open Access Journals (Sweden)

    Esmaeil eNourani

    2015-02-01

    Full Text Available Infectious diseases are still among the major and prevalent health problems, mostly because of the drug resistance of novel variants of pathogens. Molecular interactions between pathogens and their hosts are the key part of the infection mechanisms. Novel antimicrobial therapeutics to fight drug resistance is only possible in case of a thorough understanding of pathogen-host interaction (PHI systems. Existing databases, which contain experimentally verified PHI data, suffer from scarcity of reported interactions due to the technically challenging and time consuming process of experiments. This has motivated many researchers to address the problem by proposing computational approaches for analysis and prediction of PHIs. The computational methods primarily utilize sequence information, protein structure and known interactions. Classic machine learning techniques are used when there are sufficient known interactions to be used as training data. On the opposite case, transfer and multi task learning methods are preferred. Here, we present an overview of these computational approaches for PHI prediction, discussing their weakness and abilities, with future directions.

  4. A feature-based approach to modeling protein-protein interaction hot spots.

    Science.gov (United States)

    Cho, Kyu-il; Kim, Dongsup; Lee, Doheon

    2009-05-01

    Identifying features that effectively represent the energetic contribution of an individual interface residue to the interactions between proteins remains problematic. Here, we present several new features and show that they are more effective than conventional features. By combining the proposed features with conventional features, we develop a predictive model for interaction hot spots. Initially, 54 multifaceted features, composed of different levels of information including structure, sequence and molecular interaction information, are quantified. Then, to identify the best subset of features for predicting hot spots, feature selection is performed using a decision tree. Based on the selected features, a predictive model for hot spots is created using support vector machine (SVM) and tested on an independent test set. Our model shows better overall predictive accuracy than previous methods such as the alanine scanning methods Robetta and FOLDEF, and the knowledge-based method KFC. Subsequent analysis yields several findings about hot spots. As expected, hot spots have a larger relative surface area burial and are more hydrophobic than other residues. Unexpectedly, however, residue conservation displays a rather complicated tendency depending on the types of protein complexes, indicating that this feature is not good for identifying hot spots. Of the selected features, the weighted atomic packing density, relative surface area burial and weighted hydrophobicity are the top 3, with the weighted atomic packing density proving to be the most effective feature for predicting hot spots. Notably, we find that hot spots are closely related to pi-related interactions, especially pi . . . pi interactions.

  5. Automatic selection of reference taxa for protein-protein interaction prediction with phylogenetic profiling

    DEFF Research Database (Denmark)

    Simonsen, Martin; Maetschke, S.R.; Ragan, M.A.

    2012-01-01

    Motivation: Phylogenetic profiling methods can achieve good accuracy in predicting protein–protein interactions, especially in prokaryotes. Recent studies have shown that the choice of reference taxa (RT) is critical for accurate prediction, but with more than 2500 fully sequenced taxa publicly......: We present three novel methods for automating the selection of RT, using machine learning based on known protein–protein interaction networks. One of these methods in particular, Tree-Based Search, yields greatly improved prediction accuracies. We further show that different methods for constituting...... phylogenetic profiles often require very different RT sets to support high prediction accuracy....

  6. A computational tool to predict the evolutionarily conserved protein-protein interaction hot-spot residues from the structure of the unbound protein.

    Science.gov (United States)

    Agrawal, Neeraj J; Helk, Bernhard; Trout, Bernhardt L

    2014-01-21

    Identifying hot-spot residues - residues that are critical to protein-protein binding - can help to elucidate a protein's function and assist in designing therapeutic molecules to target those residues. We present a novel computational tool, termed spatial-interaction-map (SIM), to predict the hot-spot residues of an evolutionarily conserved protein-protein interaction from the structure of an unbound protein alone. SIM can predict the protein hot-spot residues with an accuracy of 36-57%. Thus, the SIM tool can be used to predict the yet unknown hot-spot residues for many proteins for which the structure of the protein-protein complexes are not available, thereby providing a clue to their functions and an opportunity to design therapeutic molecules to target these proteins. Copyright © 2013 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

  7. Kinome signaling through regulated protein-protein interactions in normal and cancer cells.

    Science.gov (United States)

    Pawson, Tony; Kofler, Michael

    2009-04-01

    The flow of molecular information through normal and oncogenic signaling pathways frequently depends on protein phosphorylation, mediated by specific kinases, and the selective binding of the resulting phosphorylation sites to interaction domains present on downstream targets. This physical and functional interplay of catalytic and interaction domains can be clearly seen in cytoplasmic tyrosine kinases such as Src, Abl, Fes, and ZAP-70. Although the kinase and SH2 domains of these proteins possess similar intrinsic properties of phosphorylating tyrosine residues or binding phosphotyrosine sites, they also undergo intramolecular interactions when linked together, in a fashion that varies from protein to protein. These cooperative interactions can have diverse effects on substrate recognition and kinase activity, and provide a variety of mechanisms to link the stimulation of catalytic activity to substrate recognition. Taken together, these data have suggested how protein kinases, and the signaling pathways in which they are embedded, can evolve complex properties through the stepwise linkage of domains within single polypeptides or multi-protein assemblies.

  8. Regulation of dopamine transporter function by protein-protein interactions: new discoveries and methodological challenges

    DEFF Research Database (Denmark)

    Eriksen, Jacob; Jørgensen, Trine Nygaard; Gether, Ulrik

    2010-01-01

    -synaptic neurons. This has led to the identification of a plethora of different kinases, receptors and scaffolding proteins that interact with DAT and hereby either modulate the catalytic activity of the transporter or regulate its trafficking and degradation. Several new tools for studying DAT regulation in live...

  9. Protein function prediction using neighbor relativity in protein-protein interaction network.

    Science.gov (United States)

    Moosavi, Sobhan; Rahgozar, Masoud; Rahimi, Amir

    2013-04-01

    There is a large gap between the number of discovered proteins and the number of functionally annotated ones. Due to the high cost of determining protein function by wet-lab research, function prediction has become a major task for computational biology and bioinformatics. Some researches utilize the proteins interaction information to predict function for un-annotated proteins. In this paper, we propose a novel approach called "Neighbor Relativity Coefficient" (NRC) based on interaction network topology which estimates the functional similarity between two proteins. NRC is calculated for each pair of proteins based on their graph-based features including distance, common neighbors and the number of paths between them. In order to ascribe function to an un-annotated protein, NRC estimates a weight for each neighbor to transfer its annotation to the unknown protein. Finally, the unknown protein will be annotated by the top score transferred functions. We also investigate the effect of using different coefficients for various types of functions. The proposed method has been evaluated on Saccharomyces cerevisiae and Homo sapiens interaction networks. The performance analysis demonstrates that NRC yields better results in comparison with previous protein function prediction approaches that utilize interaction network. Copyright © 2012 Elsevier Ltd. All rights reserved.

  10. Prediction of residue-residue contact matrix for protein-protein interaction with Fisher score features and deep learning.

    Science.gov (United States)

    Du, Tianchuan; Liao, Li; Wu, Cathy H; Sun, Bilin

    2016-11-01

    Protein-protein interactions play essential roles in many biological processes. Acquiring knowledge of the residue-residue contact information of two interacting proteins is not only helpful in annotating functions for proteins, but also critical for structure-based drug design. The prediction of the protein residue-residue contact matrix of the interfacial regions is challenging. In this work, we introduced deep learning techniques (specifically, stacked autoencoders) to build deep neural network models to tackled the residue-residue contact prediction problem. In tandem with interaction profile Hidden Markov Models, which was used first to extract Fisher score features from protein sequences, stacked autoencoders were deployed to extract and learn hidden abstract features. The deep learning model showed significant improvement over the traditional machine learning model, Support Vector Machines (SVM), with the overall accuracy increased by 15% from 65.40% to 80.82%. We showed that the stacked autoencoders could extract novel features, which can be utilized by deep neural networks and other classifiers to enhance learning, out of the Fisher score features. It is further shown that deep neural networks have significant advantages over SVM in making use of the newly extracted features. Copyright © 2016. Published by Elsevier Inc.

  11. A novel protein-protein interaction in the RES (REtention and Splicing) complex.

    Science.gov (United States)

    Tripsianes, Konstantinos; Friberg, Anders; Barrandon, Charlotte; Brooks, Mark; van Tilbeurgh, Herman; Seraphin, Bertrand; Sattler, Michael

    2014-10-10

    The retention and splicing (RES) complex is a conserved spliceosome-associated module that was shown to enhance splicing of a subset of transcripts and promote the nuclear retention of unspliced pre-mRNAs in yeast. The heterotrimeric RES complex is organized around the Snu17p protein that binds to both the Bud13p and Pml1p subunits. Snu17p exhibits an RRM domain that resembles a U2AF homology motif (UHM) and Bud13p harbors a Trp residue reminiscent of an UHM-ligand motif (ULM). It has therefore been proposed that the interaction between Snu17p and Bud13p resembles canonical UHM-ULM complexes. Here, we have used biochemical and NMR structural analysis to characterize the structure of the yeast Snu17p-Bud13p complex. Unlike known UHMs that sequester the Trp residue of the ULM ligand in a hydrophobic pocket, Snu17p and Bud13p utilize a large interaction surface formed around the two helices of the Snu17p domain. In total 18 residues of the Bud13p ligand wrap around the Snu17p helical surface in an U-turn-like arrangement. The invariant Trp(232) in Bud13p is located in the center of the turn, and contacts surface residues of Snu17p. The structural data are supported by mutational analysis and indicate that Snu17p provides an extended binding surface with Bud13p that is notably distinct from canonical UHM-ULM interactions. Our data highlight structural diversity in RRM-protein interactions, analogous to the one seen for nucleic acid interactions. © 2014 by The American Society for Biochemistry and Molecular Biology, Inc.

  12. Investigating the Role of Large-Scale Domain Dynamics in Protein-Protein Interactions.

    Science.gov (United States)

    Delaforge, Elise; Milles, Sigrid; Huang, Jie-Rong; Bouvier, Denis; Jensen, Malene Ringkjøbing; Sattler, Michael; Hart, Darren J; Blackledge, Martin

    2016-01-01

    Intrinsically disordered linkers provide multi-domain proteins with degrees of conformational freedom that are often essential for function. These highly dynamic assemblies represent a significant fraction of all proteomes, and deciphering the physical basis of their interactions represents a considerable challenge. Here we describe the difficulties associated with mapping the large-scale domain dynamics and describe two recent examples where solution state methods, in particular NMR spectroscopy, are used to investigate conformational exchange on very different timescales.

  13. Investigating the Role of Large-Scale Domain Dynamics in Protein-Protein Interactions

    Directory of Open Access Journals (Sweden)

    Elise Delaforge

    2016-09-01

    Full Text Available Intrinsically disordered linkers provide multi-domain proteins with degrees of conformational freedom that are often essential for function. These highly dynamic assemblies represent a significant fraction of all proteomes, and deciphering the physical basis of their interactions represents a considerable challenge. Here we describe the difficulties associated with mapping the large-scale domain dynamics and describe two recent examples where solution state methods, in particular NMR spectroscopy, are used to investigate conformational exchange on very different timescales.

  14. The Ser/Thr Protein Kinase Protein-Protein Interaction Map of M. tuberculosis.

    Science.gov (United States)

    Wu, Fan-Lin; Liu, Yin; Jiang, He-Wei; Luan, Yi-Zhao; Zhang, Hai-Nan; He, Xiang; Xu, Zhao-Wei; Hou, Jing-Li; Ji, Li-Yun; Xie, Zhi; Czajkowsky, Daniel M; Yan, Wei; Deng, Jiao-Yu; Bi, Li-Jun; Zhang, Xian-En; Tao, Sheng-Ce

    2017-08-01

    Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis, the leading cause of death among all infectious diseases. There are 11 eukaryotic-like serine/threonine protein kinases (STPKs) in Mtb, which are thought to play pivotal roles in cell growth, signal transduction and pathogenesis. However, their underlying mechanisms of action remain largely uncharacterized. In this study, using a Mtb proteome microarray, we have globally identified the binding proteins in Mtb for all of the STPKs, and constructed the first STPK protein interaction (KPI) map that includes 492 binding proteins and 1,027 interactions. Bioinformatics analysis showed that the interacting proteins reflect diverse functions, including roles in two-component system, transcription, protein degradation, and cell wall integrity. Functional investigations confirmed that PknG regulates cell wall integrity through key components of peptidoglycan (PG) biosynthesis, e.g. MurC. The global STPK-KPIs network constructed here is expected to serve as a rich resource for understanding the key signaling pathways in Mtb, thus facilitating drug development and effective control of Mtb. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

  15. Predicting Protein-Protein Interactions Using BiGGER: Case Studies

    Directory of Open Access Journals (Sweden)

    Rui M. Almeida

    2016-08-01

    Full Text Available The importance of understanding interactomes makes preeminent the study of protein interactions and protein complexes. Traditionally, protein interactions have been elucidated by experimental methods or, with lower impact, by simulation with protein docking algorithms. This article describes features and applications of the BiGGER docking algorithm, which stands at the interface of these two approaches. BiGGER is a user-friendly docking algorithm that was specifically designed to incorporate experimental data at different stages of the simulation, to either guide the search for correct structures or help evaluate the results, in order to combine the reliability of hard data with the convenience of simulations. Herein, the applications of BiGGER are described by illustrative applications divided in three Case Studies: (Case Study A in which no specific contact data is available; (Case Study B when different experimental data (e.g., site-directed mutagenesis, properties of the complex, NMR chemical shift perturbation mapping, electron tunneling on one of the partners is available; and (Case Study C when experimental data are available for both interacting surfaces, which are used during the search and/or evaluation stage of the docking. This algorithm has been extensively used, evidencing its usefulness in a wide range of different biological research fields.

  16. Investigating CFTR and KCa3.1 Protein/Protein Interactions.

    Directory of Open Access Journals (Sweden)

    Hélène Klein

    Full Text Available In epithelia, Cl- channels play a prominent role in fluid and electrolyte transport. Of particular importance is the cAMP-dependent cystic fibrosis transmembrane conductance regulator Cl- channel (CFTR with mutations of the CFTR encoding gene causing cystic fibrosis. The bulk transepithelial transport of Cl- ions and electrolytes needs however to be coupled to an increase in K+ conductance in order to recycle K+ and maintain an electrical driving force for anion exit across the apical membrane. In several epithelia, this K+ efflux is ensured by K+ channels, including KCa3.1, which is expressed at both the apical and basolateral membranes. We show here for the first time that CFTR and KCa3.1 can physically interact. We first performed a two-hybrid screen to identify which KCa3.1 cytosolic domains might mediate an interaction with CFTR. Our results showed that both the N-terminal fragment M1-M40 of KCa3.1 and part of the KCa3.1 calmodulin binding domain (residues L345-A400 interact with the NBD2 segment (G1237-Y1420 and C- region of CFTR (residues T1387-L1480, respectively. An association of CFTR and F508del-CFTR with KCa3.1 was further confirmed in co-immunoprecipitation experiments demonstrating the formation of immunoprecipitable CFTR/KCa3.1 complexes in CFBE cells. Co-expression of KCa3.1 and CFTR in HEK cells did not impact CFTR expression at the cell surface, and KCa3.1 trafficking appeared independent of CFTR stimulation. Finally, evidence is presented through cross-correlation spectroscopy measurements that KCa3.1 and CFTR colocalize at the plasma membrane and that KCa3.1 channels tend to aggregate consequent to an enhanced interaction with CFTR channels at the plasma membrane following an increase in intracellular Ca2+ concentration. Altogether, these results suggest 1 that the physical interaction KCa3.1/CFTR can occur early during the biogenesis of both proteins and 2 that KCa3.1 and CFTR form a dynamic complex, the formation of which

  17. The missing piece in the puzzle: Prediction of aggregation via the protein-protein interaction parameter A∗2.

    Science.gov (United States)

    Koepf, Ellen; Schroeder, Rudolf; Brezesinski, Gerald; Friess, Wolfgang

    2018-07-01

    The tendency of protein pharmaceuticals to form aggregates is a major challenge during formulation development, as aggregation affects quality and safety of the product. In particular, the formation of large native-like particles in the context of liquid-air interfacial stress is a well-known but not fully understood problem. Focusing on the two most fundamental criteria of protein formulation affecting protein-protein interaction, the impact of pH and ionic strength on the interaction parameter A ∗ 2 and its link to aggregation upon mechanical stress was investigated. A ∗ 2 of two monoclonal antibodies (mABs) and a polyclonal IgG was determined using dynamic light scattering and was correlated to the number of particles formed upon shaking in vials analyzed by visual inspection, turbidity analysis, light obscuration and micro-flow imaging. A good correlation between aggregation induced by interfacial stress and formulation pH was given. It could be shown that A ∗ 2 was highest for mAB 1 and lowest for IgG, what was in good accordance with the number of particles formed. Shaking of IgG resulted in overall higher numbers of particles compared to the two mABs. A ∗ 2 decreased and particle numbers increased with increasing pH. Different to pH, ionic strength only slightly affected A ∗ 2 . Nevertheless, at high ionic (100 mM) strength the samples exhibited more pronounced particle formation, particularly of large particles >25 µm, which was most pronounced at high pH. Protein solutions were identified to form continuous films with an inhomogeneous protein distribution at the liquid-air interface. These areas of agglomerated, native-like protein material can be transferred into the bulk solution by compression-decompression of the interface. Whether or not those clusters lead to the appearance of large protein aggregates or fall apart depends on the attractive or repulsive forces between protein molecules. Thus, protein aggregation due to interfacial

  18. Improving protein-protein interaction prediction using evolutionary information from low-quality MSAs.

    Science.gov (United States)

    Várnai, Csilla; Burkoff, Nikolas S; Wild, David L

    2017-01-01

    Evolutionary information stored in multiple sequence alignments (MSAs) has been used to identify the interaction interface of protein complexes, by measuring either co-conservation or co-mutation of amino acid residues across the interface. Recently, maximum entropy related correlated mutation measures (CMMs) such as direct information, decoupling direct from indirect interactions, have been developed to identify residue pairs interacting across the protein complex interface. These studies have focussed on carefully selected protein complexes with large, good-quality MSAs. In this work, we study protein complexes with a more typical MSA consisting of fewer than 400 sequences, using a set of 79 intramolecular protein complexes. Using a maximum entropy based CMM at the residue level, we develop an interface level CMM score to be used in re-ranking docking decoys. We demonstrate that our interface level CMM score compares favourably to the complementarity trace score, an evolutionary information-based score measuring co-conservation, when combined with the number of interface residues, a knowledge-based potential and the variability score of individual amino acid sites. We also demonstrate, that, since co-mutation and co-complementarity in the MSA contain orthogonal information, the best prediction performance using evolutionary information can be achieved by combining the co-mutation information of the CMM with co-conservation information of a complementarity trace score, predicting a near-native structure as the top prediction for 41% of the dataset. The method presented is not restricted to small MSAs, and will likely improve interface prediction also for complexes with large and good-quality MSAs.

  19. Cross-Species Virus-Host Protein-Protein Interactions Inhibiting Innate Immunity

    Science.gov (United States)

    2016-07-01

    diseases are a regular occurrence globally (Figure 1). The Zika virus is the latest example gaining widespread attention. Many of the (re-)emerging...for establishing infection and/or modulating pathogenesis (Figures 2 and 3). 3 Figure 2. Schematic of several virus -host protein interactions within...8725 John J. Kingman Road, MS 6201 Fort Belvoir, VA 22060-6201 T E C H N IC A L R E P O R T DTRA-TR-16-79 Cross-species virus -host

  20. Unraveling protein-protein interactions in clathrin assemblies via atomic force spectroscopy.

    Science.gov (United States)

    Jin, Albert J; Lafer, Eileen M; Peng, Jennifer Q; Smith, Paul D; Nossal, Ralph

    2013-03-01

    Atomic force microscopy (AFM), single molecule force spectroscopy (SMFS), and single particle force spectroscopy (SPFS) are used to characterize intermolecular interactions and domain structures of clathrin triskelia and clathrin-coated vesicles (CCVs). The latter are involved in receptor-mediated endocytosis (RME) and other trafficking pathways. Here, we subject individual triskelia, bovine-brain CCVs, and reconstituted clathrin-AP180 coats to AFM-SMFS and AFM-SPFS pulling experiments and apply novel analytics to extract force-extension relations from very large data sets. The spectroscopic fingerprints of these samples differ markedly, providing important new information about the mechanism of CCV uncoating. For individual triskelia, SMFS reveals a series of events associated with heavy chain alpha-helix hairpin unfolding, as well as cooperative unraveling of several hairpin domains. SPFS of clathrin assemblies exposes weaker clathrin-clathrin interactions that are indicative of inter-leg association essential for RME and intracellular trafficking. Clathrin-AP180 coats are energetically easier to unravel than the coats of CCVs, with a non-trivial dependence on force-loading rate. Published by Elsevier Inc.

  1. Noninvasive imaging of protein-protein interactions from live cells and living subjects using bioluminescence resonance energy transfer.

    Science.gov (United States)

    De, Abhijit; Gambhir, Sanjiv Sam

    2005-12-01

    This study demonstrates a significant advancement of imaging of a distance-dependent physical process, known as the bioluminescent resonance energy transfer (BRET2) signal in living subjects, by using a cooled charge-coupled device (CCD) camera. A CCD camera-based spectral imaging strategy enables simultaneous visualization and quantitation of BRET signal from live cells and cells implanted in living mice. We used the BRET2 system, which utilizes Renilla luciferase (hRluc) protein and its substrate DeepBlueC (DBC) as an energy donor and a mutant green fluorescent protein (GFP2) as the acceptor. To accomplish this objective in this proof-of-principle study, the donor and acceptor proteins were fused to FKBP12 and FRB, respectively, which are known to interact only in the presence of the small molecule mediator rapamycin. Mammalian cells expressing these fusion constructs were imaged using a cooled-CCD camera either directly from culture dishes or by implanting them into mice. By comparing the emission photon yields in the presence and absence of rapamycin, the specific BRET signal was determined. The CCD imaging approach of BRET signal is particularly appealing due to its capacity to seamlessly bridge the gap between in vitro and in vivo studies. This work validates BRET as a powerful tool for interrogating and observing protein-protein interactions directly at limited depths in living mice.

  2. Recent progress in the development of protein-protein interaction inhibitors targeting androgen receptor-coactivator binding in prostate cancer.

    Science.gov (United States)

    Biron, Eric; Bédard, François

    2016-07-01

    The androgen receptor (AR) is a key regulator for the growth, differentiation and survival of prostate cancer cells. Identified as a primary target for the treatment of prostate cancer, many therapeutic strategies have been developed to attenuate AR signaling in prostate cancer cells. While frontline androgen-deprivation therapies targeting either the production or action of androgens usually yield favorable responses in prostate cancer patients, a significant number acquire treatment resistance. Known as the castration-resistant prostate cancer (CRPC), the treatment options are limited for this advanced stage. It has been shown that AR signaling is restored in CRPC due to many aberrant mechanisms such as AR mutations, amplification or expression of constitutively active splice-variants. Coregulator recruitment is a crucial regulatory step in AR signaling and the direct blockade of coactivator binding to AR offers the opportunity to develop therapeutic agents that would remain effective in prostate cancer cells resistant to conventional endocrine therapies. Structural analyses of the AR have identified key surfaces involved in protein-protein interaction with coregulators that have been recently used to design and develop promising AR-coactivator binding inhibitors. In this review we will discuss the design and development of small-molecule inhibitors targeting the AR-coactivator interactions for the treatment of prostate cancer. Copyright © 2015 Elsevier Ltd. All rights reserved.

  3. Physics challenges in the strong interactions

    International Nuclear Information System (INIS)

    Ellis, S.D.

    1992-01-01

    The study of strong interactions is now a mature field for which scientist now know that the correct underlying theory is QCD. Here, an overview of the challenges to be faced in the area of the strong interactions during the 1990's is presented. As an illustrative example special attention is given to the analysis of jets as studied at hadron colliders

  4. Physics challenges in the strong interactions

    Energy Technology Data Exchange (ETDEWEB)

    Ellis, S.D. [Univ. of Washington, Seattle (United States)

    1992-12-31

    The study of strong interactions is now a mature field for which scientist now know that the correct underlying theory is QCD. Here, an overview of the challenges to be faced in the area of the strong interactions during the 1990`s is presented. As an illustrative example special attention is given to the analysis of jets as studied at hadron colliders.

  5. Algebra of strong and electroweak interactions

    International Nuclear Information System (INIS)

    Bolokhov, S.V.; Vladimirov, Yu.S.

    2004-01-01

    The algebraic approach to describing the electroweak and strong interactions is considered within the frames of the binary geometrophysics, based on the principles of the Fokker-Feynman direct interparticle interaction theories of the Kaluza-Klein multidimensional geometrical models and the physical structures theory. It is shown that in this approach the electroweak and strong elementary particles interaction through the intermediate vector bosons, are characterized by the subtypes of the algebraic classification of the complex 3 x 3-matrices [ru

  6. Nanobody Technology: A Versatile Toolkit for Microscopic Imaging, Protein-Protein Interaction Analysis, and Protein Function Exploration.

    Science.gov (United States)

    Beghein, Els; Gettemans, Jan

    2017-01-01

    Over the last two decades, nanobodies or single-domain antibodies have found their way in research, diagnostics, and therapy. These antigen-binding fragments, derived from Camelid heavy chain only antibodies, possess remarkable characteristics that favor their use over conventional antibodies or fragments thereof, in selected areas of research. In this review, we assess the current status of nanobodies as research tools in diverse aspects of fundamental research. We discuss the use of nanobodies as detection reagents in fluorescence microscopy and focus on recent advances in super-resolution microscopy. Second, application of nanobody technology in investigating protein-protein interactions is reviewed, with emphasis on possible uses in mass spectrometry. Finally, we discuss the potential value of nanobodies in studying protein function, and we focus on their recently reported application in targeted protein degradation. Throughout the review, we highlight state-of-the-art engineering strategies that could expand nanobody versatility and we suggest future applications of the technology in the selected areas of fundamental research.

  7. Identification of polycystic ovary syndrome potential drug targets based on pathobiological similarity in the protein-protein interaction network

    Science.gov (United States)

    Li, Wan; Wei, Wenqing; Li, Yiran; Xie, Ruiqiang; Guo, Shanshan; Wang, Yahui; Jiang, Jing; Chen, Binbin; Lv, Junjie; Zhang, Nana; Chen, Lina; He, Weiming

    2016-01-01

    Polycystic ovary syndrome (PCOS) is one of the most common endocrinological disorders in reproductive aged women. PCOS and Type 2 Diabetes (T2D) are closely linked in multiple levels and possess high pathobiological similarity. Here, we put forward a new computational approach based on the pathobiological similarity to identify PCOS potential drug target modules (PPDT-Modules) and PCOS potential drug targets in the protein-protein interaction network (PPIN). From the systems level and biological background, 1 PPDT-Module and 22 PCOS potential drug targets were identified, 21 of which were verified by literatures to be associated with the pathogenesis of PCOS. 42 drugs targeting to 13 PCOS potential drug targets were investigated experimentally or clinically for PCOS. Evaluated by independent datasets, the whole PPDT-Module and 22 PCOS potential drug targets could not only reveal the drug response, but also distinguish the statuses between normal and disease. Our identified PPDT-Module and PCOS potential drug targets would shed light on the treatment of PCOS. And our approach would provide valuable insights to research on the pathogenesis and drug response of other diseases. PMID:27191267

  8. Analysis of Protein Phosphorylation and Its Functional Impact on Protein-Protein Interactions via Text Mining of the Scientific Literature.

    Science.gov (United States)

    Wang, Qinghua; Ross, Karen E; Huang, Hongzhan; Ren, Jia; Li, Gang; Vijay-Shanker, K; Wu, Cathy H; Arighi, Cecilia N

    2017-01-01

    Post-translational modifications (PTMs) are one of the main contributors to the diversity of proteoforms in the proteomic landscape. In particular, protein phosphorylation represents an essential regulatory mechanism that plays a role in many biological processes. Protein kinases, the enzymes catalyzing this reaction, are key participants in metabolic and signaling pathways. Their activation or inactivation dictate downstream events: what substrates are modified and their subsequent impact (e.g., activation state, localization, protein-protein interactions (PPIs)). The biomedical literature continues to be the main source of evidence for experimental information about protein phosphorylation. Automatic methods to bring together phosphorylation events and phosphorylation-dependent PPIs can help to summarize the current knowledge and to expose hidden connections. In this chapter, we demonstrate two text mining tools, RLIMS-P and eFIP, for the retrieval and extraction of kinase-substrate-site data and phosphorylation-dependent PPIs from the literature. These tools offer several advantages over a literature search in PubMed as their results are specific for phosphorylation. RLIMS-P and eFIP results can be sorted, organized, and viewed in multiple ways to answer relevant biological questions, and the protein mentions are linked to UniProt identifiers.

  9. CHIPMUNK: A Virtual Synthesizable Small-Molecule Library for Medicinal Chemistry, Exploitable for Protein-Protein Interaction Modulators.

    Science.gov (United States)

    Humbeck, Lina; Weigang, Sebastian; Schäfer, Till; Mutzel, Petra; Koch, Oliver

    2018-03-20

    A common issue during drug design and development is the discovery of novel scaffolds for protein targets. On the one hand the chemical space of purchasable compounds is rather limited; on the other hand artificially generated molecules suffer from a grave lack of accessibility in practice. Therefore, we generated a novel virtual library of small molecules which are synthesizable from purchasable educts, called CHIPMUNK (CHemically feasible In silico Public Molecular UNiverse Knowledge base). Altogether, CHIPMUNK covers over 95 million compounds and encompasses regions of the chemical space that are not covered by existing databases. The coverage of CHIPMUNK exceeds the chemical space spanned by the Lipinski rule of five to foster the exploration of novel and difficult target classes. The analysis of the generated property space reveals that CHIPMUNK is well suited for the design of protein-protein interaction inhibitors (PPIIs). Furthermore, a recently developed structural clustering algorithm (StruClus) for big data was used to partition the sub-libraries into meaningful subsets and assist scientists to process the large amount of data. These clustered subsets also contain the target space based on ChEMBL data which was included during clustering. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  10. Characterizing genes with distinct methylation patterns in the context of protein-protein interaction network: application to human brain tissues.

    Science.gov (United States)

    Li, Yongsheng; Xu, Juan; Chen, Hong; Zhao, Zheng; Li, Shengli; Bai, Jing; Wu, Aiwei; Jiang, Chunjie; Wang, Yuan; Su, Bin; Li, Xia

    2013-01-01

    DNA methylation is an essential epigenetic mechanism involved in transcriptional control. However, how genes with different methylation patterns are assembled in the protein-protein interaction network (PPIN) remains a mystery. In the present study, we systematically dissected the characterization of genes with different methylation patterns in the PPIN. A negative association was detected between the methylation levels in the brain tissues and topological centralities. By focusing on two classes of genes with considerably different methylation levels in the brain tissues, namely the low methylated genes (LMGs) and high methylated genes (HMGs), we found that their organizing principles in the PPIN are distinct. The LMGs tend to be the center of the PPIN, and attacking them causes a more deleterious effect on the network integrity. Furthermore, the LMGs express their functions in a modular pattern and substantial differences in functions are observed between the two types of genes. The LMGs are enriched in the basic biological functions, such as binding activity and regulation of transcription. More importantly, cancer genes, especially recessive cancer genes, essential genes, and aging-related genes were all found more often in the LMGs. Additionally, our analysis presented that the intra-classes communications are enhanced, but inter-classes communications are repressed. Finally, a functional complementation was revealed between methylation and miRNA regulation in the human genome. We have elucidated the assembling principles of genes with different methylation levels in the context of the PPIN, providing key insights into the complex epigenetic regulation mechanisms.

  11. Prioritizing disease candidate proteins in cardiomyopathy-specific protein-protein interaction networks based on "guilt by association" analysis.

    Directory of Open Access Journals (Sweden)

    Wan Li

    Full Text Available The cardiomyopathies are a group of heart muscle diseases which can be inherited (familial. Identifying potential disease-related proteins is important to understand mechanisms of cardiomyopathies. Experimental identification of cardiomyophthies is costly and labour-intensive. In contrast, bioinformatics approach has a competitive advantage over experimental method. Based on "guilt by association" analysis, we prioritized candidate proteins involving in human cardiomyopathies. We first built weighted human cardiomyopathy-specific protein-protein interaction networks for three subtypes of cardiomyopathies using the known disease proteins from Online Mendelian Inheritance in Man as seeds. We then developed a method in prioritizing disease candidate proteins to rank candidate proteins in the network based on "guilt by association" analysis. It was found that most candidate proteins with high scores shared disease-related pathways with disease seed proteins. These top ranked candidate proteins were related with the corresponding disease subtypes, and were potential disease-related proteins. Cross-validation and comparison with other methods indicated that our approach could be used for the identification of potentially novel disease proteins, which may provide insights into cardiomyopathy-related mechanisms in a more comprehensive and integrated way.

  12. Mining disease genes using integrated protein-protein interaction and gene-gene co-regulation information.

    Science.gov (United States)

    Li, Jin; Wang, Limei; Guo, Maozu; Zhang, Ruijie; Dai, Qiguo; Liu, Xiaoyan; Wang, Chunyu; Teng, Zhixia; Xuan, Ping; Zhang, Mingming

    2015-01-01

    In humans, despite the rapid increase in disease-associated gene discovery, a large proportion of disease-associated genes are still unknown. Many network-based approaches have been used to prioritize disease genes. Many networks, such as the protein-protein interaction (PPI), KEGG, and gene co-expression networks, have been used. Expression quantitative trait loci (eQTLs) have been successfully applied for the determination of genes associated with several diseases. In this study, we constructed an eQTL-based gene-gene co-regulation network (GGCRN) and used it to mine for disease genes. We adopted the random walk with restart (RWR) algorithm to mine for genes associated with Alzheimer disease. Compared to the Human Protein Reference Database (HPRD) PPI network alone, the integrated HPRD PPI and GGCRN networks provided faster convergence and revealed new disease-related genes. Therefore, using the RWR algorithm for integrated PPI and GGCRN is an effective method for disease-associated gene mining.

  13. Identification of Small Molecule Translesion Synthesis Inhibitors That Target the Rev1-CT/RIR Protein-Protein Interaction.

    Science.gov (United States)

    Sail, Vibhavari; Rizzo, Alessandro A; Chatterjee, Nimrat; Dash, Radha C; Ozen, Zuleyha; Walker, Graham C; Korzhnev, Dmitry M; Hadden, M Kyle

    2017-07-21

    Translesion synthesis (TLS) is an important mechanism through which proliferating cells tolerate DNA damage during replication. The mutagenic Rev1/Polζ-dependent branch of TLS helps cancer cells survive first-line genotoxic chemotherapy and introduces mutations that can contribute to the acquired resistance so often observed with standard anticancer regimens. As such, inhibition of Rev1/Polζ-dependent TLS has recently emerged as a strategy to enhance the efficacy of first-line chemotherapy and reduce the acquisition of chemoresistance by decreasing tumor mutation rate. The TLS DNA polymerase Rev1 serves as an integral scaffolding protein that mediates the assembly of the active multiprotein TLS complexes. Protein-protein interactions (PPIs) between the C-terminal domain of Rev1 (Rev1-CT) and the Rev1-interacting region (RIR) of other TLS DNA polymerases play an essential role in regulating TLS activity. To probe whether disrupting the Rev1-CT/RIR PPI is a valid approach for developing a new class of targeted anticancer agents, we designed a fluorescence polarization-based assay that was utilized in a pilot screen for small molecule inhibitors of this PPI. Two small molecule scaffolds that disrupt this interaction were identified, and secondary validation assays confirmed that compound 5 binds to Rev1-CT at the RIR interface. Finally, survival and mutagenesis assays in mouse embryonic fibroblasts and human fibrosarcoma HT1080 cells treated with cisplatin and ultraviolet light indicate that these compounds inhibit mutagenic Rev1/Polζ-dependent TLS in cells, validating the Rev1-CT/RIR PPI for future anticancer drug discovery and identifying the first small molecule inhibitors of TLS that target Rev1-CT.

  14. The Prediction of Key Cytoskeleton Components Involved in Glomerular Diseases Based on a Protein-Protein Interaction Network.

    Science.gov (United States)

    Ding, Fangrui; Tan, Aidi; Ju, Wenjun; Li, Xuejuan; Li, Shao; Ding, Jie

    2016-01-01

    Maintenance of the physiological morphologies of different types of cells and tissues is essential for the normal functioning of each system in the human body. Dynamic variations in cell and tissue morphologies depend on accurate adjustments of the cytoskeletal system. The cytoskeletal system in the glomerulus plays a key role in the normal process of kidney filtration. To enhance the understanding of the possible roles of the cytoskeleton in glomerular diseases, we constructed the Glomerular Cytoskeleton Network (GCNet), which shows the protein-protein interaction network in the glomerulus, and identified several possible key cytoskeletal components involved in glomerular diseases. In this study, genes/proteins annotated to the cytoskeleton were detected by Gene Ontology analysis, and glomerulus-enriched genes were selected from nine available glomerular expression datasets. Then, the GCNet was generated by combining these two sets of information. To predict the possible key cytoskeleton components in glomerular diseases, we then examined the common regulation of the genes in GCNet in the context of five glomerular diseases based on their transcriptomic data. As a result, twenty-one cytoskeleton components as potential candidate were highlighted for consistently down- or up-regulating in all five glomerular diseases. And then, these candidates were examined in relation to existing known glomerular diseases and genes to determine their possible functions and interactions. In addition, the mRNA levels of these candidates were also validated in a puromycin aminonucleoside(PAN) induced rat nephropathy model and were also matched with existing Diabetic Nephropathy (DN) transcriptomic data. As a result, there are 15 of 21 candidates in PAN induced nephropathy model were consistent with our predication and also 12 of 21 candidates were matched with differentially expressed genes in the DN transcriptomic data. By providing a novel interaction network and prediction, GCNet

  15. Setting up a Bioluminescence Resonance Energy Transfer high throughput screening assay to search for protein/protein interaction inhibitors in mammalian cells.

    Directory of Open Access Journals (Sweden)

    Cyril eCouturier

    2012-09-01

    Full Text Available Each step of the cell life and its response or adaptation to its environment are mediated by a network of protein/protein interactions termed interactome. Our knowledge of this network keeps growing due to the development of sensitive techniques devoted to study these interactions. The bioluminescence resonance energy transfer (BRET technique was primarily developed to allow the dynamic monitoring of protein-protein interactions in living cells, and has widely been used to study receptor activation by intra- or extra-molecular conformational changes within receptors and activated complexes in mammal cells. Some interactions are described as crucial in human pathological processes, and a new class of drugs targeting them has recently emerged. The BRET method is well suited to identify inhibitors of protein-protein interactions and here is described why and how to set up and optimize a High Throughput Screening assay based on BRET to search for such inhibitory compounds. The different parameters to take into account when developing such BRET assays in mammal cells are reviewed to give general guidelines: considerations on the targeted interaction, choice of BRET version, inducibility of the interaction, kinetic of the monitored interaction, and of the BRET reading, influence substrate concentration, number of cells and medium composition used on the Z’ factor, and expected interferences for colored or fluorescent compounds.

  16. Stringent DDI-based prediction of H. sapiens-M. tuberculosis H37Rv protein-protein interactions.

    Science.gov (United States)

    Zhou, Hufeng; Rezaei, Javad; Hugo, Willy; Gao, Shangzhi; Jin, Jingjing; Fan, Mengyuan; Yong, Chern-Han; Wozniak, Michal; Wong, Limsoon

    2013-01-01

    H. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are very important information to illuminate the infection mechanism of M. tuberculosis H37Rv. But current H. sapiens-M. tuberculosis H37Rv PPI data are very scarce. This seriously limits the study of the interaction between this important pathogen and its host H. sapiens. Computational prediction of H. sapiens-M. tuberculosis H37Rv PPIs is an important strategy to fill in the gap. Domain-domain interaction (DDI) based prediction is one of the frequently used computational approaches in predicting both intra-species and inter-species PPIs. However, the performance of DDI-based host-pathogen PPI prediction has been rather limited. We develop a stringent DDI-based prediction approach with emphasis on (i) differences between the specific domain sequences on annotated regions of proteins under the same domain ID and (ii) calculation of the interaction strength of predicted PPIs based on the interacting residues in their interaction interfaces. We compare our stringent DDI-based approach to a conventional DDI-based approach for predicting PPIs based on gold standard intra-species PPIs and coherent informative Gene Ontology terms assessment. The assessment results show that our stringent DDI-based approach achieves much better performance in predicting PPIs than the conventional approach. Using our stringent DDI-based approach, we have predicted a small set of reliable H. sapiens-M. tuberculosis H37Rv PPIs which could be very useful for a variety of related studies. We also analyze the H. sapiens-M. tuberculosis H37Rv PPIs predicted by our stringent DDI-based approach using cellular compartment distribution analysis, functional category enrichment analysis and pathway enrichment analysis. The analyses support the validity of our prediction result. Also, based on an analysis of the H. sapiens-M. tuberculosis H37Rv PPI network predicted by our stringent DDI-based approach, we have discovered some

  17. Protein-protein interactions

    DEFF Research Database (Denmark)

    Byron, Olwyn; Vestergaard, Bente

    2015-01-01

    . The biophysical and structural investigations of PPIs consequently demand hybrid approaches, implementing orthogonal methods and strategies for global data analysis. Currently, impressive developments in hardware and software within several methodologies define a new era for the biostructural community. Data can...

  18. An efficient heuristic method for active feature acquisition and its application to protein-protein interaction prediction

    Directory of Open Access Journals (Sweden)

    Thahir Mohamed

    2012-11-01

    Full Text Available Abstract Background Machine learning approaches for classification learn the pattern of the feature space of different classes, or learn a boundary that separates the feature space into different classes. The features of the data instances are usually available, and it is only the class-labels of the instances that are unavailable. For example, to classify text documents into different topic categories, the words in the documents are features and they are readily available, whereas the topic is what is predicted. However, in some domains obtaining features may be resource-intensive because of which not all features may be available. An example is that of protein-protein interaction prediction, where not only are the labels ('interacting' or 'non-interacting' unavailable, but so are some of the features. It may be possible to obtain at least some of the missing features by carrying out a few experiments as permitted by the available resources. If only a few experiments can be carried out to acquire missing features, which proteins should be studied and which features of those proteins should be determined? From the perspective of machine learning for PPI prediction, it would be desirable that those features be acquired which when used in training the classifier, the accuracy of the classifier is improved the most. That is, the utility of the feature-acquisition is measured in terms of how much acquired features contribute to improving the accuracy of the classifier. Active feature acquisition (AFA is a strategy to preselect such instance-feature combinations (i.e. protein and experiment combinations for maximum utility. The goal of AFA is the creation of optimal training set that would result in the best classifier, and not in determining the best classification model itself. Results We present a heuristic method for active feature acquisition to calculate the utility of acquiring a missing feature. This heuristic takes into account the change in

  19. Physics challenges in the strong interactions

    Energy Technology Data Exchange (ETDEWEB)

    Ellis, S.D.

    1991-01-01

    An overview of the challenges to be faced in the area of the strong interactions during the 1990`s is presented. As an illustrative example special attention is given to the analysis of jets as studied at hadron colliders.

  20. Physics challenges in the strong interactions

    International Nuclear Information System (INIS)

    Ellis, S.D.

    1991-01-01

    An overview of the challenges to be faced in the area of the strong interactions during the 1990's is presented. As an illustrative example special attention is given to the analysis of jets as studied at hadron colliders

  1. Strong interaction effects in hadronic atoms

    International Nuclear Information System (INIS)

    Kaufmann, W.B.

    1977-01-01

    The WKB method is applied to the calculation of strong interaction-induced level widths and shifts of hadronic atoms. The calculation, while elementary enough for undergraduate quantum mechanics students, gives a good account of kaonic and antiprotonic atom data

  2. The Charm and Beauty of Strong Interactions

    Science.gov (United States)

    El-Bennich, Bruno

    2018-01-01

    We briefly review common features and overlapping issues in hadron and flavor physics focussing on continuum QCD approaches to heavy bound states, their mass spectrum and weak decay constants in different strong interaction models.

  3. Structure-based drug design, synthesis and biological assays of P. falciparum Atg3-Atg8 protein-protein interaction inhibitors

    Science.gov (United States)

    Villa, Stefania; Legnani, Laura; Colombo, Diego; Gelain, Arianna; Lammi, Carmen; Bongiorno, Daniele; Ilboudo, Denise P.; McGee, Kellen E.; Bosch, Jürgen; Grazioso, Giovanni

    2018-03-01

    The proteins involved in the autophagy (Atg) pathway have recently been considered promising targets for the development of new antimalarial drugs. In particular, inhibitors of the protein-protein interaction (PPI) between Atg3 and Atg8 of Plasmodium falciparum retarded the blood- and liver-stages of parasite growth. In this paper, we used computational techniques to design a new class of peptidomimetics mimicking the Atg3 interaction motif, which were then synthesized by click-chemistry. Surface plasmon resonance has been employed to measure the ability of these compounds to inhibit the Atg3-Atg8 reciprocal protein-protein interaction. Moreover, P. falciparum growth inhibition in red blood cell cultures was evaluated as well as the cyto-toxicity of the compounds.

  4. Dual field theory of strong interactions

    International Nuclear Information System (INIS)

    Akers, D.

    1987-01-01

    A dual field theory of strong interactions is derived from a Lagrangian of the Yang-Mills and Higgs fields. The existence of a magnetic monopole of mass 2397 MeV and Dirac charge g = (137/2)e is incorporated into the theory. Unification of the strong, weak, and electromagnetic forces is shown to converge at the mass of the intermediate vector boson W/sup +/-/. The coupling constants of the strong and weak interactions are derived in terms of the fine-structure constant α = 1/137

  5. BioC-compatible full-text passage detection for protein-protein interactions using extended dependency graph.

    Science.gov (United States)

    Peng, Yifan; Arighi, Cecilia; Wu, Cathy H; Vijay-Shanker, K

    2016-01-01

    There has been a large growth in the number of biomedical publications that report experimental results. Many of these results concern detection of protein-protein interactions (PPI). In BioCreative V, we participated in the BioC task and developed a PPI system to detect text passages with PPIs in the full-text articles. By adopting the BioC format, the output of the system can be seamlessly added to the biocuration pipeline with little effort required for the system integration. A distinctive feature of our PPI system is that it utilizes extended dependency graph, an intermediate level of representation that attempts to abstract away syntactic variations in text. As a result, we are able to use only a limited set of rules to extract PPI pairs in the sentences, and additional rules to detect additional passages for PPI pairs. For evaluation, we used the 95 articles that were provided for the BioC annotation task. We retrieved the unique PPIs from the BioGRID database for these articles and show that our system achieves a recall of 83.5%. In order to evaluate the detection of passages with PPIs, we further annotated Abstract and Results sections of 20 documents from the dataset and show that an f-value of 80.5% was obtained. To evaluate the generalizability of the system, we also conducted experiments on AIMed, a well-known PPI corpus. We achieved an f-value of 76.1% for sentence detection and an f-value of 64.7% for unique PPI detection.Database URL: http://proteininformationresource.org/iprolink/corpora. © The Author(s) 2016. Published by Oxford University Press.

  6. Predicting protein complexes from weighted protein-protein interaction graphs with a novel unsupervised methodology: Evolutionary enhanced Markov clustering.

    Science.gov (United States)

    Theofilatos, Konstantinos; Pavlopoulou, Niki; Papasavvas, Christoforos; Likothanassis, Spiros; Dimitrakopoulos, Christos; Georgopoulos, Efstratios; Moschopoulos, Charalampos; Mavroudi, Seferina

    2015-03-01

    Proteins are considered to be the most important individual components of biological systems and they combine to form physical protein complexes which are responsible for certain molecular functions. Despite the large availability of protein-protein interaction (PPI) information, not much information is available about protein complexes. Experimental methods are limited in terms of time, efficiency, cost and performance constraints. Existing computational methods have provided encouraging preliminary results, but they phase certain disadvantages as they require parameter tuning, some of them cannot handle weighted PPI data and others do not allow a protein to participate in more than one protein complex. In the present paper, we propose a new fully unsupervised methodology for predicting protein complexes from weighted PPI graphs. The proposed methodology is called evolutionary enhanced Markov clustering (EE-MC) and it is a hybrid combination of an adaptive evolutionary algorithm and a state-of-the-art clustering algorithm named enhanced Markov clustering. EE-MC was compared with state-of-the-art methodologies when applied to datasets from the human and the yeast Saccharomyces cerevisiae organisms. Using public available datasets, EE-MC outperformed existing methodologies (in some datasets the separation metric was increased by 10-20%). Moreover, when applied to new human datasets its performance was encouraging in the prediction of protein complexes which consist of proteins with high functional similarity. In specific, 5737 protein complexes were predicted and 72.58% of them are enriched for at least one gene ontology (GO) function term. EE-MC is by design able to overcome intrinsic limitations of existing methodologies such as their inability to handle weighted PPI networks, their constraint to assign every protein in exactly one cluster and the difficulties they face concerning the parameter tuning. This fact was experimentally validated and moreover, new

  7. PEPSI-Dock: a detailed data-driven protein-protein interaction potential accelerated by polar Fourier correlation.

    Science.gov (United States)

    Neveu, Emilie; Ritchie, David W; Popov, Petr; Grudinin, Sergei

    2016-09-01

    Docking prediction algorithms aim to find the native conformation of a complex of proteins from knowledge of their unbound structures. They rely on a combination of sampling and scoring methods, adapted to different scales. Polynomial Expansion of Protein Structures and Interactions for Docking (PEPSI-Dock) improves the accuracy of the first stage of the docking pipeline, which will sharpen up the final predictions. Indeed, PEPSI-Dock benefits from the precision of a very detailed data-driven model of the binding free energy used with a global and exhaustive rigid-body search space. As well as being accurate, our computations are among the fastest by virtue of the sparse representation of the pre-computed potentials and FFT-accelerated sampling techniques. Overall, this is the first demonstration of a FFT-accelerated docking method coupled with an arbitrary-shaped distance-dependent interaction potential. First, we present a novel learning process to compute data-driven distant-dependent pairwise potentials, adapted from our previous method used for rescoring of putative protein-protein binding poses. The potential coefficients are learned by combining machine-learning techniques with physically interpretable descriptors. Then, we describe the integration of the deduced potentials into a FFT-accelerated spherical sampling provided by the Hex library. Overall, on a training set of 163 heterodimers, PEPSI-Dock achieves a success rate of 91% mid-quality predictions in the top-10 solutions. On a subset of the protein docking benchmark v5, it achieves 44.4% mid-quality predictions in the top-10 solutions when starting from bound structures and 20.5% when starting from unbound structures. The method runs in 5-15 min on a modern laptop and can easily be extended to other types of interactions. https://team.inria.fr/nano-d/software/PEPSI-Dock sergei.grudinin@inria.fr. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e

  8. The BioC-BioGRID corpus: full text articles annotated for curation of protein-protein and genetic interactions.

    Science.gov (United States)

    Islamaj Dogan, Rezarta; Kim, Sun; Chatr-Aryamontri, Andrew; Chang, Christie S; Oughtred, Rose; Rust, Jennifer; Wilbur, W John; Comeau, Donald C; Dolinski, Kara; Tyers, Mike

    2017-01-01

    A great deal of information on the molecular genetics and biochemistry of model organisms has been reported in the scientific literature. However, this data is typically described in free text form and is not readily amenable to computational analyses. To this end, the BioGRID database systematically curates the biomedical literature for genetic and protein interaction data. This data is provided in a standardized computationally tractable format and includes structured annotation of experimental evidence. BioGRID curation necessarily involves substantial human effort by expert curators who must read each publication to extract the relevant information. Computational text-mining methods offer the potential to augment and accelerate manual curation. To facilitate the development of practical text-mining strategies, a new challenge was organized in BioCreative V for the BioC task, the collaborative Biocurator Assistant Task. This was a non-competitive, cooperative task in which the participants worked together to build BioC-compatible modules into an integrated pipeline to assist BioGRID curators. As an integral part of this task, a test collection of full text articles was developed that contained both biological entity annotations (gene/protein and organism/species) and molecular interaction annotations (protein-protein and genetic interactions (PPIs and GIs)). This collection, which we call the BioC-BioGRID corpus, was annotated by four BioGRID curators over three rounds of annotation and contains 120 full text articles curated in a dataset representing two major model organisms, namely budding yeast and human. The BioC-BioGRID corpus contains annotations for 6409 mentions of genes and their Entrez Gene IDs, 186 mentions of organism names and their NCBI Taxonomy IDs, 1867 mentions of PPIs and 701 annotations of PPI experimental evidence statements, 856 mentions of GIs and 399 annotations of GI evidence statements. The purpose, characteristics and possible future

  9. Stringent homology-based prediction of H. sapiens-M. tuberculosis H37Rv protein-protein interactions.

    Science.gov (United States)

    Zhou, Hufeng; Gao, Shangzhi; Nguyen, Nam Ninh; Fan, Mengyuan; Jin, Jingjing; Liu, Bing; Zhao, Liang; Xiong, Geng; Tan, Min; Li, Shijun; Wong, Limsoon

    2014-04-08

    H. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are essential for understanding the infection mechanism of the formidable pathogen M. tuberculosis H37Rv. Computational prediction is an important strategy to fill the gap in experimental H. sapiens-M. tuberculosis H37Rv PPI data. Homology-based prediction is frequently used in predicting both intra-species and inter-species PPIs. However, some limitations are not properly resolved in several published works that predict eukaryote-prokaryote inter-species PPIs using intra-species template PPIs. We develop a stringent homology-based prediction approach by taking into account (i) differences between eukaryotic and prokaryotic proteins and (ii) differences between inter-species and intra-species PPI interfaces. We compare our stringent homology-based approach to a conventional homology-based approach for predicting host-pathogen PPIs, based on cellular compartment distribution analysis, disease gene list enrichment analysis, pathway enrichment analysis and functional category enrichment analysis. These analyses support the validity of our prediction result, and clearly show that our approach has better performance in predicting H. sapiens-M. tuberculosis H37Rv PPIs. Using our stringent homology-based approach, we have predicted a set of highly plausible H. sapiens-M. tuberculosis H37Rv PPIs which might be useful for many of related studies. Based on our analysis of the H. sapiens-M. tuberculosis H37Rv PPI network predicted by our stringent homology-based approach, we have discovered several interesting properties which are reported here for the first time. We find that both host proteins and pathogen proteins involved in the host-pathogen PPIs tend to be hubs in their own intra-species PPI network. Also, both host and pathogen proteins involved in host-pathogen PPIs tend to have longer primary sequence, tend to have more domains, tend to be more hydrophilic, etc. And the protein domains from both

  10. ALDH16A1 is a novel non-catalytic enzyme that may be involved in the etiology of gout via protein-protein interactions with HPRT1.

    Science.gov (United States)

    Vasiliou, Vasilis; Sandoval, Monica; Backos, Donald S; Jackson, Brian C; Chen, Ying; Reigan, Philip; Lanaspa, Miguel A; Johnson, Richard J; Koppaka, Vindhya; Thompson, David C

    2013-02-25

    Gout, a common form of inflammatory arthritis, is strongly associated with elevated uric acid concentrations in the blood (hyperuricemia). A recent study in Icelanders identified a rare missense single nucleotide polymorphism (SNP) in the ALDH16A1 gene, ALDH16A1*2, to be associated with gout and serum uric acid levels. ALDH16A1 is a novel and rather unique member of the ALDH superfamily in relation to its gene and protein structures. ALDH16 genes are present in fish, amphibians, protista, bacteria but absent from archaea, fungi and plants. In most mammalian species, two ALDH16A1 spliced variants (ALDH16A1, long form and ALDH16A1_v2, short form) have been identified and both are expressed in HepG-2, HK-2 and HK-293 human cell lines. The ALDH16 proteins contain two ALDH domains (as opposed to one in the other members of the superfamily), four transmembrane and one coiled-coil domains. The active site of ALDH16 proteins from bacterial, frog and lower animals contain the catalytically important cysteine residue (Cys-302); this residue is absent from the mammalian and fish orthologs. Molecular modeling predicts that both the short and long forms of human ALDH16A1 protein would lack catalytic activity but may interact with the hypoxanthine-guanine phosphoribosyltransferase (HPRT1) protein, a key enzyme involved in uric acid metabolism and gout. Interestingly, such protein-protein interactions with HPRT1 are predicted to be impaired for the long or short forms of ALDH16A1*2. These results lead to the intriguing possibility that association between ALDH16A1 and HPRT1 may be required for optimal HPRT activity with disruption of this interaction possibly contributing to the hyperuricemia seen in ALDH16A1*2 carriers. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  11. Stoichiometric balance of protein copy numbers is measurable and functionally significant in a protein-protein interaction network for yeast endocytosis.

    Science.gov (United States)

    Holland, David O; Johnson, Margaret E

    2018-03-01

    Stoichiometric balance, or dosage balance, implies that proteins that are subunits of obligate complexes (e.g. the ribosome) should have copy numbers expressed to match their stoichiometry in that complex. Establishing balance (or imbalance) is an important tool for inferring subunit function and assembly bottlenecks. We show here that these correlations in protein copy numbers can extend beyond complex subunits to larger protein-protein interactions networks (PPIN) involving a range of reversible binding interactions. We develop a simple method for quantifying balance in any interface-resolved PPINs based on network structure and experimentally observed protein copy numbers. By analyzing such a network for the clathrin-mediated endocytosis (CME) system in yeast, we found that the real protein copy numbers were significantly more balanced in relation to their binding partners compared to randomly sampled sets of yeast copy numbers. The observed balance is not perfect, highlighting both under and overexpressed proteins. We evaluate the potential cost and benefits of imbalance using two criteria. First, a potential cost to imbalance is that 'leftover' proteins without remaining functional partners are free to misinteract. We systematically quantify how this misinteraction cost is most dangerous for strong-binding protein interactions and for network topologies observed in biological PPINs. Second, a more direct consequence of imbalance is that the formation of specific functional complexes depends on relative copy numbers. We therefore construct simple kinetic models of two sub-networks in the CME network to assess multi-protein assembly of the ARP2/3 complex and a minimal, nine-protein clathrin-coated vesicle forming module. We find that the observed, imperfectly balanced copy numbers are less effective than balanced copy numbers in producing fast and complete multi-protein assemblies. However, we speculate that strategic imbalance in the vesicle forming module

  12. Enabling systematic interrogation of protein-protein interactions in live cells with a versatile ultra-high-throughput biosensor platform | Office of Cancer Genomics

    Science.gov (United States)

    The vast datasets generated by next generation gene sequencing and expression profiling have transformed biological and translational research. However, technologies to produce large-scale functional genomics datasets, such as high-throughput detection of protein-protein interactions (PPIs), are still in early development. While a number of powerful technologies have been employed to detect PPIs, a singular PPI biosensor platform featured with both high sensitivity and robustness in a mammalian cell environment remains to be established.

  13. A theory of the strong interactions

    International Nuclear Information System (INIS)

    Gross, D.J.

    1979-01-01

    The most promising candidate for a fundamental microscopic theory of the strong interactions is a gauge theory of colored quarks-Quantum Chromodynamics (QCD). There are many excellent reasons for believing in this theory. It embodies the broken symmetries, SU(3) and chiral SU(3)xSU(3), of the strong interactions and reflects the success of (albeit crude) quark models in explaining the spectrum of the observed hadrons. The hidden quantum number of color, necessary to account for the quantum numbers of the low lying hadrons, plays a fundamental role in this theory as the SU(3) color gauge vector 'gluons' are the mediators of the strong interactions. The absence of physical quark states can be 'explained' by the hypothesis of color confinement i.e. that hadrons are permanently bound in color singlet bound states. Finally this theory is unique in being asymptotically free, thus accounting for the almost free field theory behvior of quarks observed at short distances. (Auth.)

  14. Interaction of strong electromagnetic fields with atoms

    International Nuclear Information System (INIS)

    Brandi, H.S.; Davidovich, L.; Zagury, N.

    1982-06-01

    Several non-linear processes involvoing the interaction of atoms with strong laser fields are discussed, with particular emphasis on the ionization problem. Non-perturbative methods which have been proposed to tackle this problem are analysed, and shown to correspond to an expansion in the intra-atomic potential. The relation between tunneling and multiphoton absorption as ionization mechanisms, and the generalization of Einstein's photoelectric equation to the strong-field case are discussed. (Author) [pt

  15. Topological and organizational properties of the products of house-keeping and tissue-specific genes in protein-protein interaction networks.

    Science.gov (United States)

    Lin, Wen-Hsien; Liu, Wei-Chung; Hwang, Ming-Jing

    2009-03-11

    Human cells of various tissue types differ greatly in morphology despite having the same set of genetic information. Some genes are expressed in all cell types to perform house-keeping functions, while some are selectively expressed to perform tissue-specific functions. In this study, we wished to elucidate how proteins encoded by human house-keeping genes and tissue-specific genes are organized in human protein-protein interaction networks. We constructed protein-protein interaction networks for different tissue types using two gene expression datasets and one protein-protein interaction database. We then calculated three network indices of topological importance, the degree, closeness, and betweenness centralities, to measure the network position of proteins encoded by house-keeping and tissue-specific genes, and quantified their local connectivity structure. Compared to a random selection of proteins, house-keeping gene-encoded proteins tended to have a greater number of directly interacting neighbors and occupy network positions in several shortest paths of interaction between protein pairs, whereas tissue-specific gene-encoded proteins did not. In addition, house-keeping gene-encoded proteins tended to connect with other house-keeping gene-encoded proteins in all tissue types, whereas tissue-specific gene-encoded proteins also tended to connect with other tissue-specific gene-encoded proteins, but only in approximately half of the tissue types examined. Our analysis showed that house-keeping gene-encoded proteins tend to occupy important network positions, while those encoded by tissue-specific genes do not. The biological implications of our findings were discussed and we proposed a hypothesis regarding how cells organize their protein tools in protein-protein interaction networks. Our results led us to speculate that house-keeping gene-encoded proteins might form a core in human protein-protein interaction networks, while clusters of tissue-specific gene

  16. Design, synthesis, and validation of a β-turn mimetic library targeting protein-protein and peptide-receptor interactions.

    Science.gov (United States)

    Whitby, Landon R; Ando, Yoshio; Setola, Vincent; Vogt, Peter K; Roth, Bryan L; Boger, Dale L

    2011-07-06

    The design and synthesis of a β-turn mimetic library as a key component of a small-molecule library targeting the major recognition motifs involved in protein-protein interactions is described. Analysis of a geometric characterization of 10,245 β-turns in the protein data bank (PDB) suggested that trans-pyrrolidine-3,4-dicarboxamide could serve as an effective and synthetically accessible library template. This was confirmed by initially screening select compounds against a series of peptide-activated GPCRs that recognize a β-turn structure in their endogenous ligands. This validation study was highlighted by identification of both nonbasic and basic small molecules with high affinities (K(i) = 390 and 23 nM, respectively) for the κ-opioid receptor (KOR). Consistent with the screening capabilities of collaborators and following the design validation, the complete library was assembled as 210 mixtures of 20 compounds, providing a total of 4200 compounds designed to mimic all possible permutations of 3 of the 4 residues in a naturally occurring β-turn. Unique to the design and because of the C(2) symmetry of the template, a typical 20 × 20 × 20-mix (8000 compounds prepared as 400 mixtures of 20 compounds) needed to represent 20 variations in the side chains of three amino acid residues reduces to a 210 × 20-mix, thereby simplifying the library synthesis and subsequent screening. The library was prepared using a solution-phase synthetic protocol with liquid-liquid or liquid-solid extractions for purification and conducted on a scale that insures its long-term availability for screening campaigns. Screening the library against the human opioid receptors (KOR, MOR, and DOR) identified not only the activity of library members expected to mimic the opioid receptor peptide ligands but also additional side-chain combinations that provided enhanced receptor binding selectivities (>100-fold) and affinities (as low as K(i) = 80 nM for KOR). A key insight to emerge from

  17. Investigation and identification of functional post-translational modification sites associated with drug binding and protein-protein interactions.

    Science.gov (United States)

    Su, Min-Gang; Weng, Julia Tzu-Ya; Hsu, Justin Bo-Kai; Huang, Kai-Yao; Chi, Yu-Hsiang; Lee, Tzong-Yi

    2017-12-21

    Protein post-translational modification (PTM) plays an essential role in various cellular processes that modulates the physical and chemical properties, folding, conformation, stability and activity of proteins, thereby modifying the functions of proteins. The improved throughput of mass spectrometry (MS) or MS/MS technology has not only brought about a surge in proteome-scale studies, but also contributed to a fruitful list of identified PTMs. However, with the increase in the number of identified PTMs, perhaps the more crucial question is what kind of biological mechanisms these PTMs are involved in. This is particularly important in light of the fact that most protein-based pharmaceuticals deliver their therapeutic effects through some form of PTM. Yet, our understanding is still limited with respect to the local effects and frequency of PTM sites near pharmaceutical binding sites and the interfaces of protein-protein interaction (PPI). Understanding PTM's function is critical to our ability to manipulate the biological mechanisms of protein. In this study, to understand the regulation of protein functions by PTMs, we mapped 25,835 PTM sites to proteins with available three-dimensional (3D) structural information in the Protein Data Bank (PDB), including 1785 modified PTM sites on the 3D structure. Based on the acquired structural PTM sites, we proposed to use five properties for the structural characterization of PTM substrate sites: the spatial composition of amino acids, residues and side-chain orientations surrounding the PTM substrate sites, as well as the secondary structure, division of acidity and alkaline residues, and solvent-accessible surface area. We further mapped the structural PTM sites to the structures of drug binding and PPI sites, identifying a total of 1917 PTM sites that may affect PPI and 3951 PTM sites associated with drug-target binding. An integrated analytical platform (CruxPTM), with a variety of methods and online molecular docking

  18. Simple and efficient machine learning frameworks for identifying protein-protein interaction relevant articles and experimental methods used to study the interactions.

    Science.gov (United States)

    Agarwal, Shashank; Liu, Feifan; Yu, Hong

    2011-10-03

    Protein-protein interaction (PPI) is an important biomedical phenomenon. Automatically detecting PPI-relevant articles and identifying methods that are used to study PPI are important text mining tasks. In this study, we have explored domain independent features to develop two open source machine learning frameworks. One performs binary classification to determine whether the given article is PPI relevant or not, named "Simple Classifier", and the other one maps the PPI relevant articles with corresponding interaction method nodes in a standardized PSI-MI (Proteomics Standards Initiative-Molecular Interactions) ontology, named "OntoNorm". We evaluated our system in the context of BioCreative challenge competition using the standardized data set. Our systems are amongst the top systems reported by the organizers, attaining 60.8% F1-score for identifying relevant documents, and 52.3% F1-score for mapping articles to interaction method ontology. Our results show that domain-independent machine learning frameworks can perform competitively well at the tasks of detecting PPI relevant articles and identifying the methods that were used to study the interaction in such articles. Simple Classifier is available at http://sourceforge.net/p/simpleclassify/home/ and OntoNorm at http://sourceforge.net/p/ontonorm/home/.

  19. Mimicking protein-protein interactions through peptide-peptide interactions: HIV-1 gp120 and CXCR4

    Directory of Open Access Journals (Sweden)

    Andrea eGross

    2013-09-01

    Full Text Available We have recently designed a soluble synthetic peptide that functionally mimics the HIV-1 coreceptor CXCR4, which is a chemokine receptor that belongs to the family of seven-transmembrane GPCRs. This CXCR4 mimetic peptide, termed CX4-M1, presents the three extracellular loops (ECLs of the receptor. In binding assays involving recombinant proteins, as well as in cellular infection assays, CX4-M1 was found to selectively recognize gp120 from HIV-1 strains that use CXCR4 for cell entry (X4 tropic HIV-1. Furthermore, anti-HIV-1 antibodies modulate this interaction in a molecular mechanism related to that of their impact on the gp120-CXCR4 interaction. We could now show that the selectivity of CX4-M1 pertains not only to gp120 from X4 tropic HIV-1, but also to synthetic peptides presenting the V3 loops of these gp120 proteins. The V3 loop is thought to be an essential part of the coreceptor binding site of gp120 that contacts the second ECL of the coreceptor. We were able to experimentally confirm this notion in binding assays using substitution analogs of CX4-M1 and the V3 loop peptides, respectively, as well as in cellular infection assays. These results indicate that interactions of the HIV-1 Env with coreceptors can be mimicked by synthetic peptides, which may be useful to explore these interactions at the molecular level in more detail.

  20. Physics challenges in the strong interactions

    Energy Technology Data Exchange (ETDEWEB)

    Ellis, S.D.

    1991-01-01

    An overview of the challenges to be faced in the area of the strong interactions during the 1990's is presented. As an illustrative example special attention is given to the analysis of jets as studied at hadron colliders.

  1. Protein-protein interaction site prediction in Homo sapiens and E. coli using an interaction-affinity based membership function in fuzzy SVM.

    Science.gov (United States)

    Sriwastava, Brijesh Kumar; Basu, Subhadip; Maulik, Ujjwal

    2015-10-01

    Protein-protein interaction (PPI) site prediction aids to ascertain the interface residues that participate in interaction processes. Fuzzy support vector machine (F-SVM) is proposed as an effective method to solve this problem, and we have shown that the performance of the classical SVM can be enhanced with the help of an interaction-affinity based fuzzy membership function. The performances of both SVM and F-SVM on the PPI databases of the Homo sapiens and E. coli organisms are evaluated and estimated the statistical significance of the developed method over classical SVM and other fuzzy membership-based SVM methods available in the literature. Our membership function uses the residue-level interaction affinity scores for each pair of positive and negative sequence fragments. The average AUC scores in the 10-fold cross-validation experiments are measured as 79.94% and 80.48% for the Homo sapiens and E. coli organisms respectively. On the independent test datasets, AUC scores are obtained as 76.59% and 80.17% respectively for the two organisms. In almost all cases, the developed F-SVM method improves the performances obtained by the corresponding classical SVM and the other classifiers, available in the literature.

  2. Development of a Model Protein Interaction Pair as a Benchmarking Tool for the Quantitative Analysis of 2-Site Protein-Protein Interactions.

    Science.gov (United States)

    Yamniuk, Aaron P; Newitt, John A; Doyle, Michael L; Arisaka, Fumio; Giannetti, Anthony M; Hensley, Preston; Myszka, David G; Schwarz, Fred P; Thomson, James A; Eisenstein, Edward

    2015-12-01

    A significant challenge in the molecular interaction field is to accurately determine the stoichiometry and stepwise binding affinity constants for macromolecules having >1 binding site. The mission of the Molecular Interactions Research Group (MIRG) of the Association of Biomolecular Resource Facilities (ABRF) is to show how biophysical technologies are used to quantitatively characterize molecular interactions, and to educate the ABRF members and scientific community on the utility and limitations of core technologies [such as biosensor, microcalorimetry, or analytic ultracentrifugation (AUC)]. In the present work, the MIRG has developed a robust model protein interaction pair consisting of a bivalent variant of the Bacillus amyloliquefaciens extracellular RNase barnase and a variant of its natural monovalent intracellular inhibitor protein barstar. It is demonstrated that this system can serve as a benchmarking tool for the quantitative analysis of 2-site protein-protein interactions. The protein interaction pair enables determination of precise binding constants for the barstar protein binding to 2 distinct sites on the bivalent barnase binding partner (termed binase), where the 2 binding sites were engineered to possess affinities that differed by 2 orders of magnitude. Multiple MIRG laboratories characterized the interaction using isothermal titration calorimetry (ITC), AUC, and surface plasmon resonance (SPR) methods to evaluate the feasibility of the system as a benchmarking model. Although general agreement was seen for the binding constants measured using solution-based ITC and AUC approaches, weaker affinity was seen for surface-based method SPR, with protein immobilization likely affecting affinity. An analysis of the results from multiple MIRG laboratories suggests that the bivalent barnase-barstar system is a suitable model for benchmarking new approaches for the quantitative characterization of complex biomolecular interactions.

  3. Novel fusion protein approach for efficient high-throughput screening of small molecule-mediating protein-protein interactions in cells and living animals.

    Science.gov (United States)

    Paulmurugan, Ramasamy; Gambhir, Sanjiv S

    2005-08-15

    Networks of protein interactions execute many different intracellular pathways. Small molecules either synthesized within the cell or obtained from the external environment mediate many of these protein-protein interactions. The study of these small molecule-mediated protein-protein interactions is important in understanding abnormal signal transduction pathways in a variety of disorders, as well as in optimizing the process of drug development and validation. In this study, we evaluated the rapamycin-mediated interaction of the human proteins FK506-binding protein (FKBP12) rapamycin-binding domain (FRB) and FKBP12 by constructing a fusion of these proteins with a split-Renilla luciferase or a split enhanced green fluorescent protein (split-EGFP) such that complementation of the reporter fragments occurs in the presence of rapamycin. Different linker peptides in the fusion protein were evaluated for the efficient maintenance of complemented reporter activity. This system was studied in both cell culture and xenografts in living animals. We found that peptide linkers with two or four EAAAR repeat showed higher protein-protein interaction-mediated signal with lower background signal compared with having no linker or linkers with amino acid sequences GGGGSGGGGS, ACGSLSCGSF, and ACGSLSCGSFACGSLSCGSF. A 9 +/- 2-fold increase in signal intensity both in cell culture and in living mice was seen compared with a system that expresses both reporter fragments and the interacting proteins separately. In this fusion system, rapamycin induced heterodimerization of the FRB and FKBP12 moieties occurred rapidly even at very lower concentrations (0.00001 nmol/L) of rapamycin. For a similar fusion system employing split-EGFP, flow cytometry analysis showed significant level of rapamycin-induced complementation.

  4. Unification of electromagnetic, strong and weak interaction

    International Nuclear Information System (INIS)

    Duong Van Phi; Duong Anh Duc

    1993-09-01

    The Unification of Electromagnetic, Strong and Weak Interactions is realized in the framework of the Quantum Field Theory, established in an 8-dimensional Unified Space. Two fundamental, spinor and vector field equations are considered. The first of the matter particles and the second is of the gauge particles. Interaction Lagrangians are formed from the external and internal currents and the external and internal vector field operators. Generators of the local gauge transformations are the combinations of the matrices of the first field equation. (author). 15 refs

  5. Super symmetry in strong and weak interactions

    International Nuclear Information System (INIS)

    Seshavatharam, U.V.S.; Lakshminarayana, S.

    2010-01-01

    For strong interaction two new fermion mass units 105.32 MeV and 11450 MeV are assumed. Existence of "Integral charge quark bosons", "Integral charge effective quark fermions", "Integral charge (effective) quark fermi-gluons" and "Integral charge quark boso-gluons" are assumed and their masses are estimated. It is noticed that, characteristic nuclear charged fermion is X s · 105.32 = 938.8 MeV and corresponding charged boson is X s (105.32/x) = 415.0 where X s = 8.914 is the inverse of the strong coupling constant and x = 2.26234 is a new number by using which "super symmetry" can be seen in "strong and weak" interactions. 11450 MeV fermion and its boson of mass = 11450/x = 5060 MeV plays a crucial role in "sub quark physics" and "weak interaction". 938.8 MeV strong fermion seems to be the proton. 415 MeV strong boson seems to be the mother of the presently believed 493,496 and 547 MeV etc, strange mesons. With 11450 MeV fermion "effective quark-fermi-gluons" and with 5060 MeV boson "quark boso-gluon masses" are estimated. "Effective quark fermi-gluons" plays a crucial role in ground state charged baryons mass generation. Light quark bosons couple with these charged baryons to form doublets and triplets. "Quark boso-gluons" plays a crucial role in ground state neutral and charged mesons mass generation. Fine and super-fine rotational levels can be given by [I or (I/2)] power(1/4) and [I or (I/2)] power(1/12) respectively. Here, I = n(n+1) and n = 1, 2, 3, … (author)

  6. Strongly interacting matter in magnetic fields

    CERN Document Server

    Landsteiner, Karl; Schmitt, Andreas; Yee, Ho-Ung

    2013-01-01

    The physics of strongly interacting matter in an external magnetic field is presently emerging as a topic of great cross-disciplinary interest for particle, nuclear, astro- and condensed matter physicists. It is known that strong magnetic fields are created in heavy ion collisions, an insight that has made it possible to study a variety of surprising and intriguing phenomena that emerge from the interplay of quantum anomalies, the topology of non-Abelian gauge fields, and the magnetic field. In particular, the non-trivial topological configurations of the gluon field induce a non-dissipative electric current in the presence of a magnetic field. These phenomena have led to an extended formulation of relativistic hydrodynamics, called chiral magnetohydrodynamics. Hitherto unexpected applications in condensed matter physics include graphene and topological insulators. Other fields of application include astrophysics, where strong magnetic fields exist in magnetars and pulsars. Last but not least, an important ne...

  7. A strongly interacting polaritonic quantum dot

    Science.gov (United States)

    Jia, Ningyuan; Schine, Nathan; Georgakopoulos, Alexandros; Ryou, Albert; Clark, Logan W.; Sommer, Ariel; Simon, Jonathan

    2018-06-01

    Polaritons are promising constituents of both synthetic quantum matter1 and quantum information processors2, whose properties emerge from their components: from light, polaritons draw fast dynamics and ease of transport; from matter, they inherit the ability to collide with one another. Cavity polaritons are particularly promising as they may be confined and subjected to synthetic magnetic fields controlled by cavity geometry3, and furthermore they benefit from increased robustness due to the cavity enhancement in light-matter coupling. Nonetheless, until now, cavity polaritons have operated only in a weakly interacting mean-field regime4,5. Here we demonstrate strong interactions between individual cavity polaritons enabled by employing highly excited Rydberg atoms as the matter component of the polaritons. We assemble a quantum dot composed of approximately 150 strongly interacting Rydberg-dressed 87Rb atoms in a cavity, and observe blockaded transport of photons through it. We further observe coherent photon tunnelling oscillations, demonstrating that the dot is zero-dimensional. This work establishes the cavity Rydberg polariton as a candidate qubit in a photonic information processor and, by employing multiple resonator modes as the spatial degrees of freedom of a photonic particle, the primary ingredient to form photonic quantum matter6.

  8. New strong interactions above the electroweak scale

    International Nuclear Information System (INIS)

    White, A.R.

    1994-01-01

    Theoretical arguments for a new higher-color quark sector, based on Pomeron physics in QCD, are briefly described. The electroweak symmetry-breaking, Strong CP conservation, and electroweak scale CP violation, that is naturally produced by this sector is also outlined. A further consequence is that above the electroweak scale there will be a radical change in the strong interaction. Electroweak states, in particular multiple W's and Z's, and new, semi-stable, very massive, baryons, will be commonly produced. The possible correlation of expected phenomena with a wide range of observed Cosmic Ray effects at and above the primary spectrum knee is described. Related phenomena that might be seen in the highest energy hard scattering events at the Fermilab Tevatron, some of which could be confused with top production, are also briefly discussed

  9. The application of an emerging technique for protein-protein interaction interface mapping: the combination of photo-initiated cross-linking protein nanoprobes with mass spectrometry

    Czech Academy of Sciences Publication Activity Database

    Ptáčková, Renata; Ječmen, Tomáš; Novák, Petr; Šulc, Miroslav; Hudeček, J.; Stiborová, M.

    2014-01-01

    Roč. 15, č. 6 (2014), s. 9224-9241 E-ISSN 1422-0067 R&D Projects: GA ČR(CZ) GAP207/12/0627 Grant - others:Universita Karlova(CZ) 903413; Magistrát hlavního města Prahy(CZ) CZ.2.16/3.1.00/24023; UNCE(BE) 204025/2012 Institutional support: RVO:61388971 Keywords : nanoprobes * mass spectrometry * protein-protein interactions Subject RIV: CE - Biochemistry Impact factor: 2.862, year: 2014

  10. Many Body Structure of Strongly Interacting Systems

    CERN Document Server

    Arenhövel, Hartmuth; Drechsel, Dieter; Friedrich, Jörg; Kaiser, Karl-Heinz; Walcher, Thomas; Symposium on 20 Years of Physics at the Mainz Microtron MAMI

    2006-01-01

    This carefully edited proceedings volume provides an extensive review and analysis of the work carried out over the past 20 years at the Mainz Microtron (MAMI). This research centered around the application of Quantum Chromodynamics in the strictly nonperturbative regime at hadronic scales of about 1 fm. Due to the many degrees of freedom in hadrons at this scale the leitmotiv of this research is "Many body structure of strongly interacting systems". Further, an outlook on the research with the forthcoming upgrade of MAMI is given. This volume is an authoritative source of reference for everyone interested in the field of the electro-weak probing of the structure of hadrons.

  11. Strong Interaction Studies with PANDA at FAIR

    Science.gov (United States)

    Schönning, Karin

    2016-10-01

    The Facility for Antiproton and Ion Research (FAIR) in Darmstadt, Germany, provides unique possibilities for a new generation of nuclear-, hadron- and atomic physics experiments. The future PANDA experiment at FAIR will offer a broad physics programme with emphasis on different aspects of hadron physics. Understanding the strong interaction in the perturbative regime remains one of the greatest challenges in contemporary physics and hadrons provide several important keys. In these proceedings, PANDA will be presented along with some high-lights of the planned physics programme.

  12. Strong Interaction Studies with PANDA at FAIR

    International Nuclear Information System (INIS)

    Schönning, Karin

    2016-01-01

    The Facility for Antiproton and Ion Research (FAIR) in Darmstadt, Germany, provides unique possibilities for a new generation of nuclear-, hadron- and atomic physics experiments. The future PANDA experiment at FAIR will offer a broad physics programme with emphasis on different aspects of hadron physics. Understanding the strong interaction in the perturbative regime remains one of the greatest challenges in contemporary physics and hadrons provide several important keys. In these proceedings, PANDA will be presented along with some high-lights of the planned physics programme

  13. Strongly interacting Higgs sector without technicolor

    International Nuclear Information System (INIS)

    Liu Chuan; Kuti, J.

    1994-12-01

    Simulation results are presented on Higgs mass calculations in the spontaneously broken phase of the Higgs sector in the minimal Standard Model with a higher derviative regulator. A heavy Higgs particle is found in the TeV mass range in the presence of a complex conjugate ghost pair at higher energies. The ghost pair evades easy experimental detection. As a finite and unitary theory in the continuum, this model serves as an explicit and simple example of a strong interacting Higgs sector without technicolor. (orig.)

  14. Strong Interactions Physics at BaBar

    Energy Technology Data Exchange (ETDEWEB)

    Pioppi, M.

    2005-03-14

    Recent results obtained by BABAR experiment and related to strong interactions physics are presented, with particular attention to the extraction of the first four hadronic-mass moments and the first three lepton-energy moments in semileptonic decays. From a simultaneous fit to the moments, the CKM element |V{sub cb}|, the inclusive B {yields} X{sub c}lv and other heavy quark parameters are derived. The second topic is the ambiguity-free measurement of cos(2{beta}) in B {yields} J/{Psi}K* decays. With approximately 88 million of B{bar B} pairs, negative solutions for cos(2{beta}) are excluded at 89%.

  15. Strong Interactive Massive Particles from a Strong Coupled Theory

    DEFF Research Database (Denmark)

    Yu. Khlopov, Maxim; Kouvaris, Christoforos

    2008-01-01

    (-2). These excessive techniparticles are all captured by $^4He$, creating \\emph{techni-O-helium} $tOHe$ ``atoms'', as soon as $^4He$ is formed in Big Bang Nucleosynthesis. The interaction of techni-O-helium with nuclei opens new paths to the creation of heavy nuclei in Big Bang Nucleosynthesis. Due...

  16. Toward Small-Molecule Inhibition of Protein-Protein Interactions: General Aspects and Recent Progress in Targeting Costimulatory and Coinhibitory (Immune Checkpoint) Interactions.

    Science.gov (United States)

    Bojadzic, Damir; Buchwald, Peter

    2018-05-30

    Protein-protein interactions (PPIs) that are part of the costimulatory and coinhibitory (immune checkpoint) signaling are critical for adequate T cell response and are important therapeutic targets for immunomodulation. Biologics targeting them have already achieved considerable clinical success in the treatment of autoimmune diseases or transplant recipients (e.g., abatacept, belatacept, and belimumab) as well as cancer (e.g., ipilimumab, nivolumab, pembrolizumab, atezolizumab, durvalumab, and avelumab). In view of such progress, there have been only relatively limited efforts toward developing small-molecule PPI inhibitors (SMPPIIs) targeting these cosignaling interactions, possibly because they, as all other PPIs, are difficult to target by small molecules and were not considered druggable. Nevertheless, substantial progress has been achieved during the last decade. SMPPIIs proving the feasibility of such approaches have been identified through various strategies for a number of cosignaling interactions including CD40-CD40L, OX40-OX40L, BAFFR-BAFF, CD80-CD28, and PD-1-PD-L1s. Here, after an overview of the general aspects and challenges of SMPPII-focused drug discovery, we review them briefly together with relevant structural, immune-signaling, physicochemical, and medicinal chemistry aspects. While so far only a few of these SMPPIIs have shown activity in animal models (DRI-C21045 for CD40-D40L, KR33426 for BAFFR-BAFF) or reached clinical development (RhuDex for CD80-CD28, CA-170 for PD-1-PD-L1), there is proof-of-principle evidence for the feasibility of such approaches in immunomodulation. They can result in products that are easier to develop/manufacture and are less likely to be immunogenic or encounter postmarket safety events than corresponding biologics, and, contrary to them, can even become orally bioavailable. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  17. Finite temperature system of strongly interacting baryons

    International Nuclear Information System (INIS)

    Bowers, R.L.; Gleeson, A.M.; Pedigo, R.D.; Wheeler, J.W.

    1976-07-01

    A fully relativistic finite temperature many body theory is constructed and used to examine the bulk properties of a system of strongly interacting baryons. The strong interactions are described by a two parameter phenomenological model fit to a simple description of nuclear matter at T = 0. The zero temperature equation of state for such a system which has already been discussed in the literature was developed to give a realistic description of nuclear matter. The model presented here is the exact finite temperature extension of that model. The effect of the inclusion of baryon pairs for T greater than or equal to 2mc 2 /k is discussed in detail. The phase transition identified with nuclear matter vanishes for system temperatures in excess of T/sub C/ = 1.034 x 10 11 0 K. All values of epsilon (P,T) correspond to systems that are causal in the sense that the locally determined speed of sound never exceeds the speed of light

  18. Finite temperature system of strongly interacting baryons

    Energy Technology Data Exchange (ETDEWEB)

    Bowers, R.L.; Gleeson, A.M.; Pedigo, R.D.; Wheeler, J.W.

    1976-07-01

    A fully relativistic finite temperature many body theory is constructed and used to examine the bulk properties of a system of strongly interacting baryons. The strong interactions are described by a two parameter phenomenological model fit to a simple description of nuclear matter at T = 0. The zero temperature equation of state for such a system which has already been discussed in the literature was developed to give a realistic description of nuclear matter. The model presented here is the exact finite temperature extension of that model. The effect of the inclusion of baryon pairs for T greater than or equal to 2mc/sup 2//k is discussed in detail. The phase transition identified with nuclear matter vanishes for system temperatures in excess of T/sub C/ = 1.034 x 10/sup 11/ /sup 0/K. All values of epsilon (P,T) correspond to systems that are causal in the sense that the locally determined speed of sound never exceeds the speed of light.

  19. Combining random gene fission and rational gene fusion to discover near-infrared fluorescent protein fragments that report on protein-protein interactions.

    Science.gov (United States)

    Pandey, Naresh; Nobles, Christopher L; Zechiedrich, Lynn; Maresso, Anthony W; Silberg, Jonathan J

    2015-05-15

    Gene fission can convert monomeric proteins into two-piece catalysts, reporters, and transcription factors for systems and synthetic biology. However, some proteins can be challenging to fragment without disrupting function, such as near-infrared fluorescent protein (IFP). We describe a directed evolution strategy that can overcome this challenge by randomly fragmenting proteins and concomitantly fusing the protein fragments to pairs of proteins or peptides that associate. We used this method to create libraries that express fragmented IFP as fusions to a pair of associating peptides (IAAL-E3 and IAAL-K3) and proteins (CheA and CheY) and screened for fragmented IFP with detectable near-infrared fluorescence. Thirteen novel fragmented IFPs were identified, all of which arose from backbone fission proximal to the interdomain linker. Either the IAAL-E3 and IAAL-K3 peptides or CheA and CheY proteins could assist with IFP fragment complementation, although the IAAL-E3 and IAAL-K3 peptides consistently yielded higher fluorescence. These results demonstrate how random gene fission can be coupled to rational gene fusion to create libraries enriched in fragmented proteins with AND gate logic that is dependent upon a protein-protein interaction, and they suggest that these near-infrared fluorescent protein fragments will be suitable as reporters for pairs of promoters and protein-protein interactions within whole animals.

  20. Protein-protein interactions within photosystem II under photoprotection: the synergy between CP29 minor antenna, subunit S (PsbS) and zeaxanthin at all-atom resolution.

    Science.gov (United States)

    Daskalakis, Vangelis

    2018-05-07

    The assembly and disassembly of protein complexes within cells are crucial life-sustaining processes. In photosystem II (PSII) of higher plants, there is a delicate yet obscure balance between light harvesting and photo-protection under fluctuating light conditions, that involves protein-protein complexes. Recent breakthroughs in molecular dynamics (MD) simulations are combined with new approaches herein to provide structural and energetic insight into such a complex between the CP29 minor antenna and the PSII subunit S (PsbS). The microscopic model involves extensive sampling of bound and dissociated states at atomic resolution in the presence of photo-protective zeaxanthin (Zea), and reveals well defined protein-protein cross-sections. The complex is placed within PSII, and macroscopic connections are emerging (PsbS-CP29-CP24-CP47) along the energy transfer pathways from the antenna to the PSII core. These connections explain macroscopic observations in the literature, while the previously obscured atomic scale details are now revealed. The implications of these findings are discussed in the context of the Non-Photochemical Quenching (NPQ) of chlorophyll fluorescence, the down-regulatory mechanism of photosynthesis, that enables the protection of PSII against excess excitation load. Zea is found at the PsbS-CP29 cross-section and a pH-dependent equilibrium between PsbS dimer/monomers and the PsbS-CP29 dissociation/association is identified as the target for engineering tolerant plants with increased crop and biomass yields. Finally, the new MD based approaches can be used to probe protein-protein interactions in general, and the PSII structure provided can initiate large scale molecular simulations of the photosynthetic apparatus, under NPQ conditions.

  1. Combinatorial description of space and strong interactions

    International Nuclear Information System (INIS)

    Zenczykowski, P.

    1988-01-01

    A reinterpretation is given of a successful phenomenological approach to hadron self-energy effects known as the unitarized quark model. General arguments are given that the proper description of strong interactions may require abandoning the assignment of a primary role to continuous concepts such as position and momentum in favor of discrete ones such as spin or W-spin. The reinterpretation exploits an analogy between the W-spin diagrams occurring in the calculations of hadronic loop effects and the spin network idea of Penrose. A connection between the S-matrix approach to hadron masses and the purely algebraic approach characteristic of the quark model is indicated. Several hadron mass relations generated by a resulting SU(6)/sub w/-group-theoretic expression are presented and discussed. Results of an attempt to generalize the scheme to the description of hadron vertices are reported

  2. Diversity of T cell epitopes in Plasmodium falciparum circumsporozoite protein likely due to protein-protein interactions.

    Directory of Open Access Journals (Sweden)

    Nagesh R Aragam

    Full Text Available Circumsporozoite protein (CS is a leading vaccine antigen for falciparum malaria, but is highly polymorphic in natural parasite populations. The factors driving this diversity are unclear, but non-random assortment of the T cell epitopes TH2 and TH3 has been observed in a Kenyan parasite population. The recent publication of the crystal structure of the variable C terminal region of the protein allows the assessment of the impact of diversity on protein structure and T cell epitope assortment. Using data from the Gambia (55 isolates and Malawi (235 isolates, we evaluated the patterns of diversity within and between epitopes in these two distantly-separated populations. Only non-synonymous mutations were observed with the vast majority in both populations at similar frequencies suggesting strong selection on this region. A non-random pattern of T cell epitope assortment was seen in Malawi and in the Gambia, but structural analysis indicates no intramolecular spatial interactions. Using the information from these parasite populations, structural analysis reveals that polymorphic amino acids within TH2 and TH3 colocalize to one side of the protein, surround, but do not involve, the hydrophobic pocket in CS, and predominately involve charge switches. In addition, free energy analysis suggests residues forming and behind the novel pocket within CS are tightly constrained and well conserved in all alleles. In addition, free energy analysis shows polymorphic residues tend to be populated by energetically unfavorable amino acids. In combination, these findings suggest the diversity of T cell epitopes in CS may be primarily an evolutionary response to intermolecular interactions at the surface of the protein potentially counteracting antibody-mediated immune recognition or evolving host receptor diversity.

  3. An α-Helix-Mimicking 12,13-Helix: Designed α/β/γ-Foldamers as Selective Inhibitors of Protein-Protein Interactions.

    Science.gov (United States)

    Grison, Claire M; Miles, Jennifer A; Robin, Sylvie; Wilson, Andrew J; Aitken, David J

    2016-09-05

    A major current challenge in bioorganic chemistry is the identification of effective mimics of protein secondary structures that act as inhibitors of protein-protein interactions (PPIs). In this work, trans-2-aminocyclobutanecarboxylic acid (tACBC) was used as the key β-amino acid component in the design of α/β/γ-peptides to structurally mimic a native α-helix. Suitably functionalized α/β/γ-peptides assume an α-helix-mimicking 12,13-helix conformation in solution, exhibit enhanced proteolytic stability in comparison to the wild-type α-peptide parent sequence from which they are derived, and act as selective inhibitors of the p53/hDM2 interaction. © 2016 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

  4. Signatures of pleiotropy, economy and convergent evolution in a domain-resolved map of human-virus protein-protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Sara Garamszegi

    Full Text Available A central challenge in host-pathogen systems biology is the elucidation of general, systems-level principles that distinguish host-pathogen interactions from within-host interactions. Current analyses of host-pathogen and within-host protein-protein interaction networks are largely limited by their resolution, treating proteins as nodes and interactions as edges. Here, we construct a domain-resolved map of human-virus and within-human protein-protein interaction networks by annotating protein interactions with high-coverage, high-accuracy, domain-centric interaction mechanisms: (1 domain-domain interactions, in which a domain in one protein binds to a domain in a second protein, and (2 domain-motif interactions, in which a domain in one protein binds to a short, linear peptide motif in a second protein. Analysis of these domain-resolved networks reveals, for the first time, significant mechanistic differences between virus-human and within-human interactions at the resolution of single domains. While human proteins tend to compete with each other for domain binding sites by means of sequence similarity, viral proteins tend to compete with human proteins for domain binding sites in the absence of sequence similarity. Independent of their previously established preference for targeting human protein hubs, viral proteins also preferentially target human proteins containing linear motif-binding domains. Compared to human proteins, viral proteins participate in more domain-motif interactions, target more unique linear motif-binding domains per residue, and contain more unique linear motifs per residue. Together, these results suggest that viruses surmount genome size constraints by convergently evolving multiple short linear motifs in order to effectively mimic, hijack, and manipulate complex host processes for their survival. Our domain-resolved analyses reveal unique signatures of pleiotropy, economy, and convergent evolution in viral

  5. Signatures of pleiotropy, economy and convergent evolution in a domain-resolved map of human-virus protein-protein interaction networks.

    Science.gov (United States)

    Garamszegi, Sara; Franzosa, Eric A; Xia, Yu

    2013-01-01

    A central challenge in host-pathogen systems biology is the elucidation of general, systems-level principles that distinguish host-pathogen interactions from within-host interactions. Current analyses of host-pathogen and within-host protein-protein interaction networks are largely limited by their resolution, treating proteins as nodes and interactions as edges. Here, we construct a domain-resolved map of human-virus and within-human protein-protein interaction networks by annotating protein interactions with high-coverage, high-accuracy, domain-centric interaction mechanisms: (1) domain-domain interactions, in which a domain in one protein binds to a domain in a second protein, and (2) domain-motif interactions, in which a domain in one protein binds to a short, linear peptide motif in a second protein. Analysis of these domain-resolved networks reveals, for the first time, significant mechanistic differences between virus-human and within-human interactions at the resolution of single domains. While human proteins tend to compete with each other for domain binding sites by means of sequence similarity, viral proteins tend to compete with human proteins for domain binding sites in the absence of sequence similarity. Independent of their previously established preference for targeting human protein hubs, viral proteins also preferentially target human proteins containing linear motif-binding domains. Compared to human proteins, viral proteins participate in more domain-motif interactions, target more unique linear motif-binding domains per residue, and contain more unique linear motifs per residue. Together, these results suggest that viruses surmount genome size constraints by convergently evolving multiple short linear motifs in order to effectively mimic, hijack, and manipulate complex host processes for their survival. Our domain-resolved analyses reveal unique signatures of pleiotropy, economy, and convergent evolution in viral-host interactions that are

  6. Transport coefficients of strongly interacting matter

    International Nuclear Information System (INIS)

    Heckmann, Klaus

    2011-01-01

    In this thesis, we investigate the dissipative transport phenomena of strongly interacting matter. The special interest is in the shear viscosity and its value divided by entropy density. The performed calculations are based on effective models for Quantum Chromodynamics, mostly focused on the 2-flavor Nambu-Jona-Lasinio model. This allows us to study the hadronic sector as well as the quark sector within one single model. We expand the models up to next-to-leading order in inverse numbers of colors. We present different possibilities of calculating linear transport coefficients and give an overview over qualitative properties as well as over recent ideas concerning ideal fluids. As present methods are not able to calculate the quark two-point function in Minkowski space-time in the self-consistent approximation scheme of the Nambu-Jona-Lasinio model, a new method for this purpose is developed. This self-energy parametrization method is applied to the expansion scheme, yielding the quark spectral function with meson back-coupling effects. The usage of this spectral function in the transport calculation is only one result of this work. We also test the application of different transport approaches in the NJL model, and find an interesting behavior of the shear viscosity at the critical end point of the phase diagram. We also use the NJL model to calculate the viscosity of a pion gas in the dilute regime. After an analysis of other models for pions and their interaction, we find that the NJL-result leads to an important modification of transport properties in comparison with the calculations which purely rely on pion properties in the vacuum. (orig.)

  7. Strong and Electromagnetic Interactions at SPS Energies

    CERN Document Server

    Ribicki, Andrzej

    2009-01-01

    Particle production in peripheral Pb+Pb collisions has been measured at a beam energy of 158 GeV per nucleon, corresponding to psNN 17.3 GeV. The measurements provide full double differential coverage in a wide range of longitudinal and transverse momenta, including the central (“mid-rapidity”) area and extending far into the projectile fragmentation region. The resulting analysis shows the heavy ion reaction as a mixture of different processes. In particular, surprising phenomena, like the presence of large and strongly varying structures in the shape of the double differential cross section d2s /dxFd pT , are induced by the final state electromagnetic interaction between produced particles and the charged spectator system. This effect is largest at low transverse momenta, where it results in a deep valley in the xF -dependence of the produced p+/p− ratio. The basic characteristics of the electromagnetic phenomenon described above agree with the results of a theoretical analysis, performed by means of ...

  8. Conduction properties of strongly interacting Fermions

    Science.gov (United States)

    Brantut, Jean-Philippe; Stadler, David; Krinner, Sebastian; Meineke, Jakob; Esslinger, Tilman

    2013-05-01

    We experimentally study the transport process of ultracold fermionic atoms through a mesoscopic, quasi two-dimensional channel connecting macroscopic reservoirs. By observing the current response to a bias applied between the reservoirs, we directly access the resistance of the channel in a manner analogous to a solid state conduction measurement. The resistance is further controlled by a gate potential reducing the atomic density in the channel, like in a field effect transistor. In this setup, we study the flow of a strongly interacting Fermi gas, and observe a striking drop of resistance with increasing density in the channel, as expected at the onset of superfluidity. We relate the transport properties to the in-situ evolution of the thermodynamic potential, providing a model independant thermodynamic scale. The resistance is compared to that of an ideal Fermi gas in the same geometry, which shows an order of magnitude larger resistance, originating from the contact resistance between the channel and the reservoirs. The extension of this study to a channel containing a tunable disorder is briefly outlined.

  9. Toward a Strongly Interacting Scalar Higgs Particle

    International Nuclear Information System (INIS)

    Shalaby, Abouzeid M.; El-Houssieny, M.

    2008-01-01

    We calculate the vacuum energy of the non-Hermitian and PT symmetric (-gφ 4 ) 2+1 scalar field theory. Rather than the corresponding Hermitian theory and due to the asymptotic freedom property of the theory, the vacuum energy does not blow up for large energy scales which is a good sign to solve the hierarchy problem when using this model to break the U(1)xSU(2) symmetry in the standard model. The theory is strongly interacting and in fact, all the dimensionful parameters in the theory like mass and energy are finite even for very high energy scales. Moreover, relative to the vacuum energy for the Hermitian φ 4 theory, the vacuum energy of the non-Hermitian and PT symmetric (-gφ 4 ) 2+1 theory is tiny, which is a good sign toward the solution of the cosmological constant problem. Remarkably, these features of the non-Hermitian and PT symmetric (-gφ 4 ) 2+1 scalar field theory make it very plausible to be employed as a Higgs mechanism in the standard model instead of the problematic Hermitian Higgs mechanism

  10. DTFP-Growth: Dynamic Threshold-Based FP-Growth Rule Mining Algorithm Through Integrating Gene Expression, Methylation, and Protein-Protein Interaction Profiles.

    Science.gov (United States)

    Mallik, Saurav; Bhadra, Tapas; Mukherji, Ayan; Mallik, Saurav; Bhadra, Tapas; Mukherji, Ayan; Mallik, Saurav; Bhadra, Tapas; Mukherji, Ayan

    2018-04-01

    Association rule mining is an important technique for identifying interesting relationships between gene pairs in a biological data set. Earlier methods basically work for a single biological data set, and, in maximum cases, a single minimum support cutoff can be applied globally, i.e., across all genesets/itemsets. To overcome this limitation, in this paper, we propose dynamic threshold-based FP-growth rule mining algorithm that integrates gene expression, methylation and protein-protein interaction profiles based on weighted shortest distance to find the novel associations among different pairs of genes in multi-view data sets. For this purpose, we introduce three new thresholds, namely, Distance-based Variable/Dynamic Supports (DVS), Distance-based Variable Confidences (DVC), and Distance-based Variable Lifts (DVL) for each rule by integrating co-expression, co-methylation, and protein-protein interactions existed in the multi-omics data set. We develop the proposed algorithm utilizing these three novel multiple threshold measures. In the proposed algorithm, the values of , , and are computed for each rule separately, and subsequently it is verified whether the support, confidence, and lift of each evolved rule are greater than or equal to the corresponding individual , , and values, respectively, or not. If all these three conditions for a rule are found to be true, the rule is treated as a resultant rule. One of the major advantages of the proposed method compared with other related state-of-the-art methods is that it considers both the quantitative and interactive significance among all pairwise genes belonging to each rule. Moreover, the proposed method generates fewer rules, takes less running time, and provides greater biological significance for the resultant top-ranking rules compared to previous methods.

  11. The Development of Protein Microarrays and Their Applications in DNA-Protein and Protein-Protein Interaction Analyses of Arabidopsis Transcription Factors

    Science.gov (United States)

    Gong, Wei; He, Kun; Covington, Mike; Dinesh-Kumar, S. P.; Snyder, Michael; Harmer, Stacey L.; Zhu, Yu-Xian; Deng, Xing Wang

    2009-01-01

    We used our collection of Arabidopsis transcription factor (TF) ORFeome clones to construct protein microarrays containing as many as 802 TF proteins. These protein microarrays were used for both protein-DNA and protein-protein interaction analyses. For protein-DNA interaction studies, we examined AP2/ERF family TFs and their cognate cis-elements. By careful comparison of the DNA-binding specificity of 13 TFs on the protein microarray with previous non-microarray data, we showed that protein microarrays provide an efficient and high throughput tool for genome-wide analysis of TF-DNA interactions. This microarray protein-DNA interaction analysis allowed us to derive a comprehensive view of DNA-binding profiles of AP2/ERF family proteins in Arabidopsis. It also revealed four TFs that bound the EE (evening element) and had the expected phased gene expression under clock-regulation, thus providing a basis for further functional analysis of their roles in clock regulation of gene expression. We also developed procedures for detecting protein interactions using this TF protein microarray and discovered four novel partners that interact with HY5, which can be validated by yeast two-hybrid assays. Thus, plant TF protein microarrays offer an attractive high-throughput alternative to traditional techniques for TF functional characterization on a global scale. PMID:19802365

  12. Strong field interaction of laser radiation

    International Nuclear Information System (INIS)

    Pukhov, Alexander

    2003-01-01

    The Review covers recent progress in laser-matter interaction at intensities above 10 18 W cm -2 . At these intensities electrons swing in the laser pulse with relativistic energies. The laser electric field is already much stronger than the atomic fields, and any material is instantaneously ionized, creating plasma. The physics of relativistic laser-plasma is highly non-linear and kinetic. The best numerical tools applicable here are particle-in-cell (PIC) codes, which provide the most fundamental plasma model as an ensemble of charged particles. The three-dimensional (3D) PIC code Virtual Laser-Plasma Laboratory runs on a massively parallel computer tracking trajectories of up to 10 9 particles simultaneously. This allows one to simulate real laser-plasma experiments for the first time. When the relativistically intense laser pulses propagate through plasma, a bunch of new physical effects appears. The laser pulses are subject to relativistic self-channelling and filamentation. The gigabar ponderomotive pressure of the laser pulse drives strong currents of plasma electrons in the laser propagation direction; these currents reach the Alfven limit and generate 100 MG quasistatic magnetic fields. These magnetic fields, in turn, lead to the mutual filament attraction and super-channel formation. The electrons in the channels are accelerated up to gigaelectronvolt energies and the ions gain multi-MeV energies. We discuss different mechanisms of particle acceleration and compare numerical simulations with experimental data. One of the very important applications of the relativistically strong laser beams is the fast ignition (FI) concept for the inertial fusion energy (IFE). Petawatt-class lasers may provide enough energy to isochorically ignite a pre-compressed target consisting of thermonuclear fuel. The FI approach would ease dramatically the constraints on the implosion symmetry and improve the energy gain. However, there is a set of problems to solve before the FI

  13. ABA signaling in guard cells entails a dynamic protein-protein interaction relay from the PYL-RCAR family receptors to ion channels.

    Science.gov (United States)

    Lee, Sung Chul; Lim, Chae Woo; Lan, Wenzhi; He, Kai; Luan, Sheng

    2013-03-01

    Plant hormone abscisic acid (ABA) serves as an integrator of environmental stresses such as drought to trigger stomatal closure by regulating specific ion channels in guard cells. We previously reported that SLAC1, an outward anion channel required for stomatal closure, was regulated via reversible protein phosphorylation events involving ABA signaling components, including protein phosphatase 2C members and a SnRK2-type kinase (OST1). In this study, we reconstituted the ABA signaling pathway as a protein-protein interaction relay from the PYL/RCAR-type receptors, to the PP2C-SnRK2 phosphatase-kinase pairs, to the ion channel SLAC1. The ABA receptors interacted with and inhibited PP2C phosphatase activity against the SnRK2-type kinase, releasing active SnRK2 kinase to phosphorylate, and activate the SLAC1 channel, leading to reduced guard cell turgor and stomatal closure. Both yeast two-hybrid and bimolecular fluorescence complementation assays were used to verify the interactions among the components in the pathway. These biochemical assays demonstrated activity modifications of phosphatases and kinases by their interaction partners. The SLAC1 channel activity was used as an endpoint readout for the strength of the signaling pathway, depending on the presence of different combinations of signaling components. Further study using transgenic plants overexpressing one of the ABA receptors demonstrated that changing the relative level of interacting partners would change ABA sensitivity.

  14. Lectin receptor kinases participate in protein-protein interactions to mediate plasma membrane-cell wall adhesions in Arabidopsis

    NARCIS (Netherlands)

    Gouget, A.; Senchou, V.; Govers, F.; Sanson, A.; Barre, A.; Rougé, P.; Pont-Lezica, R.; Canut, H.

    2006-01-01

    Interactions between plant cell walls and plasma membranes are essential for cells to function properly, but the molecules that mediate the structural continuity between wall and membrane are unknown. Some of these interactions, which are visualized upon tissue plasmolysis in Arabidopsis

  15. System in biology leading to cell pathology: stable protein-protein interactions after covalent modifications by small molecules or in transgenic cells.

    Science.gov (United States)

    Malina, Halina Z

    2011-01-19

    The physiological processes in the cell are regulated by reversible, electrostatic protein-protein interactions. Apoptosis is such a regulated process, which is critically important in tissue homeostasis and development and leads to complete disintegration of the cell. Pathological apoptosis, a process similar to apoptosis, is associated with aging and infection. The current study shows that pathological apoptosis is a process caused by the covalent interactions between the signaling proteins, and a characteristic of this pathological network is the covalent binding of calmodulin to regulatory sequences. Small molecules able to bind covalently to the amino group of lysine, histidine, arginine, or glutamine modify the regulatory sequences of the proteins. The present study analyzed the interaction of calmodulin with the BH3 sequence of Bax, and the calmodulin-binding sequence of myristoylated alanine-rich C-kinase substrate in the presence of xanthurenic acid in primary retinal epithelium cell cultures and murine epithelial fibroblast cell lines transformed with SV40 (wild type [WT], Bid knockout [Bid-/-], and Bax-/-/Bak-/- double knockout [DKO]). Cell death was observed to be associated with the covalent binding of calmodulin, in parallel, to the regulatory sequences of proteins. Xanthurenic acid is known to activate caspase-3 in primary cell cultures, and the results showed that this activation is also observed in WT and Bid-/- cells, but not in DKO cells. However, DKO cells were not protected against death, but high rates of cell death occurred by detachment. The results showed that small molecules modify the basic amino acids in the regulatory sequences of proteins leading to covalent interactions between the modified sequences (e.g., calmodulin to calmodulin-binding sites). The formation of these polymers (aggregates) leads to an unregulated and, consequently, pathological protein network. The results suggest a mechanism for the involvement of small molecules

  16. Protein-Protein Interaction Article Classification Using a Convolutional Recurrent Neural Network with Pre-trained Word Embeddings.

    Science.gov (United States)

    Matos, Sérgio; Antunes, Rui

    2017-12-13

    Curation of protein interactions from scientific articles is an important task, since interaction networks are essential for the understanding of biological processes associated with disease or pharmacological action for example. However, the increase in the number of publications that potentially contain relevant information turns this into a very challenging and expensive task. In this work we used a convolutional recurrent neural network for identifying relevant articles for extracting information regarding protein interactions. Using the BioCreative III Article Classification Task dataset, we achieved an area under the precision-recall curve of 0.715 and a Matthew's correlation coefficient of 0.600, which represents an improvement over previous works.

  17. Protein-protein interactions as a strategy towards protein-specific drug design: the example of ataxin-1.

    Directory of Open Access Journals (Sweden)

    Cesira de Chiara

    Full Text Available A main challenge for structural biologists is to understand the mechanisms that discriminate between molecular interactions and determine function. Here, we show how partner recognition of the AXH domain of the transcriptional co-regulator ataxin-1 is fine-tuned by a subtle balance between self- and hetero-associations. Ataxin-1 is the protein responsible for the hereditary spinocerebellar ataxia type 1, a disease linked to protein aggregation and transcriptional dysregulation. Expansion of a polyglutamine tract is essential for ataxin-1 aggregation, but the sequence-wise distant AXH domain plays an important aggravating role in the process. The AXH domain is also a key element for non-aberrant function as it intervenes in interactions with multiple protein partners. Previous data have shown that AXH is dimeric in solution and forms a dimer of dimers when crystallized. By solving the structure of a complex of AXH with a peptide from the interacting transcriptional repressor CIC, we show that the dimer interface of AXH is displaced by the new interaction and that, when blocked by the CIC peptide AXH aggregation and misfolding are impaired. This is a unique example in which palindromic self- and hetero-interactions within a sequence with chameleon properties discriminate the partner. We propose a drug design strategy for the treatment of SCA1 that is based on the information gained from the AXH/CIC complex.

  18. Exploration of the dynamic properties of protein complexes predicted from spatially constrained protein-protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Eric A Yen

    2014-05-01

    Full Text Available Protein complexes are not static, but rather highly dynamic with subunits that undergo 1-dimensional diffusion with respect to each other. Interactions within protein complexes are modulated through regulatory inputs that alter interactions and introduce new components and deplete existing components through exchange. While it is clear that the structure and function of any given protein complex is coupled to its dynamical properties, it remains a challenge to predict the possible conformations that complexes can adopt. Protein-fragment Complementation Assays detect physical interactions between protein pairs constrained to ≤8 nm from each other in living cells. This method has been used to build networks composed of 1000s of pair-wise interactions. Significantly, these networks contain a wealth of dynamic information, as the assay is fully reversible and the proteins are expressed in their natural context. In this study, we describe a method that extracts this valuable information in the form of predicted conformations, allowing the user to explore the conformational landscape, to search for structures that correlate with an activity state, and estimate the abundance of conformations in the living cell. The generator is based on a Markov Chain Monte Carlo simulation that uses the interaction dataset as input and is constrained by the physical resolution of the assay. We applied this method to an 18-member protein complex composed of the seven core proteins of the budding yeast Arp2/3 complex and 11 associated regulators and effector proteins. We generated 20,480 output structures and identified conformational states using principle component analysis. We interrogated the conformation landscape and found evidence of symmetry breaking, a mixture of likely active and inactive conformational states and dynamic exchange of the core protein Arc15 between core and regulatory components. Our method provides a novel tool for prediction and

  19. SynSysNet: integration of experimental data on synaptic protein-protein interactions with drug-target relations

    NARCIS (Netherlands)

    von Eichborn, J.; Dunkel, M.; Gohlke, B.O.; Preissner, S.C.; Hoffmann, M.F.; Bauer, J.M.J.; Armstrong, J.D.; Schaefer, M.H.; Andrade-Navarro, M.A.; Le Novere, N.; Croning, M.D.R.; Grant, S.G.N.; van Nierop, P.; Smit, A.B.; Preissner, R.

    2013-01-01

    We created SynSysNet, available online at http://bioinformatics.charite.de/ synsysnet, to provide a platform that creates a comprehensive 4D network of synaptic interactions. Neuronal synapses are fundamental structures linking nerve cells in the brain and they are responsible for neuronal

  20. Computational design, construction, and characterization of a set of specificity determining residues in protein-protein interactions.

    Science.gov (United States)

    Nagao, Chioko; Izako, Nozomi; Soga, Shinji; Khan, Samia Haseeb; Kawabata, Shigeki; Shirai, Hiroki; Mizuguchi, Kenji

    2012-10-01

    Proteins interact with different partners to perform different functions and it is important to elucidate the determinants of partner specificity in protein complex formation. Although methods for detecting specificity determining positions have been developed previously, direct experimental evidence for these amino acid residues is scarce, and the lack of information has prevented further computational studies. In this article, we constructed a dataset that is likely to exhibit specificity in protein complex formation, based on available crystal structures and several intuitive ideas about interaction profiles and functional subclasses. We then defined a "structure-based specificity determining position (sbSDP)" as a set of equivalent residues in a protein family showing a large variation in their interaction energy with different partners. We investigated sequence and structural features of sbSDPs and demonstrated that their amino acid propensities significantly differed from those of other interacting residues and that the importance of many of these residues for determining specificity had been verified experimentally. Copyright © 2012 Wiley Periodicals, Inc.

  1. Site-Specific Phosphorylation of PSD-95 PDZ Domains Reveals Fine-Tuned Regulation of Protein-Protein Interactions

    DEFF Research Database (Denmark)

    Pedersen, Søren W; Albertsen, Louise; Moran, Griffin E

    2017-01-01

    The postsynaptic density protein of 95 kDa (PSD-95) is a key scaffolding protein that controls signaling at synapses in the brain through interactions of its PDZ domains with the C-termini of receptors, ion channels, and enzymes. PSD-95 is highly regulated by phosphorylation. To explore the effec...

  2. De Sitter vacua of strongly interacting QFT

    Energy Technology Data Exchange (ETDEWEB)

    Buchel, Alex [Department of Applied Mathematics, University of Western Ontario,London, Ontario N6A 5B7 (Canada); Department of Physics and Astronomy, University of Western Ontario,London, Ontario N6A 5B7 (Canada); Perimeter Institute for Theoretical Physics,Waterloo, Ontario N2J 2W9 (Canada); Karapetyan, Aleksandr [Department of Applied Mathematics, University of Western Ontario,London, Ontario N6A 5B7 (Canada)

    2017-03-22

    We use holographic correspondence to argue that Euclidean (Bunch-Davies) vacuum is a late-time attractor of the dynamical evolution of quantum gauge theories at strong coupling. The Bunch-Davies vacuum is not an adiabatic state, if the gauge theory is non-conformal — the comoving entropy production rate is nonzero. Using the N=2{sup ∗} gauge theory holography, we explore prospects of explaining current accelerated expansion of the Universe as due to the vacuum energy of a strongly coupled QFT.

  3. Small molecule inhibitors of ERCC1-XPF protein-protein interaction synergize alkylating agents in cancer cells.

    Science.gov (United States)

    Jordheim, Lars Petter; Barakat, Khaled H; Heinrich-Balard, Laurence; Matera, Eva-Laure; Cros-Perrial, Emeline; Bouledrak, Karima; El Sabeh, Rana; Perez-Pineiro, Rolando; Wishart, David S; Cohen, Richard; Tuszynski, Jack; Dumontet, Charles

    2013-07-01

    The benefit of cancer chemotherapy based on alkylating agents is limited because of the action of DNA repair enzymes, which mitigate the damage induced by these agents. The interaction between the proteins ERCC1 and XPF involves two major components of the nucleotide excision repair pathway. Here, novel inhibitors of this interaction were identified by virtual screening based on available structures with use of the National Cancer Institute diversity set and a panel of DrugBank small molecules. Subsequently, experimental validation of the in silico screening was undertaken. Top hits were evaluated on A549 and HCT116 cancer cells. In particular, the compound labeled NSC 130813 [4-[(6-chloro-2-methoxy-9-acridinyl)amino]-2-[(4-methyl-1-piperazinyl)methyl

  4. Electromagnetic probes of strongly interacting matter

    Indian Academy of Sciences (India)

    2015-05-07

    May 7, 2015 ... Collisions between two nuclei at relativistic energies will create charged particles – either in .... The thermal cutting rules give a systematic procedure to express ...... mesons due to its interaction with the thermal partons [80] and employment of running .... [16] J Deng, Q Wang, N Xu and P Zhuang, Phys. Lett.

  5. Relativistic rapprochement of electromagnetic and strong interactions

    International Nuclear Information System (INIS)

    Strel'tsov, V.N.

    1995-01-01

    On the basis of the Lienard-Wiechert potential and the relativistic Yukawa potential it is shown that the corresponding interactions with velocity growth increase differently (the electromagnetic one increases faster). According to preliminary estimations they are equivalent, at distances of the 'action radius' of nuclear forces, at γ≅ 960, where γ is the Lorentz factor. 2 refs

  6. In-Culture Cross-Linking of Bacterial Cells Reveals Large-Scale Dynamic Protein-Protein Interactions at the Peptide Level.

    Science.gov (United States)

    de Jong, Luitzen; de Koning, Edward A; Roseboom, Winfried; Buncherd, Hansuk; Wanner, Martin J; Dapic, Irena; Jansen, Petra J; van Maarseveen, Jan H; Corthals, Garry L; Lewis, Peter J; Hamoen, Leendert W; de Koster, Chris G

    2017-07-07

    Identification of dynamic protein-protein interactions at the peptide level on a proteomic scale is a challenging approach that is still in its infancy. We have developed a system to cross-link cells directly in culture with the special lysine cross-linker bis(succinimidyl)-3-azidomethyl-glutarate (BAMG). We used the Gram-positive model bacterium Bacillus subtilis as an exemplar system. Within 5 min extensive intracellular cross-linking was detected, while intracellular cross-linking in a Gram-negative species, Escherichia coli, was still undetectable after 30 min, in agreement with the low permeability in this organism for lipophilic compounds like BAMG. We were able to identify 82 unique interprotein cross-linked peptides with cross-links occur in assemblies involved in transcription and translation. Several of these interactions are new, and we identified a binding site between the δ and β' subunit of RNA polymerase close to the downstream DNA channel, providing a clue into how δ might regulate promoter selectivity and promote RNA polymerase recycling. Our methodology opens new avenues to investigate the functional dynamic organization of complex protein assemblies involved in bacterial growth. Data are available via ProteomeXchange with identifier PXD006287.

  7. Maximum flow approach to prioritize potential drug targets of Mycobacterium tuberculosis H37Rv from protein-protein interaction network.

    Science.gov (United States)

    Melak, Tilahun; Gakkhar, Sunita

    2015-12-01

    In spite of the implementations of several strategies, tuberculosis (TB) is overwhelmingly a serious global public health problem causing millions of infections and deaths every year. This is mainly due to the emergence of drug-resistance varieties of TB. The current treatment strategies for the drug-resistance TB are of longer duration, more expensive and have side effects. This highlights the importance of identification and prioritization of targets for new drugs. This study has been carried out to prioritize potential drug targets of Mycobacterium tuberculosis H37Rv based on their flow to resistance genes. The weighted proteome interaction network of the pathogen was constructed using a dataset from STRING database. Only a subset of the dataset with interactions that have a combined score value ≥770 was considered. Maximum flow approach has been used to prioritize potential drug targets. The potential drug targets were obtained through comparative genome and network centrality analysis. The curated set of resistance genes was retrieved from literatures. Detail literature review and additional assessment of the method were also carried out for validation. A list of 537 proteins which are essential to the pathogen and non-homologous with human was obtained from the comparative genome analysis. Through network centrality measures, 131 of them were found within the close neighborhood of the centre of gravity of the proteome network. These proteins were further prioritized based on their maximum flow value to resistance genes and they are proposed as reliable drug targets of the pathogen. Proteins which interact with the host were also identified in order to understand the infection mechanism. Potential drug targets of Mycobacterium tuberculosis H37Rv were successfully prioritized based on their flow to resistance genes of existing drugs which is believed to increase the druggability of the targets since inhibition of a protein that has a maximum flow to

  8. Binding Direction-Based Two-Dimensional Flattened Contact Area Computing Algorithm for Protein-Protein Interactions.

    Science.gov (United States)

    Kang, Beom Sik; Pugalendhi, GaneshKumar; Kim, Ku-Jin

    2017-10-13

    Interactions between protein molecules are essential for the assembly, function, and regulation of proteins. The contact region between two protein molecules in a protein complex is usually complementary in shape for both molecules and the area of the contact region can be used to estimate the binding strength between two molecules. Although the area is a value calculated from the three-dimensional surface, it cannot represent the three-dimensional shape of the surface. Therefore, we propose an original concept of two-dimensional contact area which provides further information such as the ruggedness of the contact region. We present a novel algorithm for calculating the binding direction between two molecules in a protein complex, and then suggest a method to compute the two-dimensional flattened area of the contact region between two molecules based on the binding direction.

  9. PARPs database: A LIMS systems for protein-protein interaction data mining or laboratory information management system

    Directory of Open Access Journals (Sweden)

    Picard-Cloutier Aude

    2007-12-01

    Full Text Available Abstract Background In the "post-genome" era, mass spectrometry (MS has become an important method for the analysis of proteins and the rapid advancement of this technique, in combination with other proteomics methods, results in an increasing amount of proteome data. This data must be archived and analysed using specialized bioinformatics tools. Description We herein describe "PARPs database," a data analysis and management pipeline for liquid chromatography tandem mass spectrometry (LC-MS/MS proteomics. PARPs database is a web-based tool whose features include experiment annotation, protein database searching, protein sequence management, as well as data-mining of the peptides and proteins identified. Conclusion Using this pipeline, we have successfully identified several interactions of biological significance between PARP-1 and other proteins, namely RFC-1, 2, 3, 4 and 5.

  10. Structure/Function Analysis of Protein-Protein Interactions and Role of Dynamic Motions in Mercuric Ion Reductase

    Energy Technology Data Exchange (ETDEWEB)

    Miller, Susan M.

    2005-05-18

    This report summarizes the activities and findings of our structure/function studies of the bacterial detoxification enzyme mercuric ion reductase. The objectives of the work were to obtain crystal structure information for the catalytic core of this enzyme, use the information to investigate the importance of specific parts of the enzyme to its function, and investigate the role of one domain of the enzyme in its function within cells. We describe the accomplishments towards these goals including many structures of the wild type and mutant forms of the enzyme that highlight its interactions with its Hg(II) substrate, elucidation of the role of the N-terminal domain in vitro and in vivo, and elucidation of the roles of at two conserved residues in the core in the mechanism of catalysis.

  11. The mitosis-regulating and protein-protein interaction activities of astrin are controlled by aurora-A-induced phosphorylation.

    Science.gov (United States)

    Chiu, Shao-Chih; Chen, Jo-Mei Maureen; Wei, Tong-You Wade; Cheng, Tai-Shan; Wang, Ya-Hui Candice; Ku, Chia-Feng; Lian, Chiao-Hsuan; Liu, Chun-Chih Jared; Kuo, Yi-Chun; Yu, Chang-Tze Ricky

    2014-09-01

    Cells display dramatic morphological changes in mitosis, where numerous factors form regulatory networks to orchestrate the complicated process, resulting in extreme fidelity of the segregation of duplicated chromosomes into two daughter cells. Astrin regulates several aspects of mitosis, such as maintaining the cohesion of sister chromatids by inactivating Separase and stabilizing spindle, aligning and segregating chromosomes, and silencing spindle assembly checkpoint by interacting with Src kinase-associated phosphoprotein (SKAP) and cytoplasmic linker-associated protein-1α (CLASP-1α). To understand how Astrin is regulated in mitosis, we report here that Astrin acts as a mitotic phosphoprotein, and Aurora-A phosphorylates Astrin at Ser(115). The phosphorylation-deficient mutant Astrin S115A abnormally activates spindle assembly checkpoint and delays mitosis progression, decreases spindle stability, and induces chromosome misalignment. Mechanistic analyses reveal that Astrin phosphorylation mimicking mutant S115D, instead of S115A, binds and induces ubiquitination and degradation of securin, which sequentially activates Separase, an enzyme required for the separation of sister chromatids. Moreover, S115A fails to bind mitosis regulators, including SKAP and CLASP-1α, which results in the mitotic defects observed in Astrin S115A-transfected cells. In conclusion, Aurora-A phosphorylates Astrin and guides the binding of Astrin to its cellular partners, which ensures proper progression of mitosis. Copyright © 2014 the American Physiological Society.

  12. Relativistic rapprochement of weak and strong interactions

    International Nuclear Information System (INIS)

    Strel'tsov, V.N.

    1995-01-01

    On the basis of the relativistic Yukawa potentials for the nuclear (quark) field and the field of intermediate vector W-, Z-bosons, it is shown that the interactions described by them increase differently with growing velocity (the weak one increases more rapidly). According to the estimates, they are compared (at distances of the 'action radius' of nuclear forces) at an energy of about 10 12 GeV (10 6 GeV for the pion field) what is smaller than the corresponding value in the model of 'grand unification'. 3 refs., 2 tabs

  13. Strong Interactions, (De)coherence and Quarkonia

    CERN Document Server

    Bellucci, Stefano; Tiwari, Bhupendra Nath

    2011-01-01

    Quarkonia are the central objects to explore the non-perturbative nature of non-abelian gauge theories. We describe the confinement-deconfinement phases for heavy quarkonia in a hot QCD medium and thereby the statistical nature of the inter-quark forces. In the sense of one-loop quantum effects, we propose that the "quantum" nature of quark matters follows directly from the thermodynamic consideration of Richardson potential. Thereby we gain an understanding of the formation of hot and dense states of quark gluon plasma matter in heavy ion collisions and the early universe. In the case of the non-abelian theory, the consideration of the Sudhakov form factor turns out to be an efficient tool for soft gluons. In the limit of the Block-Nordsieck resummation, the strong coupling obtained from the Sudhakov form factor yields the statistical nature of hadronic bound states, e.g. kaons and Ds particles.

  14. Strong and electromagnetic interactions in hadron systems

    International Nuclear Information System (INIS)

    Aissat, N.; Amghar, A.; Cano, F.; Gonzalez, F.; Noguera, S.; Carbonell, J.; Desplanques, B.; Silvestre-Brac, B.; Karmanov, V.; Mathiot, J.F.

    1997-01-01

    The pionic strong decay amplitudes of baryon resonances are studied in a constituent quark model. Particular attention is given to the operator describing the transition. The nucleon form factors are calculated in a non-relativistic approach, with emphasis on the highest momentum transfers. The aim is to determine the ingredients that are essential in getting correct results and are likely to be required for a more realistic estimate in a fully relativistic approach. The deuteron form factors have been calculated in the light-front approach using wave functions determined in a perturbative way. The derivation of the neutron charge form factor from the deuteron structure function, A(q 2 ), is reanalyzed including further mesonic exchange contributions. (authors)

  15. "Strong interaction" for particle physics laboratories

    CERN Multimedia

    2003-01-01

    A new Web site pooling the communications resources of particle physics centres all over the world has just been launched. The official launching of the new particle physics website Interactions.org during the Lepton-Proton 2003 Conference at the American laboratory Fermilab was accompanied by music and a flurry of balloons. On the initiative of Fermilab, the site was created by a collaboration of communication teams from over fifteen of the world's particle physics laboratories, including KEK, SLAC, INFN, JINR and, of course, CERN, who pooled their efforts to develop the new tool. The spectacular launching of the new particle physics website Interactions.org at Fermilab on 12 August 2003. A real gateway to particle physics, the site not only contains all the latest news from the laboratories but also offers images, graphics and a video/animation link. In addition, it provides information about scientific policies, links to the universities, a very useful detailed glossary of particle physics and astrophysic...

  16. Supersymmetry and weak, electromagnetic and strong interactions

    International Nuclear Information System (INIS)

    Fayet, P.

    1977-01-01

    A supersymmetric theory of particle interactions is discussed. It is based on the earlier model which involves gauge (or vector) superfields, and matter (or chiral) superfields; each of them describes a vector and a Majorana spinor in the first case, or a two-component Dirac spinor and a complex scalar in the second case. The new theory suggests the possible existence of spin - 1/2 gluons and heavy spin-0 quarks, besides spin - 1 gluons and spin - 1/2 quarks. To prevent scalar particles to be exchanged in processes such as μ or β decays a new class of leptons with its own quantum number is introduced; it includes charged leptons and a ''photonic neutrino''

  17. Wiki-pi: a web-server of annotated human protein-protein interactions to aid in discovery of protein function.

    Directory of Open Access Journals (Sweden)

    Naoki Orii

    Full Text Available Protein-protein interactions (PPIs are the basis of biological functions. Knowledge of the interactions of a protein can help understand its molecular function and its association with different biological processes and pathways. Several publicly available databases provide comprehensive information about individual proteins, such as their sequence, structure, and function. There also exist databases that are built exclusively to provide PPIs by curating them from published literature. The information provided in these web resources is protein-centric, and not PPI-centric. The PPIs are typically provided as lists of interactions of a given gene with links to interacting partners; they do not present a comprehensive view of the nature of both the proteins involved in the interactions. A web database that allows search and retrieval based on biomedical characteristics of PPIs is lacking, and is needed. We present Wiki-Pi (read Wiki-π, a web-based interface to a database of human PPIs, which allows users to retrieve interactions by their biomedical attributes such as their association to diseases, pathways, drugs and biological functions. Each retrieved PPI is shown with annotations of both of the participant proteins side-by-side, creating a basis to hypothesize the biological function facilitated by the interaction. Conceptually, it is a search engine for PPIs analogous to PubMed for scientific literature. Its usefulness in generating novel scientific hypotheses is demonstrated through the study of IGSF21, a little-known gene that was recently identified to be associated with diabetic retinopathy. Using Wiki-Pi, we infer that its association to diabetic retinopathy may be mediated through its interactions with the genes HSPB1, KRAS, TMSB4X and DGKD, and that it may be involved in cellular response to external stimuli, cytoskeletal organization and regulation of molecular activity. The website also provides a wiki-like capability allowing users

  18. Effect of phospholipid, detergent and protein-protein interaction on stability and phosphoenzyme isomerization of soluble sarcoplasmic reticulum Ca-ATPase.

    Science.gov (United States)

    Vilsen, B; Andersen, J P

    1987-12-30

    The purpose of the present study was to elucidate the separate roles of lipid, detergent and protein-protein interaction for stability and catalytic properties of sarcoplasmic reticulum Ca-ATPase solubilized in the non-ionic detergent octa(ethylene glycol) monododecyl ether (C12E8). The use of large-zone high-performance liquid chromatography permitted us to define the self-association state of Ca-ATPase peptide at various detergent, phospholipid and protein concentrations, and also during enzymatic turnover with ATP. Conditions were established for monomerization of Ca-ATPase in the presence of a high concentration of phospholipid relative to detergent. The lipid-saturated monomeric preparation was relatively resistant to inactivation in the absence of Ca2+, whereas delipidated enzyme in monomeric or in oligomeric form was prone to inactivation. Kinetics of phosphoenzyme turnover were examined in the presence and absence of Mg2+. Dephosphorylation rates were sensitive to Mg2+, irrespective of whether the peptide was present in soluble monomeric form or was membrane-bound. C12E8-solubilized monomer without added phospholipid was, however, characterized by a fast initial phase of dephosphorylation in the absence of Mg2+. This was not observed with monomer saturated with phospholipid or with monomer solubilized in myristoylglycerophosphocholine or deoxycholate. The mechanism underlying this difference was shown to be a C12E8-induced acceleration of conversion of ADP-sensitive phosphoenzyme (E1P) to ADP-insensitive phosphoenzyme (E2P). The phosphoenzyme isomerization rate was also found to be enhanced by low-affinity binding of ATP. This was demonstrated both in membrane-bound and in soluble monomeric Ca-ATPase. Our results indicate that a single peptide chain constitutes the target for modulation of phosphoenzyme turnover by Mg2+ and ATP, and that detergent effects, distinct from those arising from disruption of protein-protein contacts, are the major determinants of

  19. The Use of Protein-Protein Interactions for the Analysis of the Associations between PM2.5 and Some Diseases

    Directory of Open Access Journals (Sweden)

    Qing Zhang

    2016-01-01

    Full Text Available Nowadays, pollution levels are rapidly increasing all over the world. One of the most important pollutants is PM2.5. It is known that the pollution environment may cause several problems, such as greenhouse effect and acid rain. Among them, the most important problem is that pollutants can induce a number of serious diseases. Some studies have reported that PM2.5 is an important etiologic factor for lung cancer. In this study, we extensively investigate the associations between PM2.5 and 22 disease classes recommended by Goh et al., such as respiratory diseases, cardiovascular diseases, and gastrointestinal diseases. The protein-protein interactions were used to measure the linkage between disease genes and genes that have been reported to be modulated by PM2.5. The results suggest that some diseases, such as diseases related to ear, nose, and throat and gastrointestinal, nutritional, renal, and cardiovascular diseases, are influenced by PM2.5 and some evidences were provided to confirm our results. For example, a total of 18 genes related to cardiovascular diseases are identified to be closely related to PM2.5, and cardiovascular disease relevant gene DSP is significantly related to PM2.5 gene JUP.

  20. The Use of Protein-Protein Interactions for the Analysis of the Associations between PM2.5 and Some Diseases.

    Science.gov (United States)

    Zhang, Qing; Zhang, Pei-Wei; Cai, Yu-Dong

    2016-01-01

    Nowadays, pollution levels are rapidly increasing all over the world. One of the most important pollutants is PM2.5. It is known that the pollution environment may cause several problems, such as greenhouse effect and acid rain. Among them, the most important problem is that pollutants can induce a number of serious diseases. Some studies have reported that PM2.5 is an important etiologic factor for lung cancer. In this study, we extensively investigate the associations between PM2.5 and 22 disease classes recommended by Goh et al., such as respiratory diseases, cardiovascular diseases, and gastrointestinal diseases. The protein-protein interactions were used to measure the linkage between disease genes and genes that have been reported to be modulated by PM2.5. The results suggest that some diseases, such as diseases related to ear, nose, and throat and gastrointestinal, nutritional, renal, and cardiovascular diseases, are influenced by PM2.5 and some evidences were provided to confirm our results. For example, a total of 18 genes related to cardiovascular diseases are identified to be closely related to PM2.5, and cardiovascular disease relevant gene DSP is significantly related to PM2.5 gene JUP.

  1. Identification of novel type 1 diabetes candidate genes by integrating genome-wide association data, protein-protein interactions, and human pancreatic islet gene expression

    DEFF Research Database (Denmark)

    Bergholdt, Regine; Brorsson, Caroline; Palleja, Albert

    2012-01-01

    Genome-wide association studies (GWAS) have heralded a new era in susceptibility locus discovery in complex diseases. For type 1 diabetes, >40 susceptibility loci have been discovered. However, GWAS do not inevitably lead to identification of the gene or genes in a given locus associated with dis......-cells. Our results provide novel insight to the mechanisms behind type 1 diabetes pathogenesis and, thus, may provide the basis for the design of novel treatment strategies.......Genome-wide association studies (GWAS) have heralded a new era in susceptibility locus discovery in complex diseases. For type 1 diabetes, >40 susceptibility loci have been discovered. However, GWAS do not inevitably lead to identification of the gene or genes in a given locus associated...... with disease, and they do not typically inform the broader context in which the disease genes operate. Here, we integrated type 1 diabetes GWAS data with protein-protein interactions to construct biological networks of relevance for disease. A total of 17 networks were identified. To prioritize...

  2. From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data.

    Science.gov (United States)

    Vella, Danila; Zoppis, Italo; Mauri, Giancarlo; Mauri, Pierluigi; Di Silvestre, Dario

    2017-12-01

    The reductionist approach of dissecting biological systems into their constituents has been successful in the first stage of the molecular biology to elucidate the chemical basis of several biological processes. This knowledge helped biologists to understand the complexity of the biological systems evidencing that most biological functions do not arise from individual molecules; thus, realizing that the emergent properties of the biological systems cannot be explained or be predicted by investigating individual molecules without taking into consideration their relations. Thanks to the improvement of the current -omics technologies and the increasing understanding of the molecular relationships, even more studies are evaluating the biological systems through approaches based on graph theory. Genomic and proteomic data are often combined with protein-protein interaction (PPI) networks whose structure is routinely analyzed by algorithms and tools to characterize hubs/bottlenecks and topological, functional, and disease modules. On the other hand, co-expression networks represent a complementary procedure that give the opportunity to evaluate at system level including organisms that lack information on PPIs. Based on these premises, we introduce the reader to the PPI and to the co-expression networks, including aspects of reconstruction and analysis. In particular, the new idea to evaluate large-scale proteomic data by means of co-expression networks will be discussed presenting some examples of application. Their use to infer biological knowledge will be shown, and a special attention will be devoted to the topological and module analysis.

  3. An Integrative Analysis of Preeclampsia Based on the Construction of an Extended Composite Network Featuring Protein-Protein Physical Interactions and Transcriptional Relationships.

    Directory of Open Access Journals (Sweden)

    Daniel Vaiman

    Full Text Available Preeclampsia (PE is a pregnancy disorder defined by hypertension and proteinuria. This disease remains a major cause of maternal and fetal morbidity and mortality. Defective placentation is generally described as being at the root of the disease. The characterization of the transcriptome signature of the preeclamptic placenta has allowed to identify differentially expressed genes (DEGs. However, we still lack a detailed knowledge on how these DEGs impact the function of the placenta. The tools of network biology offer a methodology to explore complex diseases at a systems level. In this study we performed a cross-platform meta-analysis of seven publically available gene expression datasets comparing non-pathological and preeclamptic placentas. Using the rank product algorithm we identified a total of 369 DEGs consistently modified in PE. The DEGs were used as seeds to build both an extended physical protein-protein interactions network and a transcription factors regulatory network. Topological and clustering analysis was conducted to analyze the connectivity properties of the networks. Finally both networks were merged into a composite network which presents an integrated view of the regulatory pathways involved in preeclampsia and the crosstalk between them. This network is a useful tool to explore the relationship between the DEGs and enable hypothesis generation for functional experimentation.

  4. 2P2I HUNTER: a tool for filtering orthosteric protein-protein interaction modulators via a dedicated support vector machine.

    Science.gov (United States)

    Hamon, Véronique; Bourgeas, Raphael; Ducrot, Pierre; Theret, Isabelle; Xuereb, Laura; Basse, Marie Jeanne; Brunel, Jean Michel; Combes, Sebastien; Morelli, Xavier; Roche, Philippe

    2014-01-06

    Over the last 10 years, protein-protein interactions (PPIs) have shown increasing potential as new therapeutic targets. As a consequence, PPIs are today the most screened target class in high-throughput screening (HTS). The development of broad chemical libraries dedicated to these particular targets is essential; however, the chemical space associated with this 'high-hanging fruit' is still under debate. Here, we analyse the properties of 40 non-redundant small molecules present in the 2P2I database (http://2p2idb.cnrs-mrs.fr/) to define a general profile of orthosteric inhibitors and propose an original protocol to filter general screening libraries using a support vector machine (SVM) with 11 standard Dragon molecular descriptors. The filtering protocol has been validated using external datasets from PubChem BioAssay and results from in-house screening campaigns. This external blind validation demonstrated the ability of the SVM model to reduce the size of the filtered chemical library by eliminating up to 96% of the compounds as well as enhancing the proportion of active compounds by up to a factor of 8. We believe that the resulting chemical space identified in this paper will provide the scientific community with a concrete support to search for PPI inhibitors during HTS campaigns.

  5. Signatures for strongly interacting W's and Z's

    International Nuclear Information System (INIS)

    Gaillard, M.K.

    1985-09-01

    The observed structure of the electroweak interactions is understood in terms of a spontaneously broken gauge theory. Although we have as yet no experimental indication as to the nature of the phenomenon responsible for symmetry breaking, general theoretical arguments set an upper limit of 1 or 2 TeV on the energy scale at which some manifestation of this phenomenon must occur. This scale defines a target for the effective hard collision energy that should be achieved in the next accelerator facility; the work reported here was aimed at sharpening this requirement by studying the minimal manifestations of electroweak symmetry breaking that can be expected to occur in the TeV energy region if a Higgs particle with m/sub H/ < 1 TeV is not found. While we used the minimal Higgs model as a guide, the results obtained are of far more general validity. Our analysis relied on three tools, briefly discussed. These are: the equivalence at high energies of longitudinally polarized W's and Z's to their scalar counterparts, the Goldstone bosons; the symmetries of the scalar sector; and the vector boson fusion process. 8 refs

  6. QCD : the theory of strong interactions Conference MT17

    CERN Multimedia

    2001-01-01

    The theory of strong interactions,Quantum Chromodynamics (QCD), predicts that the strong interaction is transmitted by the exchange of particles called gluons. Unlike the messengers of electromagnetism photons, which are electrically neutral - gluons carry a strong charge associated with the interaction they mediate. QCD predicts that the strength of the interaction between quarks and gluons becomes weaker at higher energies. LEP has measured the evolution of the strong coupling constant up to energies of 200 GeV and has confirmed this prediction.

  7. PCVMZM: Using the Probabilistic Classification Vector Machines Model Combined with a Zernike Moments Descriptor to Predict Protein-Protein Interactions from Protein Sequences.

    Science.gov (United States)

    Wang, Yanbin; You, Zhuhong; Li, Xiao; Chen, Xing; Jiang, Tonghai; Zhang, Jingting

    2017-05-11

    Protein-protein interactions (PPIs) are essential for most living organisms' process. Thus, detecting PPIs is extremely important to understand the molecular mechanisms of biological systems. Although many PPIs data have been generated by high-throughput technologies for a variety of organisms, the whole interatom is still far from complete. In addition, the high-throughput technologies for detecting PPIs has some unavoidable defects, including time consumption, high cost, and high error rate. In recent years, with the development of machine learning, computational methods have been broadly used to predict PPIs, and can achieve good prediction rate. In this paper, we present here PCVMZM, a computational method based on a Probabilistic Classification Vector Machines (PCVM) model and Zernike moments (ZM) descriptor for predicting the PPIs from protein amino acids sequences. Specifically, a Zernike moments (ZM) descriptor is used to extract protein evolutionary information from Position-Specific Scoring Matrix (PSSM) generated by Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then, PCVM classifier is used to infer the interactions among protein. When performed on PPIs datasets of Yeast and H. Pylori , the proposed method can achieve the average prediction accuracy of 94.48% and 91.25%, respectively. In order to further evaluate the performance of the proposed method, the state-of-the-art support vector machines (SVM) classifier is used and compares with the PCVM model. Experimental results on the Yeast dataset show that the performance of PCVM classifier is better than that of SVM classifier. The experimental results indicate that our proposed method is robust, powerful and feasible, which can be used as a helpful tool for proteomics research.

  8. Effective comparative analysis of protein-protein interaction networks by measuring the steady-state network flow using a Markov model.

    Science.gov (United States)

    Jeong, Hyundoo; Qian, Xiaoning; Yoon, Byung-Jun

    2016-10-06

    Comparative analysis of protein-protein interaction (PPI) networks provides an effective means of detecting conserved functional network modules across different species. Such modules typically consist of orthologous proteins with conserved interactions, which can be exploited to computationally predict the modules through network comparison. In this work, we propose a novel probabilistic framework for comparing PPI networks and effectively predicting the correspondence between proteins, represented as network nodes, that belong to conserved functional modules across the given PPI networks. The basic idea is to estimate the steady-state network flow between nodes that belong to different PPI networks based on a Markov random walk model. The random walker is designed to make random moves to adjacent nodes within a PPI network as well as cross-network moves between potential orthologous nodes with high sequence similarity. Based on this Markov random walk model, we estimate the steady-state network flow - or the long-term relative frequency of the transitions that the random walker makes - between nodes in different PPI networks, which can be used as a probabilistic score measuring their potential correspondence. Subsequently, the estimated scores can be used for detecting orthologous proteins in conserved functional modules through network alignment. Through evaluations based on multiple real PPI networks, we demonstrate that the proposed scheme leads to improved alignment results that are biologically more meaningful at reduced computational cost, outperforming the current state-of-the-art algorithms. The source code and datasets can be downloaded from http://www.ece.tamu.edu/~bjyoon/CUFID .

  9. Phospholipase D and phosphatidic acid in plant defence response: from protein-protein and lipid-protein interactions to hormone signalling.

    Science.gov (United States)

    Zhao, Jian

    2015-04-01

    Phospholipase Ds (PLDs) and PLD-derived phosphatidic acids (PAs) play vital roles in plant hormonal and environmental responses and various cellular dynamics. Recent studies have further expanded the functions of PLDs and PAs into plant-microbe interaction. The molecular diversities and redundant functions make PLD-PA an important signalling complex regulating lipid metabolism, cytoskeleton dynamics, vesicle trafficking, and hormonal signalling in plant defence through protein-protein and protein-lipid interactions or hormone signalling. Different PLD-PA signalling complexes and their targets have emerged as fast-growing research topics for understanding their numerous but not yet established roles in modifying pathogen perception, signal transduction, and downstream defence responses. Meanwhile, advanced lipidomics tools have allowed researchers to reveal further the mechanisms of PLD-PA signalling complexes in regulating lipid metabolism and signalling, and their impacts on jasmonic acid/oxylipins, salicylic acid, and other hormone signalling pathways that essentially mediate plant defence responses. This review attempts to summarize the progress made in spatial and temporal PLD/PA signalling as well as PLD/PA-mediated modification of plant defence. It presents an in-depth discussion on the functions and potential mechanisms of PLD-PA complexes in regulating actin filament/microtubule cytoskeleton, vesicle trafficking, and hormonal signalling, and in influencing lipid metabolism-derived metabolites as critical signalling components in plant defence responses. The discussion puts PLD-PA in a broader context in order to guide future research. © The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

  10. Dynamics of Strong Interactions and the S-Matrix

    Energy Technology Data Exchange (ETDEWEB)

    Omnes, R. [Laboratoire de Physique Theorique et Hautes Energies, Universite de Paris, Orsay (France)

    1969-08-15

    The physical principles underlying the S-matrix theory of strong interactions are reviewed. In particular, the problem of whether these principles are sufficient to completely determine the S-matrix, i.e. to yield a dynamical theory of strong interactions, is discussed. (author)

  11. Strong enhancement of transport by interaction on contact links

    DEFF Research Database (Denmark)

    Bohr, Dan; Schmitteckert, P.

    2007-01-01

    Strong repulsive interactions within a one-dimensional Fermi system in a two-probe configuration normally lead to a reduced off-resonance conductance. We show that if the repulsive interaction extends to the contact regions, a strong increase of the conductance may occur, even for systems where o...

  12. QCD : the theory of strong interactions Exhibition LEPFest 2000

    CERN Multimedia

    2000-01-01

    The theory of strong interactions,Quantum Chromodynamics (QCD),predicts that the strong interac- tion is transmitted by the exchange of particles called glu- ons.Unlike the messengers of electromagnetism -pho- tons,which are electrically neutral -gluons carry a strong charge associated with the interaction they mediate. QCD predicts that the strength of the interaction between quarks and gluons becomes weaker at higher energies.LEP has measured the evolution of the strong coupling constant up to energies of 200 GeV and has confirmed this prediction.

  13. Numerical Calculation of the Phase Space Density for the Strong-Strong Beam-Beam Interaction

    International Nuclear Information System (INIS)

    Sobol, A.; Ellison, J.A.

    2003-01-01

    We developed a parallel code to calculate the evolution of the 4D phase space density of two colliding beams, which are coupled via the collective strong-strong beam-beam interaction, in the absence of diffusion and damping, using the Perron-Frobenius (PF) operator technique

  14. Quantitative real-time PCR as a sensitive protein-protein interaction quantification method and a partial solution for non-accessible autoactivator and false-negative molecule analysis in the yeast two-hybrid system.

    Science.gov (United States)

    Maier, Richard H; Maier, Christina J; Hintner, Helmut; Bauer, Johann W; Onder, Kamil

    2012-12-01

    Many functional proteomic experiments make use of high-throughput technologies such as mass spectrometry combined with two-dimensional polyacrylamide gel electrophoresis and the yeast two-hybrid (Y2H) system. Currently there are even automated versions of the Y2H system available that can be used for proteome-wide research. The Y2H system has the capacity to deliver a profusion of Y2H positive colonies from a single library screen. However, subsequent analysis of these numerous primary candidates with complementary methods can be overwhelming. Therefore, a method to select the most promising candidates with strong interaction properties might be useful to reduce the number of candidates requiring further analysis. The method described here offers a new way of quantifying and rating the performance of positive Y2H candidates. The novelty lies in the detection and measurement of mRNA expression instead of proteins or conventional Y2H genetic reporters. This method correlates well with the direct genetic reporter readouts usually used in the Y2H system, and has greater sensitivity for detecting and quantifying protein-protein interactions (PPIs) than the conventional Y2H system, as demonstrated by detection of the Y2H false-negative PPI of RXR/PPARG. Approximately 20% of all proteins are not suitable for the Y2H system, the so-called autoactivators. A further advantage of this method is the possibility to evaluate molecules that usually cannot be analyzed in the Y2H system, exemplified by a VDR-LXXLL motif peptide interaction. Copyright © 2012 Elsevier Inc. All rights reserved.

  15. Prospects for strong interaction physics at ISABELLE. [Seven papers

    Energy Technology Data Exchange (ETDEWEB)

    Sidhu, D P; Trueman, T L

    1977-01-01

    Seven papers are presented resulting from a conference intended to stimulate thinking about how ISABELLE could be used for studying strong interactions. A separate abstract was prepared for each paper for inclusion in DOE Energy Research Abstracts (ERA). (PMA)

  16. A theory of strong interactions ''from'' general relativity

    International Nuclear Information System (INIS)

    Caldirola, P.; Recami, E.

    1979-01-01

    In this paper a previous letter (where, among other things, a classical ''quark confinement'' was derived from general relativity plus dilatation-covariance), is completed by showing that the theory is compatible also with quarks ''asymptotic freedom''. Then -within a bi-scale theory of gravitational and strong interactions- a classical field theory is proposed for the (strong) interactions between hadrons. Various consequences are briefly analysed

  17. Measurement of strong interaction effects in antiprotonic helium atoms

    International Nuclear Information System (INIS)

    Davies, J.D.; Gorringe, T.P.; Lowe, J.; Nelson, J.M.; Playfer, S.M.; Pyle, G.J.; Squier, G.T.A.

    1984-01-01

    The strong interaction shift and width for the 2 p level and the width for the 3d level have been measured for antiprotonic helium atoms. The results are compared with optical model calculations. The possible existence of strongly bound antiproton states in nuclei is discussed. (orig.)

  18. The Protein-Protein Interaction tasks of BioCreative III: classification/ranking of articles and linking bio-ontology concepts to full text.

    Science.gov (United States)

    Krallinger, Martin; Vazquez, Miguel; Leitner, Florian; Salgado, David; Chatr-Aryamontri, Andrew; Winter, Andrew; Perfetto, Livia; Briganti, Leonardo; Licata, Luana; Iannuccelli, Marta; Castagnoli, Luisa; Cesareni, Gianni; Tyers, Mike; Schneider, Gerold; Rinaldi, Fabio; Leaman, Robert; Gonzalez, Graciela; Matos, Sergio; Kim, Sun; Wilbur, W John; Rocha, Luis; Shatkay, Hagit; Tendulkar, Ashish V; Agarwal, Shashank; Liu, Feifan; Wang, Xinglong; Rak, Rafal; Noto, Keith; Elkan, Charles; Lu, Zhiyong; Dogan, Rezarta Islamaj; Fontaine, Jean-Fred; Andrade-Navarro, Miguel A; Valencia, Alfonso

    2011-10-03

    Determining usefulness of biomedical text mining systems requires realistic task definition and data selection criteria without artificial constraints, measuring performance aspects that go beyond traditional metrics. The BioCreative III Protein-Protein Interaction (PPI) tasks were motivated by such considerations, trying to address aspects including how the end user would oversee the generated output, for instance by providing ranked results, textual evidence for human interpretation or measuring time savings by using automated systems. Detecting articles describing complex biological events like PPIs was addressed in the Article Classification Task (ACT), where participants were asked to implement tools for detecting PPI-describing abstracts. Therefore the BCIII-ACT corpus was provided, which includes a training, development and test set of over 12,000 PPI relevant and non-relevant PubMed abstracts labeled manually by domain experts and recording also the human classification times. The Interaction Method Task (IMT) went beyond abstracts and required mining for associations between more than 3,500 full text articles and interaction detection method ontology concepts that had been applied to detect the PPIs reported in them. A total of 11 teams participated in at least one of the two PPI tasks (10 in ACT and 8 in the IMT) and a total of 62 persons were involved either as participants or in preparing data sets/evaluating these tasks. Per task, each team was allowed to submit five runs offline and another five online via the BioCreative Meta-Server. From the 52 runs submitted for the ACT, the highest Matthew's Correlation Coefficient (MCC) score measured was 0.55 at an accuracy of 89% and the best AUC iP/R was 68%. Most ACT teams explored machine learning methods, some of them also used lexical resources like MeSH terms, PSI-MI concepts or particular lists of verbs and nouns, some integrated NER approaches. For the IMT, a total of 42 runs were evaluated by comparing

  19. The Protein-Protein Interaction tasks of BioCreative III: classification/ranking of articles and linking bio-ontology concepts to full text

    Science.gov (United States)

    2011-01-01

    Background Determining usefulness of biomedical text mining systems requires realistic task definition and data selection criteria without artificial constraints, measuring performance aspects that go beyond traditional metrics. The BioCreative III Protein-Protein Interaction (PPI) tasks were motivated by such considerations, trying to address aspects including how the end user would oversee the generated output, for instance by providing ranked results, textual evidence for human interpretation or measuring time savings by using automated systems. Detecting articles describing complex biological events like PPIs was addressed in the Article Classification Task (ACT), where participants were asked to implement tools for detecting PPI-describing abstracts. Therefore the BCIII-ACT corpus was provided, which includes a training, development and test set of over 12,000 PPI relevant and non-relevant PubMed abstracts labeled manually by domain experts and recording also the human classification times. The Interaction Method Task (IMT) went beyond abstracts and required mining for associations between more than 3,500 full text articles and interaction detection method ontology concepts that had been applied to detect the PPIs reported in them. Results A total of 11 teams participated in at least one of the two PPI tasks (10 in ACT and 8 in the IMT) and a total of 62 persons were involved either as participants or in preparing data sets/evaluating these tasks. Per task, each team was allowed to submit five runs offline and another five online via the BioCreative Meta-Server. From the 52 runs submitted for the ACT, the highest Matthew's Correlation Coefficient (MCC) score measured was 0.55 at an accuracy of 89% and the best AUC iP/R was 68%. Most ACT teams explored machine learning methods, some of them also used lexical resources like MeSH terms, PSI-MI concepts or particular lists of verbs and nouns, some integrated NER approaches. For the IMT, a total of 42 runs were

  20. Interaction between Electron Holes in a Strongly Magnetized Plasma

    DEFF Research Database (Denmark)

    Lynov, Jens-Peter; Michelsen, Poul; Pécseli, Hans

    1980-01-01

    The interaction between electron holes in a strongly magnetized, plasma-filled waveguide is investigated by means of computer simulation. Two holes may or may not coalesce, depending on their amplitudes and velocities. The interaction between holes and Trivelpiece-Gould solitons is demonstrated...

  1. Quark imprisonment as the origin of strong interactions

    CERN Document Server

    Amati, Daniele

    1974-01-01

    A formal scheme is suggested in which the only dynamical ingredients are weak and electro-magnetic interactions with quarks and leptons treated on the same footing. Strong interactions are generated by the requirement that quarks do not appear physically. (7 refs).

  2. Immobilized Cytochrome P450 2C9 (CYP2C9): Applications for Metabolite Generation, Monitoring Protein-Protein Interactions, and Improving In-vivo Predictions Using Enhanced In-vitro Models

    Science.gov (United States)

    Wollenberg, Lance A.

    Cytochrome P450 (P450) enzymes are a family of oxoferroreductase enzymes containing a heme moiety and are well known to be involved in the metabolism of a wide variety of endogenous and xenobiotic materials. It is estimated that roughly 75% of all pharmaceutical compounds are metabolized by these enzymes. Traditional reconstituted in-vitro incubation studies using recombinant P450 enzymes are often used to predict in-vivo kinetic parameters of a drug early in development. However, in many cases, these reconstituted incubations are prone to aggregation which has been shown to affect the catalytic activity of an enzyme. Moreover, the presence of other isoforms of P450 enzymes present in a metabolic incubation, as is the case with microsomal systems, may affect the catalytic activity of an enzyme through isoform-specific protein-protein interactions. Both of these effects may result in inaccurate prediction of in-vivo drug metabolism using in-vitro experiments. Here we described the development of immobilized P450 constructs designed to elucidate the effects of aggregation and protein-protein interactions between P450 isoforms on catalytic activities. The long term objective of this project is to develop a system to control the oligomeric state of Cytochrome P450 enzymes to accurately elucidate discrepancies between in vitro reconstituted systems and actual in vivo drug metabolism for the precise prediction of metabolic activity. This approach will serve as a system to better draw correlations between in-vivo and in-vitro drug metabolism data. The central hypothesis is that Cytochrome P450 enzymes catalytic activity can be altered by protein-protein interactions occurring between Cytochrome P450 enzymes involved in drug metabolism, and is dependent on varying states of protein aggregation. This dissertation explains the details of the construction and characterization of a nanostructure device designed to control the state of aggregation of a P450 enzyme. Moreover

  3. Substructure and strong interactions at the TeV scale

    International Nuclear Information System (INIS)

    Peskin, M.E.

    1985-12-01

    A review is given of the current status of the three main theoretical ideas relevant to strong-interaction 1 TeV physics. These are composite vector bosons, Higgs bosons (''Technicolor''), and matter fermions. All involve the assumption that some object which is assumed to be fundamental in the standard model actually has dynamical internal structure. Complex, mechanistic models of the new physics are discussed. A brief digression is then made on how the weak interaction allows probing for this new structure. Direct manifestations of new 1 TeV strong interactions are discussed. 125 refs., 18 figs

  4. Semicalssical quantization of interacting anyons in a strong magnetic field

    International Nuclear Information System (INIS)

    Levit, S.; Sivan, N.

    1992-01-01

    We represent a semiclassical theory of charged interacting anyons in strong magnetic fields. We apply this theory to a number of few anyons systems including two interacting anyons in the presence of an impurity and three interacting anyons. We discuss the dependence of their energy levels on the statistical parameter and find regions in which this dependence follows very different patterns. The semiclassical arguments allow to correlate these patterns with the change in the character of the classical motion of the system. (author)

  5. Interaction between holo transferrin and HSA-PPIX complex in the presence of lomefloxacin: An evaluation of PPIX aggregation in protein-protein interactions

    Science.gov (United States)

    Sattar, Zohreh; Iranfar, Hediye; Asoodeh, Ahmad; Saberi, Mohammad Reza; Mazhari, Mahboobeh; Chamani, Jamshidkhan

    2012-11-01

    Human serum albumin (HSA) and holo transferrin (TF) are two serum carrier proteins that are able to interact with each other, thereby altering their binding behavior toward their ligands. During the course of this study, the interaction between HSA-PPIX and TF, in the presence and absence of lomefloxacin (LMF), was for the first time investigated using different spectroscopic and molecular modeling techniques. Fluorescence spectroscopy experiments were performed in order to study conformational changes of proteins. The RLS technique was utilized to investigate the effect of LMF on J-aggregation of PPIX, which is the first report of its kind. Our findings present clear-cut evidence for the alteration of interactions between HSA and TF in the presence of PPIX and changes in drug-binding to HSA and HSA-PPIX complex upon interaction with TF. Moreover, molecular modeling studies suggested that the binding site for LMF became switched in the presence of PPIX, and that LMF bound to the site IIA of HSA. The obtained results should give new insight into research in this field and may cast some light on the dynamics of drugs in biological systems.

  6. SpirPro: A Spirulina proteome database and web-based tools for the analysis of protein-protein interactions at the metabolic level in Spirulina (Arthrospira) platensis C1.

    Science.gov (United States)

    Senachak, Jittisak; Cheevadhanarak, Supapon; Hongsthong, Apiradee

    2015-07-29

    Spirulina (Arthrospira) platensis is the only cyanobacterium that in addition to being studied at the molecular level and subjected to gene manipulation, can also be mass cultivated in outdoor ponds for commercial use as a food supplement. Thus, encountering environmental changes, including temperature stresses, is common during the mass production of Spirulina. The use of cyanobacteria as an experimental platform, especially for photosynthetic gene manipulation in plants and bacteria, is becoming increasingly important. Understanding the mechanisms and protein-protein interaction networks that underlie low- and high-temperature responses is relevant to Spirulina mass production. To accomplish this goal, high-throughput techniques such as OMICs analyses are used. Thus, large datasets must be collected, managed and subjected to information extraction. Therefore, databases including (i) proteomic analysis and protein-protein interaction (PPI) data and (ii) domain/motif visualization tools are required for potential use in temperature response models for plant chloroplasts and photosynthetic bacteria. A web-based repository was developed including an embedded database, SpirPro, and tools for network visualization. Proteome data were analyzed integrated with protein-protein interactions and/or metabolic pathways from KEGG. The repository provides various information, ranging from raw data (2D-gel images) to associated results, such as data from interaction and/or pathway analyses. This integration allows in silico analyses of protein-protein interactions affected at the metabolic level and, particularly, analyses of interactions between and within the affected metabolic pathways under temperature stresses for comparative proteomic analysis. The developed tool, which is coded in HTML with CSS/JavaScript and depicted in Scalable Vector Graphics (SVG), is designed for interactive analysis and exploration of the constructed network. SpirPro is publicly available on the web

  7. Mixtures of Strongly Interacting Bosons in Optical Lattices

    International Nuclear Information System (INIS)

    Buonsante, P.; Penna, V.; Giampaolo, S. M.; Illuminati, F.; Vezzani, A.

    2008-01-01

    We investigate the properties of strongly interacting heteronuclear boson-boson mixtures loaded in realistic optical lattices, with particular emphasis on the physics of interfaces. In particular, we numerically reproduce the recent experimental observation that the addition of a small fraction of 41 K induces a significant loss of coherence in 87 Rb, providing a simple explanation. We then investigate the robustness against the inhomogeneity typical of realistic experimental realizations of the glassy quantum emulsions recently predicted to occur in strongly interacting boson-boson mixtures on ideal homogeneous lattices

  8. A Cross-Species Study of PI3K Protein-Protein Interactions Reveals the Direct Interaction of P85 and SHP2

    Science.gov (United States)

    Breitkopf, Susanne B.; Yang, Xuemei; Begley, Michael J.; Kulkarni, Meghana; Chiu, Yu-Hsin; Turke, Alexa B.; Lauriol, Jessica; Yuan, Min; Qi, Jie; Engelman, Jeffrey A.; Hong, Pengyu; Kontaridis, Maria I.; Cantley, Lewis C.; Perrimon, Norbert; Asara, John M.

    2016-02-01

    Using a series of immunoprecipitation (IP) - tandem mass spectrometry (LC-MS/MS) experiments and reciprocal BLAST, we conducted a fly-human cross-species comparison of the phosphoinositide-3-kinase (PI3K) interactome in a drosophila S2R+ cell line and several NSCLC and human multiple myeloma cell lines to identify conserved interacting proteins to PI3K, a critical signaling regulator of the AKT pathway. Using H929 human cancer cells and drosophila S2R+ cells, our data revealed an unexpected direct binding of Corkscrew, the drosophila ortholog of the non-receptor protein tyrosine phosphatase type II (SHP2) to the Pi3k21B (p60) regulatory subunit of PI3K (p50/p85 human ortholog) but no association with Pi3k92e, the human ortholog of the p110 catalytic subunit. The p85-SHP2 association was validated in human cell lines, and formed a ternary regulatory complex with GRB2-associated-binding protein 2 (GAB2). Validation experiments with knockdown of GAB2 and Far-Western blots proved the direct interaction of SHP2 with p85, independent of adaptor proteins and transfected FLAG-p85 provided evidence that SHP2 binding on p85 occurred on the SH2 domains. A disruption of the SHP2-p85 complex took place after insulin/IGF1 stimulation or imatinib treatment, suggesting that the direct SHP2-p85 interaction was both independent of AKT activation and positively regulates the ERK signaling pathway.

  9. New results on strong-interaction effects in antiprotonic hydrogen

    CERN Document Server

    Gotta, D; Augsburger, M A; Borchert, G L; Castelli, C M; Chatellard, D; El-Khoury, P; Egger, J P; Gorke, H; Hauser, P R; Indelicato, P J; Kirch, K; Lenz, S; Nelms, N; Rashid, K; Schult, O W B; Siems, T; Simons, L M

    1999-01-01

    Lyman and Balmer transitions of antiprotonic hydrogen and deuterium have been measured at the low-energy antiproton ring LEAR at CERN in order to determine the strong interaction effects. The X-rays were detected using charge-coupled devices (CCDs) and a reflection type crystal spectrometer. The results of the measurements support the meson-exchange models describing the medium and long range part of the nucleon-antinucleon interaction. (33 refs).

  10. New results on strong-interaction effects in antiprotonic hydrogen

    International Nuclear Information System (INIS)

    Anagnostopoulos, D. F.; Augsburger, M.; Borchert, G.; Castelli, C.; Chatellard, D.; El-Khoury, P.; Egger, J.-P.; Gorke, H.; Gotta, D.; Hauser, P.; Indelicato, P.; Kirch, K.; Lenz, S.; Nelms, N.; Rashid, K.; Schult, O. W. B.; Siems, Th.; Simons, L. M.

    1999-01-01

    Lyman and Balmer transitions of antiprotonic hydrogen and deuterium have been measured at the Low-Energy Antiproton Ring LEAR at CERN in order to determine the strong interaction effects. The X-rays were detected using Charge-Coupled Devices (CCDs) and a reflection type crystal spectrometer. The results of the measurements support the meson-exchange models describing the medium and long range part of the nucleon-antinucleon interaction

  11. H. David Politzer, Asymptotic Freedom, and Strong Interaction

    Science.gov (United States)

    dropdown arrow Site Map A-Z Index Menu Synopsis H. David Politzer, Asymptotic Freedom, and Strong Interaction Resources with Additional Information H. David Politzer Photo Credit: California Institute of Technology H. David Politzer has won the 2004 Nobel Prize in Physics 'for the discovery of asymptotic freedom

  12. Emergence of junction dynamics in a strongly interacting Bose mixture

    DEFF Research Database (Denmark)

    Barfknecht, Rafael Emilio; Foerster, Angela; Zinner, Nikolaj Thomas

    We study the dynamics of a one-dimensional system composed of a bosonic background and one impurity in single- and double-well trapping geometries. In the limit of strong interactions, this system can be modeled by a spin chain where the exchange coefficients are determined by the geometry of the...

  13. Interplay of Anderson localization and strong interaction in disordered systems

    International Nuclear Information System (INIS)

    Henseler, Peter

    2010-01-01

    We study the interplay of disorder localization and strong local interactions within the Anderson-Hubbard model. Taking into account local Mott-Hubbard physics and static screening of the disorder potential, the system is mapped onto an effective single-particle Anderson model, which is studied within the self-consistent theory of electron localization. For fermions, we find rich nonmonotonic behavior of the localization length ξ, particularly in two-dimensional systems, including an interaction-induced exponential enhancement of ξ for small and intermediate disorders and a strong reduction of ξ due to hopping suppression by strong interactions. In three dimensions, we identify for half filling a Mott-Hubbard-assisted Anderson localized phase existing between the metallic and the Mott-Hubbard-gapped phases. For small U there is re-entrant behavior from the Anderson localized phase to the metallic phase. For bosons, the unrestricted particle occupation number per lattice site yields a monotonic enhancement of ξ as a function of decreasing interaction, which we assume to persist until the superfluid Bose-Einstein condensate phase is entered. Besides, we study cold atomic gases expanding, by a diffusion process, in a weak random potential. We show that the density-density correlation function of the expanding gas is strongly affected by disorder and we estimate the typical size of a speckle spot, i.e., a region of enhanced or depleted density. Both a Fermi gas and a Bose-Einstein condensate (in a mean-field approach) are considered. (orig.)

  14. Interplay of Anderson localization and strong interaction in disordered systems

    Energy Technology Data Exchange (ETDEWEB)

    Henseler, Peter

    2010-01-15

    We study the interplay of disorder localization and strong local interactions within the Anderson-Hubbard model. Taking into account local Mott-Hubbard physics and static screening of the disorder potential, the system is mapped onto an effective single-particle Anderson model, which is studied within the self-consistent theory of electron localization. For fermions, we find rich nonmonotonic behavior of the localization length {xi}, particularly in two-dimensional systems, including an interaction-induced exponential enhancement of {xi} for small and intermediate disorders and a strong reduction of {xi} due to hopping suppression by strong interactions. In three dimensions, we identify for half filling a Mott-Hubbard-assisted Anderson localized phase existing between the metallic and the Mott-Hubbard-gapped phases. For small U there is re-entrant behavior from the Anderson localized phase to the metallic phase. For bosons, the unrestricted particle occupation number per lattice site yields a monotonic enhancement of {xi} as a function of decreasing interaction, which we assume to persist until the superfluid Bose-Einstein condensate phase is entered. Besides, we study cold atomic gases expanding, by a diffusion process, in a weak random potential. We show that the density-density correlation function of the expanding gas is strongly affected by disorder and we estimate the typical size of a speckle spot, i.e., a region of enhanced or depleted density. Both a Fermi gas and a Bose-Einstein condensate (in a mean-field approach) are considered. (orig.)

  15. Quantum transport in strongly interacting one-dimensional nanostructures

    NARCIS (Netherlands)

    Agundez, R.R.

    2015-01-01

    In this thesis we study quantum transport in several one-dimensional systems with strong electronic interactions. The first chapter contains an introduction to the concepts treated throughout this thesis, such as the Aharonov-Bohm effect, the Kondo effect, the Fano effect and quantum state transfer.

  16. Discriminative deep inelastic tests of strong interaction field theories

    International Nuclear Information System (INIS)

    Glueck, M.; Reya, E.

    1979-02-01

    It is demonstrated that recent measurements of ∫ 0 1 F 2 (x, Q 2 )dx eliminate already all strong interaction field theories except QCD. A detailed study of scaling violations of F 2 (x, Q 2 ) in QCD shows their insensitivity to the gluon content of the hadron at presently measured values of Q 2 . (orig.) [de

  17. Two-dimensional QCD as a model for strong interaction

    International Nuclear Information System (INIS)

    Ellis, J.

    1977-01-01

    After an introduction to the formalism of two-dimensional QCD, its applications to various strong interaction processes are reviewed. Among the topics discussed are spectroscopy, deep inelastic cross-sections, ''hard'' processes involving hadrons, ''Regge'' behaviour, the existence of the Pomeron, and inclusive hadron cross-sections. Attempts are made to abstracts features useful for four-dimensional QCD phenomenology. (author)

  18. Measurement of strong interaction parameters in antiprotonic hydrogen and deuterium

    CERN Document Server

    Augsburger, M A; Borchert, G L; Chatellard, D; Egger, J P; El-Khoury, P; Gorke, H; Gotta, D; Hauser, P R; Indelicato, P J; Kirch, K; Lenz, S; Siems, T; Simons, L M

    1999-01-01

    In the PS207 experiment at CERN, X-rays from antiprotonic hydrogen and deuterium have been measured at low pressure. The strong interaction shift and the broadening of the K/sub alpha / transition in antiprotonic hydrogen were $9 determined. Evidence was found for the individual hyperfine components of the protonium ground state. (7 refs).

  19. A systematic study of the strong interaction with PANDA

    NARCIS (Netherlands)

    Messchendorp, J. G.; Hosaka, A; Khemchandani, K; Nagahiro, H; Nawa, K

    2011-01-01

    The theory of Quantum Chromo Dynamics (QCD) reproduces the strong interaction at distances much shorter than the size of the nucleon. At larger distance scales, the generation of hadron masses and confinement cannot yet be derived from first principles on basis of QCD. The PANDA experiment at FAIR

  20. On the strong crack-microcrack interaction problem

    Science.gov (United States)

    Gorelik, M.; Chudnovsky, A.

    1992-07-01

    The problem of the crack-microcrack interaction is examined with special attention given to the iterative procedure described by Chudnovsky and Kachanov (1983), Chudnovsky et al. (1984), and Horii and Nemat-Nasser (1983), which yields erroneous results as the crack tips become closer (i.e., for strong crack interaction). To understand the source of error, the traction distributions along the microcrack line on the n-th step of iteration representing the exact and asymptotic stress fields are compared. It is shown that the asymptotic solution gives a gross overestimation of the actual traction.

  1. Ruling out a strongly interacting standard Higgs model

    International Nuclear Information System (INIS)

    Riesselmann, K.; Willenbrock, S.

    1997-01-01

    Previous work has suggested that perturbation theory is unreliable for Higgs- and Goldstone-boson scattering, at energies above the Higgs-boson mass, for relatively small values of the Higgs quartic coupling λ(μ). By performing a summation of nonlogarithmic terms, we show that perturbation theory is in fact reliable up to relatively large coupling. This eliminates the possibility of a strongly interacting standard Higgs model at energies above the Higgs-boson mass, complementing earlier studies which excluded strong interactions at energies near the Higgs-boson mass. The summation can be formulated in terms of an appropriate scale in the running coupling, μ=√(s)/e∼√(s)/2.7, so it can be incorporated easily in renormalization-group-improved tree-level amplitudes as well as higher-order calculations. copyright 1996 The American Physical Society

  2. Atom-Pair Kinetics with Strong Electric-Dipole Interactions.

    Science.gov (United States)

    Thaicharoen, N; Gonçalves, L F; Raithel, G

    2016-05-27

    Rydberg-atom ensembles are switched from a weakly to a strongly interacting regime via adiabatic transformation of the atoms from an approximately nonpolar into a highly dipolar quantum state. The resultant electric dipole-dipole forces are probed using a device akin to a field ion microscope. Ion imaging and pair-correlation analysis reveal the kinetics of the interacting atoms. Dumbbell-shaped pair-correlation images demonstrate the anisotropy of the binary dipolar force. The dipolar C_{3} coefficient, derived from the time dependence of the images, agrees with the value calculated from the permanent electric-dipole moment of the atoms. The results indicate many-body dynamics akin to disorder-induced heating in strongly coupled particle systems.

  3. The hadronic standard model for strong and electroweak interactions

    International Nuclear Information System (INIS)

    Raczka, R.

    1993-01-01

    We propose a new model for strong and electro-weak interactions. First, we review various QCD predictions for hadron-hadron and lepton-hadron processes. We indicate that the present formulation of strong interactions in the frame work of Quantum Chromodynamics encounters serious conceptual and numerical difficulties in a reliable description of hadron-hadron and lepton-hadron interactions. Next we propose to replace the strong sector of Standard Model based on unobserved quarks and gluons by the strong sector based on the set of the observed baryons and mesons determined by the spontaneously broken SU(6) gauge field theory model. We analyse various properties of this model such as asymptotic freedom, Reggeization of gauge bosons and fundamental fermions, baryon-baryon and meson-baryon high energy scattering, generation of Λ-polarization in inclusive processes and others. Finally we extend this model by electro-weak sector. We demonstrate a remarkable lepton and hadron anomaly cancellation and we analyse a series of important lepton-hadron and hadron-hadron processes such as e + + e - → hadrons, e + + e - → W + + W - , e + + e - → p + anti-p, e + p → e + p and p + anti-p → p + anti-p processes. We obtained a series of interesting new predictions in this model especially for processes with polarized particles. We estimated the value of the strong coupling constant α(M z ) and we predicted the top baryon mass M Λ t ≅ 240 GeV. Since in our model the proton, neutron, Λ-particles, vector mesons like ρ, ω, φ, J/ψ ect. and leptons are elementary most of experimentally analysed lepton-hadron and hadron-hadron processes in LEP1, LEP2, LEAR, HERA, HERMES, LHC and SSC experiments may be relatively easily analysed in our model. (author). 252 refs, 65 figs, 1 tab

  4. A connection between the strong and weak interactions

    International Nuclear Information System (INIS)

    Treiman, S.B.

    1989-01-01

    By studying weak scattering reactions (such as pion-nucleon scattering), the author and his colleague Marvin L Goldberger became renowned in the 1950s for work on dispersion relations. As a result of their collaboration a remarkable and unexpected connection was found between strong and weak interaction quantities. Agreement with experiment was good. Work by others found the same result, but via the partially conserved axial reactor current relation between the axial current divergence and the canonical pion field. (UK)

  5. Relative Nonlinear Electrodynamics Interaction of Charged Particles with Strong and Super Strong Laser Fields

    CERN Document Server

    Avetissian, Hamlet

    2006-01-01

    This book covers a large class of fundamental investigations into Relativistic Nonlinear Electrodynamics. It explores the interaction between charged particles and strong laser fields, mainly concentrating on contemporary problems of x-ray lasers, new type small set-up high-energy accelerators of charged particles, as well as electron-positron pair production from super powerful laser fields of relativistic intensities. It will also discuss nonlinear phenomena of threshold nature that eliminate the concurrent inverse processes in the problems of Laser Accelerator and Free Electron Laser, thus creating new opportunities for solving these problems.

  6. Application of encoded library technology (ELT) to a protein-protein interaction target: discovery of a potent class of integrin lymphocyte function-associated antigen 1 (LFA-1) antagonists.

    Science.gov (United States)

    Kollmann, Christopher S; Bai, Xiaopeng; Tsai, Ching-Hsuan; Yang, Hongfang; Lind, Kenneth E; Skinner, Steven R; Zhu, Zhengrong; Israel, David I; Cuozzo, John W; Morgan, Barry A; Yuki, Koichi; Xie, Can; Springer, Timothy A; Shimaoka, Motomu; Evindar, Ghotas

    2014-04-01

    The inhibition of protein-protein interactions remains a challenge for traditional small molecule drug discovery. Here we describe the use of DNA-encoded library technology for the discovery of small molecules that are potent inhibitors of the interaction between lymphocyte function-associated antigen 1 and its ligand intercellular adhesion molecule 1. A DNA-encoded library with a potential complexity of 4.1 billion compounds was exposed to the I-domain of the target protein and the bound ligands were affinity selected, yielding an enriched small-molecule hit family. Compounds representing this family were synthesized without their DNA encoding moiety and found to inhibit the lymphocyte function-associated antigen 1/intercellular adhesion molecule-1 interaction with submicromolar potency in both ELISA and cell adhesion assays. Re-synthesized compounds conjugated to DNA or a fluorophore were demonstrated to bind to cells expressing the target protein. Copyright © 2014 Elsevier Ltd. All rights reserved.

  7. The Electron-Phonon Interaction in Strongly Correlated Systems

    International Nuclear Information System (INIS)

    Castellani, C.; Grilli, M.

    1995-01-01

    We analyze the effect of strong electron-electron repulsion on the electron-phonon interaction from a Fermi-liquid point of view and show that the electron-electron interaction is responsible for vertex corrections, which generically lead to a strong suppression of the electron-phonon coupling in the v F q/ω >>1 region, while such effect is not present when v F q/ω F is the Fermi velocity and q and ω are the transferred momentum and frequency respectively. In particular the e-ph scattering is suppressed in transport properties which are dominated by low-energy-high-momentum processes. On the other hand, analyzing the stability criterion for the compressibility, which involves the effective interactions in the dynamical limit, we show that a sizable electron-phonon interaction can push the system towards a phase-separation instability. Finally a detailed analysis of these ideas is carried out using a slave-boson approach for the infinite-U three-band Hubbard model in the presence of a coupling between the local hole density and a dispersionless optical phonon. (author)

  8. Joule-Thomson Coefficient for Strongly Interacting Unitary Fermi Gas

    International Nuclear Information System (INIS)

    Liao Kai; Chen Jisheng; Li Chao

    2010-01-01

    The Joule-Thomson effect reflects the interaction among constituent particles of macroscopic system. For classical ideal gas, the corresponding Joule-Thomson coefficient is vanishing while it is non-zero for ideal quantum gas due to the quantum degeneracy. In recent years, much attention is paid to the unitary Fermi gas with infinite two-body scattering length. According to universal analysis, the thermodynamical law of unitary Fermi gas is similar to that of non-interacting ideal gas, which can be explored by the virial theorem P = 2E/3V. Based on previous works, we further study the unitary Fermi gas properties. The effective chemical potential is introduced to characterize the nonlinear levels crossing effects in a strongly interacting medium. The changing behavior of the rescaled Joule-Thomson coefficient according to temperature manifests a quite different behavior from that for ideal Fermi gas. (general)

  9. Experimental reduction in interaction intensity strongly affects biotic selection.

    Science.gov (United States)

    Sletvold, Nina; Ågren, Jon

    2016-11-01

    The link between biotic interaction intensity and strength of selection is of fundamental interest for understanding biotically driven diversification and predicting the consequences of environmental change. The strength of selection resulting from biotic interactions is determined by the strength of the interaction and by the covariance between fitness and the trait under selection. When the relationship between trait and absolute fitness is constant, selection strength should be a direct function of mean population interaction intensity. To test this prediction, we excluded pollinators for intervals of different length to induce five levels of pollination intensity within a single plant population. Pollen limitation (PL) increased from 0 to 0.77 across treatments, accompanied by a fivefold increase in the opportunity for selection. Trait-fitness covariance declined with PL for number of flowers, but varied little for other traits. Pollinator-mediated selection on plant height, corolla size, and spur length increased by 91%, 34%, and 330%, respectively, in the most severely pollen-limited treatment compared to open-pollinated plants. The results indicate that realized biotic selection can be predicted from mean population interaction intensity when variation in trait-fitness covariance is limited, and that declines in pollination intensity will strongly increase selection on traits involved in the interaction. © 2016 by the Ecological Society of America.

  10. Nonperturbative Dynamics of Strong Interactions from Gauge/Gravity Duality

    Energy Technology Data Exchange (ETDEWEB)

    Grigoryan, Hovhannes [Louisiana State Univ., Baton Rouge, LA (United States)

    2008-08-01

    This thesis studies important dynamical observables of strong interactions such as form factors. It is known that Quantum Chromodynamics (QCD) is a theory which describes strong interactions. For large energies, one can apply perturbative techniques to solve some of the QCD problems. However, for low energies QCD enters into the nonperturbative regime, where di erent analytical or numerical tools have to be applied to solve problems of strong interactions. The holographic dual model of QCD is such an analytical tool that allows one to solve some nonperturbative QCD problems by translating them into a dual ve-dimensional theory de ned on some warped Anti de Sitter (AdS) background. Working within the framework of the holographic dual model of QCD, we develop a formalism to calculate form factors and wave functions of vector mesons and pions. As a result, we provide predictions of the electric radius, the magnetic and quadrupole moments which can be directly veri ed in lattice calculations or even experimentally. To nd the anomalous pion form factor, we propose an extension of the holographic model by including the Chern-Simons term required to reproduce the chiral anomaly of QCD. This allows us to nd the slope of the form factor with one real and one slightly o -shell photon which appeared to be close to the experimental ndings. We also analyze the limit of large virtualities (when the photon is far o -shell) and establish that predictions of the holographic model analytically coincide with those of perturbative QCD with asymptotic pion distribution amplitude. We also study the e ects of higher dimensional terms in the AdS/QCD model and show that these terms improve the holographic description towards a more realistic scenario. We show this by calculating corrections to the vector meson form factors and corrections to the observables such as electric radii, magnetic and quadrupole moments.

  11. The hadronic standard model for strong and electroweak interactions

    Energy Technology Data Exchange (ETDEWEB)

    Raczka, R. [Soltan Inst. for Nuclear Studies, Otwock-Swierk (Poland)

    1993-12-31

    We propose a new model for strong and electro-weak interactions. First, we review various QCD predictions for hadron-hadron and lepton-hadron processes. We indicate that the present formulation of strong interactions in the frame work of Quantum Chromodynamics encounters serious conceptual and numerical difficulties in a reliable description of hadron-hadron and lepton-hadron interactions. Next we propose to replace the strong sector of Standard Model based on unobserved quarks and gluons by the strong sector based on the set of the observed baryons and mesons determined by the spontaneously broken SU(6) gauge field theory model. We analyse various properties of this model such as asymptotic freedom, Reggeization of gauge bosons and fundamental fermions, baryon-baryon and meson-baryon high energy scattering, generation of {Lambda}-polarization in inclusive processes and others. Finally we extend this model by electro-weak sector. We demonstrate a remarkable lepton and hadron anomaly cancellation and we analyse a series of important lepton-hadron and hadron-hadron processes such as e{sup +} + e{sup -} {yields} hadrons, e{sup +} + e{sup -} {yields} W{sup +} + W{sup -}, e{sup +} + e{sup -} {yields} p + anti-p, e + p {yields} e + p and p + anti-p {yields} p + anti-p processes. We obtained a series of interesting new predictions in this model especially for processes with polarized particles. We estimated the value of the strong coupling constant {alpha}(M{sub z}) and we predicted the top baryon mass M{sub {Lambda}{sub t}} {approx_equal} 240 GeV. Since in our model the proton, neutron, {Lambda}-particles, vector mesons like {rho}, {omega}, {phi}, J/{psi} ect. and leptons are elementary most of experimentally analysed lepton-hadron and hadron-hadron processes in LEP1, LEP2, LEAR, HERA, HERMES, LHC and SSC experiments may be relatively easily analysed in our model. (author). 252 refs, 65 figs, 1 tab.

  12. The hadronic standard model for strong and electroweak interactions

    Energy Technology Data Exchange (ETDEWEB)

    Raczka, R [Soltan Inst. for Nuclear Studies, Otwock-Swierk (Poland)

    1994-12-31

    We propose a new model for strong and electro-weak interactions. First, we review various QCD predictions for hadron-hadron and lepton-hadron processes. We indicate that the present formulation of strong interactions in the frame work of Quantum Chromodynamics encounters serious conceptual and numerical difficulties in a reliable description of hadron-hadron and lepton-hadron interactions. Next we propose to replace the strong sector of Standard Model based on unobserved quarks and gluons by the strong sector based on the set of the observed baryons and mesons determined by the spontaneously broken SU(6) gauge field theory model. We analyse various properties of this model such as asymptotic freedom, Reggeization of gauge bosons and fundamental fermions, baryon-baryon and meson-baryon high energy scattering, generation of {Lambda}-polarization in inclusive processes and others. Finally we extend this model by electro-weak sector. We demonstrate a remarkable lepton and hadron anomaly cancellation and we analyse a series of important lepton-hadron and hadron-hadron processes such as e{sup +} + e{sup -} {yields} hadrons, e{sup +} + e{sup -} {yields} W{sup +} + W{sup -}, e{sup +} + e{sup -} {yields} p + anti-p, e + p {yields} e + p and p + anti-p {yields} p + anti-p processes. We obtained a series of interesting new predictions in this model especially for processes with polarized particles. We estimated the value of the strong coupling constant {alpha}(M{sub z}) and we predicted the top baryon mass M{sub {Lambda}{sub t}} {approx_equal} 240 GeV. Since in our model the proton, neutron, {Lambda}-particles, vector mesons like {rho}, {omega}, {phi}, J/{psi} ect. and leptons are elementary most of experimentally analysed lepton-hadron and hadron-hadron processes in LEP1, LEP2, LEAR, HERA, HERMES, LHC and SSC experiments may be relatively easily analysed in our model. (author). 252 refs, 65 figs, 1 tab.

  13. Results from ATLAS and CMS: Strong Interactions and New Physics

    CERN Document Server

    AUTHOR|(INSPIRE)INSPIRE-00179262

    2016-01-01

    Measurements on global properties and precision results on fundamental parameters related to the Strong Interaction sector of the Standard Model of particle physics, and searches for new phenomena beyond the Standard Model, performed by the two large multi-purpose particle detectors at the Large Hadron Collider (LHC), are summarised in this review. Special attention is payed to the new data obtained at $\\sqrt{s}$ = 13~TeV in 2015, which offer a first glimpse at the large physics potential offered by the high-energy running of the LHC.

  14. The kaon factory - towards the physics of strongly interacting systems

    International Nuclear Information System (INIS)

    Vogt, Erich

    1988-01-01

    With the advent of the standard model for quarks and leptons and unified forces there are profound new questions for the physics of strongly interacting systems: the nature of the nucleon, the physics of quark confinement, fundamental symmetries governing hadron decay and the effect of quarks and gluons on nuclear behaviour. Of the new large facilities now planned to respond to these questions the kaon factory is central. It uses very intense (∼100 μA) primary proton beams (∼30 GeV) to generate intense secondary beams of various hadrons and leptons. (author)

  15. Strongly modified plasmon-matter interaction with mesoscopic quantum emitters

    DEFF Research Database (Denmark)

    Andersen, Mads Lykke; Stobbe, Søren; Søndberg Sørensen, Anders

    2011-01-01

    Semiconductor quantum dots (QDs) provide useful means to couple light and matter in applications such as light-harvesting1, 2 and all-solid-state quantum information processing3, 4. This coupling can be increased by placing QDs in nanostructured optical environments such as photonic crystals...... or metallic nanostructures that enable strong confinement of light and thereby enhance the light–matter interaction. It has thus far been assumed that QDs can be described in the same way as atomic photon emitters—as point sources with wavefunctions whose spatial extent can be disregarded. Here we demonstrate...

  16. Discriminative deep inelastic tests of strong interaction field theories

    International Nuclear Information System (INIS)

    Glueck, M.; Reya, E.

    1979-02-01

    It is demonstrated that recent measurements of F 2 (x,Q 2 ) dx eliminate already all strong interaction field theories which do not include colored quarks as well as colored vector gluons. Detailed studies of scaling violations in F 2 (x,Q 2 ) cannot discriminate between a local gauge invariant theory (QCD) and one which has no local color gauge invariance, i.e. no triple-gluon coupling. This implies that all calculations on scaling violations done so far are insensitive to the gluon self-coupling, the latter might perhaps be delineated with future ep colliding beam facilities. (orig.) [de

  17. A strong viscous–inviscid interaction model for rotating airfoils

    DEFF Research Database (Denmark)

    Ramos García, Néstor; Sørensen, Jens Nørkær; Shen, Wen Zhong

    2014-01-01

    Two-dimensional (2D) and quasi-three dimensional (3D), steady and unsteady, viscous–inviscid interactive codes capable of predicting the aerodynamic behavior of wind turbine airfoils are presented. The model is based on a viscous–inviscid interaction technique using strong coupling between...... a boundary-layer trip or computed using an en envelope transition method. Validation of the incompressible 2D version of the code is carried out against measurements and other numerical codes for different airfoil geometries at various Reynolds numbers, ranging from 0.9 ⋅ 106 to 8.2 ⋅ 106. In the quasi-3D...... version, a parametric study on rotational effects induced by the Coriolis and centrifugal forces in the boundary-layer equations shows that the effects of rotation are to decrease the growth of the boundary-layer and delay the onset of separation, hence increasing the lift coefficient slightly while...

  18. Dynamical equilibration in strongly-interacting parton-hadron matter

    Directory of Open Access Journals (Sweden)

    Gorenstein M.

    2011-04-01

    Full Text Available We study the kinetic and chemical equilibration in 'infinite' parton-hadron matter within the Parton-Hadron-String Dynamics transport approach, which is based on a dynamical quasiparticle model for partons matched to reproduce lattice-QCD results – including the partonic equation of state – in thermodynamic equilibrium. The 'infinite' matter is simulated within a cubic box with periodic boundary conditions initialized at different baryon density (or chemical potential and energy density. The transition from initially pure partonic matter to hadronic degrees of freedom (or vice versa occurs dynamically by interactions. Different thermody-namical distributions of the strongly-interacting quark-gluon plasma (sQGP are addressed and discussed.

  19. On the mixed phase of strongly interacting matter

    International Nuclear Information System (INIS)

    Suleymanov, M.K.; Abdinov, O.B.; Belashev, B.Z.; Guseynaliyev, Y.G.; Vodoplanov, A.S.

    2005-01-01

    Full text : The studying of the behavior of some characteristics of hadron-nuclear and nuclear-nuclear interactions as a function of the collision centrality Q is an important experimental method to get information about the changes of nuclear matter phase, because the increasing of the centrality could lead to the growth of the nuclear matter baryon density. The regime change in the behavior of some centrality depending characteristics of events is expected by the varying the Q. It would be the signal about the phase transition. This method is considered as the best tool reaching the quark-gluon plasma phase of strongly interacting matter. Some experimental results demonstrate already the existence of the regime changes in the event characteristics behavior as a function of collision centrality

  20. Local condensate depletion at trap center under strong interactions

    Science.gov (United States)

    Yukalov, V. I.; Yukalova, E. P.

    2018-04-01

    Cold trapped Bose-condensed atoms, interacting via hard-sphere repulsive potentials are considered. Simple mean-field approximations show that the condensate distribution inside a harmonic trap always has the shape of a hump with the maximum condensate density occurring at the trap center. However, Monte Carlo simulations at high density and strong interactions display the condensate depletion at the trap center. The explanation of this effect of local condensate depletion at trap center is suggested in the frame of self-consistent theory of Bose-condensed systems. The depletion is shown to be due to the existence of the anomalous average that takes into account pair correlations and appears in systems with broken gauge symmetry.

  1. Universal structure of a strongly interacting Fermi gas

    Energy Technology Data Exchange (ETDEWEB)

    Kuhnle, Eva; Dyke, Paul; Hoinka, Sascha; Mark, Michael; Hu Hui; Liu Xiaji; Drummond, Peter; Hannaford, Peter; Vale, Chris, E-mail: cvale@swin.edu.au [ARC Centre of Excellence for Quantum Atom Optics, Swinburne University of Technology, Hawthorn 3122 (Australia)

    2011-01-10

    This paper presents studies of the universal properties of strongly interacting Fermi gases using Bragg spectroscopy. We focus on pair-correlations, their relationship to the contact C introduced by Tan, and their dependence on both the momentum and temperature. We show that short-range pair correlations obey a universal law, first derived by Tan through measurements of the static structure factor, which displays a universal scaling with the ratio of the contact to the momentum C/q. Bragg spectroscopy of ultracold {sup 6}Li atoms is employed to measure the structure factor for a wide range of momenta and interaction strengths, providing broad confirmation of this universal law. We show that calibrating our Bragg spectra using the f-sum rule leads to a dramatic improvement in the accuracy of the structure factor measurement. We also measure the temperature dependence of the contact in a unitary gas and compare our results to calculations based on a virial expansion.

  2. Theoretical Studies of Strongly Interacting Fine Particle Systems

    Science.gov (United States)

    Fearon, Michael

    Available from UMI in association with The British Library. A theoretical analysis of the time dependent behaviour of a system of fine magnetic particles as a function of applied field and temperature was carried out. The model used was based on a theory assuming Neel relaxation with a distribution of particle sizes. This theory predicted a linear variation of S_{max} with temperature and a finite intercept, which is not reflected by experimental observations. The remanence curves of strongly interacting fine-particle systems were also investigated theoretically. It was shown that the Henkel plot of the dc demagnetisation remanence vs the isothermal remanence is a useful representation of interactions. The form of the plot was found to be a reflection of the magnetic and physical microstructure of the material, which is consistent with experimental data. The relationship between the Henkel plot and the noise of a particulate recording medium, another property dependent on the microstructure, is also considered. The Interaction Field Factor (IFF), a single parameter characterising the non-linearity of the Henkel plot, is investigated. These results are consistent with a previous experimental study. Finally the results of the noise power spectral density for erased and saturated recording media are presented, so that characterisation of interparticle interactions may be carried out with greater accuracy.

  3. Non-equilibrium magnetic interactions in strongly correlated systems

    Energy Technology Data Exchange (ETDEWEB)

    Secchi, A., E-mail: a.secchi@science.ru.nl [Institute for Molecules and Materials, Radboud University Nijmegen, 6525 AJ Nijmegen (Netherlands); Brener, S.; Lichtenstein, A.I. [Institut für Theoretische Physik, Universitat Hamburg, Jungiusstraße 9, D-20355 Hamburg (Germany); Katsnelson, M.I. [Institute for Molecules and Materials, Radboud University Nijmegen, 6525 AJ Nijmegen (Netherlands)

    2013-06-15

    We formulate a low-energy theory for the magnetic interactions between electrons in the multi-band Hubbard model under non-equilibrium conditions determined by an external time-dependent electric field which simulates laser-induced spin dynamics. We derive expressions for dynamical exchange parameters in terms of non-equilibrium electronic Green functions and self-energies, which can be computed, e.g., with the methods of time-dependent dynamical mean-field theory. Moreover, we find that a correct description of the system requires, in addition to exchange, a new kind of magnetic interaction, that we name twist exchange, which formally resembles Dzyaloshinskii–Moriya coupling, but is not due to spin–orbit, and is actually due to an effective three-spin interaction. Our theory allows the evaluation of the related time-dependent parameters as well. -- Highlights: •We develop a theory for magnetism of strongly correlated systems out of equilibrium. •Our theory is suitable for laser-induced ultrafast magnetization dynamics. •We write time-dependent exchange parameters in terms of electronic Green functions. •We find a new magnetic interaction, a “twist exchange”. •We give general expressions for magnetic noise in itinerant-electron systems.

  4. Model of OSBP-Mediated Cholesterol Supply to Aichi Virus RNA Replication Sites Involving Protein-Protein Interactions among Viral Proteins, ACBD3, OSBP, VAP-A/B, and SAC1.

    Science.gov (United States)

    Ishikawa-Sasaki, Kumiko; Nagashima, Shigeo; Taniguchi, Koki; Sasaki, Jun

    2018-04-15

    Positive-strand RNA viruses, including picornaviruses, utilize cellular machinery for genome replication. Previously, we reported that each of the 2B, 2BC, 2C, 3A, and 3AB proteins of Aichi virus (AiV), a picornavirus, forms a complex with the Golgi apparatus protein ACBD3 and phosphatidylinositol 4-kinase IIIβ (PI4KB) at viral RNA replication sites (replication organelles [ROs]), enhancing PI4KB-dependent phosphatidylinositol 4-phosphate (PI4P) production. Here, we demonstrate AiV hijacking of the cellular cholesterol transport system involving oxysterol-binding protein (OSBP), a PI4P-binding cholesterol transfer protein. AiV RNA replication was inhibited by silencing cellular proteins known to be components of this pathway, OSBP, the ER membrane proteins VAPA and VAPB (VAP-A/B), the PI4P-phosphatase SAC1, and PI-transfer protein β. OSBP, VAP-A/B, and SAC1 were present at RNA replication sites. We also found various previously unknown interactions among the AiV proteins (2B, 2BC, 2C, 3A, and 3AB), ACBD3, OSBP, VAP-A/B, and SAC1, and the interactions were suggested to be involved in recruiting the component proteins to AiV ROs. Importantly, the OSBP-2B interaction enabled PI4P-independent recruitment of OSBP to AiV ROs, indicating preferential recruitment of OSBP among PI4P-binding proteins. Protein-protein interaction-based OSBP recruitment has not been reported for other picornaviruses. Cholesterol was accumulated at AiV ROs, and inhibition of OSBP-mediated cholesterol transfer impaired cholesterol accumulation and AiV RNA replication. Electron microscopy showed that AiV-induced vesicle-like structures were close to ER membranes. Altogether, we conclude that AiV directly recruits the cholesterol transport machinery through protein-protein interactions, resulting in formation of membrane contact sites between the ER and AiV ROs and cholesterol supply to the ROs. IMPORTANCE Positive-strand RNA viruses utilize host pathways to modulate the lipid composition of

  5. Effective interactions in strongly-coupled quantum systems

    International Nuclear Information System (INIS)

    Chen, J.M.C.

    1986-01-01

    In this thesis, they study the role of effective interactions in strongly-coupled Fermi systems where the short-range correlations introduce difficulties requiring special treatment. The correlated basis function method provides the means to incorporate the short-range correlations and generate the matrix elements of the Hamiltonian and identity operators in a nonorthogonal basis of states which are so important to their studies. In the first half of the thesis, the particle-hole channel is examined to elucidate the effects of collective excitations. Proceeding from a least-action principle, a generalization of the random-phase approximation is developed capable of describing such strongly-interacting Fermi systems as nuclei, nuclear matter, neutron-star matter, and liquid 3 He. A linear response of dynamically correlated system to a weak external perturbation is also derived based on the same framework. In the second half of the thesis, the particle-particle channel is examined to elucidate the effects of pairing in nuclear and neutron-star matter

  6. Strong interactions and electromagnetism in low-energy hadron physics

    International Nuclear Information System (INIS)

    Kubis, B.

    2002-10-01

    In the present work, we study various aspects of the entanglement of the strong and electromagnetic interactions as it is manifest in low-energy hadron physics. In the framework of chiral perturbation theory, two aspects are investigated: the test of the structure of baryons as probed by external electromagnetic currents, and the modification of reactions mediated by the strong interactions in the presence of internal (virtual) photons. In the first part of this work, we study the electromagnetic form factors of nucleons and the ground state baryon octet, as well as strangeness form factors of the nucleon. Emphasis is put on the comparison of a new relativistic scheme for the calculation of loop diagrams to the heavy-baryon formalism, and on the convergence of higher-order corrections in both schemes. The new scheme is shown to yield both a phenomenologically more successful description of the data and better convergence behaviour. In the second part, we study isospin violation in pion-kaon scattering as mediated by virtual photon effects and the light quark mass difference. This investigation is of particular importance for the extraction of scattering lengths from measurements of lifetime and energy levels in pion-kaon atoms. The isospin breaking corrections are shown to be small and sufficiently well under control. (orig.)

  7. Protein-Protein Docking in Drug Design and Discovery.

    Science.gov (United States)

    Kaczor, Agnieszka A; Bartuzi, Damian; Stępniewski, Tomasz Maciej; Matosiuk, Dariusz; Selent, Jana

    2018-01-01

    Protein-protein interactions (PPIs) are responsible for a number of key physiological processes in the living cells and underlie the pathomechanism of many diseases. Nowadays, along with the concept of so-called "hot spots" in protein-protein interactions, which are well-defined interface regions responsible for most of the binding energy, these interfaces can be targeted with modulators. In order to apply structure-based design techniques to design PPIs modulators, a three-dimensional structure of protein complex has to be available. In this context in silico approaches, in particular protein-protein docking, are a valuable complement to experimental methods for elucidating 3D structure of protein complexes. Protein-protein docking is easy to use and does not require significant computer resources and time (in contrast to molecular dynamics) and it results in 3D structure of a protein complex (in contrast to sequence-based methods of predicting binding interfaces). However, protein-protein docking cannot address all the aspects of protein dynamics, in particular the global conformational changes during protein complex formation. In spite of this fact, protein-protein docking is widely used to model complexes of water-soluble proteins and less commonly to predict structures of transmembrane protein assemblies, including dimers and oligomers of G protein-coupled receptors (GPCRs). In this chapter we review the principles of protein-protein docking, available algorithms and software and discuss the recent examples, benefits, and drawbacks of protein-protein docking application to water-soluble proteins, membrane anchoring and transmembrane proteins, including GPCRs.

  8. Relativistic strings and dual models of strong interactions

    International Nuclear Information System (INIS)

    Marinov, M.S.

    1977-01-01

    The theory of strong interactions,based on the model depicting a hardon as a one-dimentional elastic relativistic system(''string'') is considered. The relationship between this model and the concepts of quarks and partons is discussed. Presented are the principal results relating to the Veneziano dual theory, which may be considered as the consequence of the string model, and to its modifications. The classical string theory is described in detail. Attention is focused on questions of importance to the construction of the quantum theory - the Hamilton mechanisms and conformal symmetry. Quantization is described, and it is shown that it is not contradictory only in the 26-dimentional space and with a special requirement imposed on the spectrum of states. The theory of a string with a distributed spin is considered. The spin is introduced with the aid of the Grassman algebra formalism. In this case quantization is possible only in the 10-dimentional space. The strings interact by their ruptures and gluings. A method for calculating the interaction amplitudes is indicated

  9. Noise in strong laser-atom interactions: Phase telegraph noise

    International Nuclear Information System (INIS)

    Eberly, J.H.; Wodkiewicz, K.; Shore, B.W.

    1984-01-01

    We discuss strong laser-atom interactions that are subjected to jump-type (random telegraph) random-phase noise. Physically, the jumps may arise from laser fluctuations, from collisions of various kinds, or from other external forces. Our discussion is carried out in two stages. First, direct and partially heuristic calculations determine the laser spectrum and also give a third-order differential equation for the average inversion of a two-level atom on resonance. At this stage a number of general features of the interaction are able to be studied easily. The optical analog of motional narrowing, for example, is clearly predicted. Second, we show that the theory of generalized Poisson processes allows laser-atom interactions in the presence of random telegraph noise of all kinds (not only phase noise) to be treated systematically, by means of a master equation first used in the context of quantum optics by Burshtein. We use the Burshtein equation to obtain an exact expression for the two-level atom's steady-state resonance fluorescence spectrum, when the exciting laser exhibits phase telegraph noise. Some comparisons are made with results obtained from other noise models. Detailed treatments of the effects ofmly jumps, or as a model of finite laser bandwidth effects, in which the laser frequency exhibits random jumps. We show that these two types of frequency noise can be distinguished in light-scattering spectra. We also discuss examples which demonstrate both temporal and spectral motional narrowing, nonexponential correlations, and non-Lorentzian spectra. Its exact solubility in finite terms makes the frequency-telegraph noise model an attractive alternative to the white-noise Ornstein-Uhlenbeck frequency noise model which has been previously applied to laser-atom interactions

  10. Extreme states of matter in strong interaction physics an introduction

    CERN Document Server

    Satz, Helmut

    2018-01-01

    This book is a course-tested primer on the thermodynamics of strongly interacting matter – a profound and challenging area of both theoretical and experimental modern physics. Analytical and numerical studies of statistical quantum chromodynamics provide the main theoretical tool, while in experiments, high-energy nuclear collisions are the key for extensive laboratory investigations. As such, the field straddles statistical, particle and nuclear physics, both conceptually and in the methods of investigation used. The book addresses, above all, the many young scientists starting their scientific research in this field, providing them with a general, self-contained introduction that highlights the basic concepts and ideas and explains why we do what we do. Much of the book focuses on equilibrium thermodynamics: first it presents simplified phenomenological pictures, leading to critical behavior in hadronic matter and to a quark-hadron phase transition. This is followed by elements of finite temperature latti...

  11. Stability of Dirac Liquids with Strong Coulomb Interaction.

    Science.gov (United States)

    Tupitsyn, Igor S; Prokof'ev, Nikolay V

    2017-01-13

    We develop and apply the diagrammatic Monte Carlo technique to address the problem of the stability of the Dirac liquid state (in a graphene-type system) against the strong long-range part of the Coulomb interaction. So far, all attempts to deal with this problem in the field-theoretical framework were limited either to perturbative or random phase approximation and functional renormalization group treatments, with diametrically opposite conclusions. Our calculations aim at the approximation-free solution with controlled accuracy by computing vertex corrections from higher-order skeleton diagrams and establishing the renormalization group flow of the effective Coulomb coupling constant. We unambiguously show that with increasing the system size L (up to ln(L)∼40), the coupling constant always flows towards zero; i.e., the two-dimensional Dirac liquid is an asymptotically free T=0 state with divergent Fermi velocity.

  12. Screening important inputs in models with strong interaction properties

    International Nuclear Information System (INIS)

    Saltelli, Andrea; Campolongo, Francesca; Cariboni, Jessica

    2009-01-01

    We introduce a new method for screening inputs in mathematical or computational models with large numbers of inputs. The method proposed here represents an improvement over the best available practice for this setting when dealing with models having strong interaction effects. When the sample size is sufficiently high the same design can also be used to obtain accurate quantitative estimates of the variance-based sensitivity measures: the same simulations can be used to obtain estimates of the variance-based measures according to the Sobol' and the Jansen formulas. Results demonstrate that Sobol' is more efficient for the computation of the first-order indices, while Jansen performs better for the computation of the total indices.

  13. Screening important inputs in models with strong interaction properties

    Energy Technology Data Exchange (ETDEWEB)

    Saltelli, Andrea [European Commission, Joint Research Centre, 21020 Ispra, Varese (Italy); Campolongo, Francesca [European Commission, Joint Research Centre, 21020 Ispra, Varese (Italy)], E-mail: francesca.campolongo@jrc.it; Cariboni, Jessica [European Commission, Joint Research Centre, 21020 Ispra, Varese (Italy)

    2009-07-15

    We introduce a new method for screening inputs in mathematical or computational models with large numbers of inputs. The method proposed here represents an improvement over the best available practice for this setting when dealing with models having strong interaction effects. When the sample size is sufficiently high the same design can also be used to obtain accurate quantitative estimates of the variance-based sensitivity measures: the same simulations can be used to obtain estimates of the variance-based measures according to the Sobol' and the Jansen formulas. Results demonstrate that Sobol' is more efficient for the computation of the first-order indices, while Jansen performs better for the computation of the total indices.

  14. Towards a unified gauge theory of gravitational and strong interactions

    International Nuclear Information System (INIS)

    Hehl, F.W.; Sijacki, D.

    1980-01-01

    The space-time properties of leptons and hadrons is studied and it is found necessary to extend general relativity to the gauge theory based on the four-dimensional affine group. This group translates and deforms the tetrads of the locally Minkowskian space-time. Its conserved currents, momentum, and hypermomentum, act as sources in the two field equations of gravity. A Lagrangian quadratic in torsion and curvature allows for the propagation of two independent gauge fields: translational e-gravity mediated by the tetrad coefficients, and deformational GAMMA-gravity mediated by the connection coefficients. For macroscopic matter e-gravity coincides with general relativity up to the post-Newtonian approximation of fourth order. For microscopic matter GAMMA-gravity represents a strong Yang-Mills type interaction. In the linear approximation, for a static source, a confinement potential is found. (author)

  15. Are Higgs particles strongly interacting(question mark)

    International Nuclear Information System (INIS)

    Shanker, O.

    1982-02-01

    The order of magnitude of Yukawa couplings in some theories with flavour violating Higgs particles is estimated. Based on these couplings, mass bounds for flavour violating Higgs particles are derived from the Ksub(L)-Ksub(S) mass difference. The Higgs particles have to be very heavy, implying that the Higgs sector quartic couplings are very large. Thus, these theories seem to require a strongly interacting Higgs sector unless one adjusts to the Higgs-fermion Yukawa couplings to within two orders of magnitude, so as to suppress the coupling of Higgs particles to the flavour-violating anti sd current. Most models with flavour violating Higgs particles have the same general features, so the conclusions are likely to hold for a wide class of models with flavour violating Higgs particles

  16. Ion Motion in a Plasma Interacting with Strong Magnetic Fields

    International Nuclear Information System (INIS)

    Weingarten, A.; Grabowski, C.; Chakrabarti, N.; Maron, Y.; Fruchtmant, A.

    1999-01-01

    The interaction of a plasma with strong magnetic fields takes place in many laboratory experiments and astrophysical plasmas. Applying a strong magnetic field to the plasma may result in plasma displacement, magnetization, or the formation of instabilities. Important phenomena in plasma, such as the energy transport and the momentum balance, take a different form in each case. We study this interaction in a plasma that carries a short-duration (80-ns) current pulse, generating a magnetic field of up to 17 kG. The evolution of the magnetic field, plasma density, ion velocities, and electric fields are determined before and during the current pulse. The dependence of the plasma limiting current on the plasma density and composition are studied and compared to theoretical models based on the different phenomena. When the plasma collisionality is low, three typical velocities should be taken into consideration: the proton and heavier-ion Alfven velocities (v A p and v A h , respectively) and the EMHD magnetic-field penetration velocity into the plasma (v EMHD ). If both Alfven velocities are larger than v EMHD the plasma is pushed ahead of the magnetic piston and the magnetic field energy is dissipated into ion kinetic energy. If v EMHD is the largest of three velocities, the plasma become magnetized and the ions acquire a small axial momentum only. Different ion species may drift in different directions along the current lines. In this case, the magnetic field energy is probably dissipated into electron thermal energy. When vs > V EMHD > vi, as in the case of one of our experiments, ion mass separation occurs. The protons are pushed ahead of the piston while the heavier-ions become magnetized. Since the plasma electrons are unmagnetized they cannot cross the piston, and the heavy ions are probably charge-neutralized by electrons originating from the cathode that are 'born' magnetized

  17. Kaonic atoms – studies of the strong interaction with strangeness

    Directory of Open Access Journals (Sweden)

    Marton J.

    2014-01-01

    Full Text Available The strong interaction of charged antikaons (K− with nucleons and nuclei in the low-energy regime is a fascinating topic. The antikaon plays a peculiar role in hadron physics due to the strong attraction antikaon-nucleon which is a key question for possible kaonic nuclear bound states. A rather direct experimental access to the antikaon-nucleon scattering lengths is provided by precision X-ray spectroscopy of transitions to low-lying states in light kaonic atoms like kaonic hydrogen and deuterium. After the successful completion of precision measurements on kaonic hydrogen and helium isotopes by SIDDHARTA at DAΦNE/LNF, new X-ray studies with the focus on kaonic deuterium are in preparation (SIDDHARTA2. In the future with kaonic deuterium data the antikaon-nucleon isospin-dependent scattering lengths can be extracted for the first time. An overview of the experimental results of SIDDHARTA and an outlook to future perspectives in the SIDDHARTA2 experiments in this frontier research field will be given.

  18. Chemical Evolution of Strongly Interacting Quark-Gluon Plasma

    International Nuclear Information System (INIS)

    Pan, Ying-Hua; Zhang, Wei-Ning

    2014-01-01

    At very initial stage of relativistic heavy ion collisions a wave of quark-gluon matter is produced from the break-up of the strong color electric field and then thermalizes at a short time scale (~1 fm/c). However, the quark-gluon plasma (QGP) system is far out of chemical equilibrium, especially for the heavy quarks which are supposed to reach chemical equilibrium much late. In this paper a continuing quark production picture for strongly interacting QGP system is derived, using the quark number susceptibilities and the equation of state; both of them are from the results calculated by the Wuppertal-Budapest lattice QCD collaboration. We find that the densities of light quarks increase by 75% from the temperature T=400 MeV to T=150 MeV, while the density of strange quark annihilates by 18% in the temperature region. We also offer a discussion on how this late production of quarks affects the final charge-charge correlations

  19. Universal contact of strongly interacting fermions at finite temperatures

    Energy Technology Data Exchange (ETDEWEB)

    Hu Hui; Liu Xiaji; Drummond, Peter D, E-mail: hhu@swin.edu.au, E-mail: xiajiliu@swin.edu.au, E-mail: pdrummond@swin.edu.au [ARC Centre of Excellence for Quantum-Atom Optics, Centre for Atom Optics and Ultrafast Spectroscopy, Swinburne University of Technology, Melbourne 3122 (Australia)

    2011-03-15

    The recently discovered universal thermodynamic behavior of dilute, strongly interacting Fermi gases also implies a universal structure in the many-body pair-correlation function at short distances, as quantified by the contact I. Here, we theoretically calculate the temperature dependence of this universal contact for a Fermi gas in free space and in a harmonic trap. At high temperatures above the Fermi degeneracy temperature, T{approx}>T{sub F}, we obtain a reliable non-perturbative quantum virial expansion up to third order. At low temperatures, we compare different approximate strong-coupling theories. These make different predictions, which need to be tested either by future experiments or by advanced quantum Monte Carlo simulations. We conjecture that in the universal unitarity limit, the contact or correlation decreases monotonically with increasing temperature, unless the temperature is significantly lower than the critical temperature, T<

  20. Air-sea interactions during strong winter extratropical storms

    Science.gov (United States)

    Nelson, Jill; He, Ruoying; Warner, John C.; Bane, John

    2014-01-01

    A high-resolution, regional coupled atmosphere–ocean model is used to investigate strong air–sea interactions during a rapidly developing extratropical cyclone (ETC) off the east coast of the USA. In this two-way coupled system, surface momentum and heat fluxes derived from the Weather Research and Forecasting model and sea surface temperature (SST) from the Regional Ocean Modeling System are exchanged via the Model Coupling Toolkit. Comparisons are made between the modeled and observed wind velocity, sea level pressure, 10 m air temperature, and sea surface temperature time series, as well as a comparison between the model and one glider transect. Vertical profiles of modeled air temperature and winds in the marine atmospheric boundary layer and temperature variations in the upper ocean during a 3-day storm period are examined at various cross-shelf transects along the eastern seaboard. It is found that the air–sea interactions near the Gulf Stream are important for generating and sustaining the ETC. In particular, locally enhanced winds over a warm sea (relative to the land temperature) induce large surface heat fluxes which cool the upper ocean by up to 2 °C, mainly during the cold air outbreak period after the storm passage. Detailed heat budget analyses show the ocean-to-atmosphere heat flux dominates the upper ocean heat content variations. Results clearly show that dynamic air–sea interactions affecting momentum and buoyancy flux exchanges in ETCs need to be resolved accurately in a coupled atmosphere–ocean modeling framework.

  1. Protein-protein interactions as a proxy to monitor conformational changes and activation states of the tomato resistance protein I-2

    NARCIS (Netherlands)

    Lukasik-Shreepaathy, E.; Vossen, J.H.; Tameling, W.I.L.; de Vroomen, M.J.; Cornelissen, B.J.C.; Takken, F.L.W.

    2012-01-01

    Plant resistance proteins (R) are involved in pathogen recognition and subsequent initiation of defence responses. Their activity is regulated by inter- and intramolecular interactions. In a yeast two-hybrid screen two clones (I2I-1 and I2I-2) specifically interacting with I-2, a Fusarium oxysporum

  2. The Citrus transcription factor, CitERF13, regulates citric acid accumulation via a protein-protein interaction with the vacuolar proton pump, CitVHA-c4.

    Science.gov (United States)

    Li, Shao-jia; Yin, Xue-ren; Xie, Xiu-lan; Allan, Andrew C; Ge, Hang; Shen, Shu-ling; Chen, Kun-song

    2016-02-03

    Organic acids are essential to fruit flavor. The vacuolar H(+) transporting adenosine triphosphatase (V-ATPase) plays an important role in organic acid transport and accumulation. However, less is known of V-ATPase interacting proteins and their relationship with organic acid accumulation. The relationship between V-ATPase and citric acid was investigated, using the citrus tangerine varieties 'Ordinary Ponkan (OPK)' and an early maturing mutant 'Zaoshu Ponkan (ZPK)'. Five V-ATPase genes (CitVHA) were predicted as important to citric acid accumulation. Among the genes, CitVHA-c4 was observed, using a yeast two-hybrid screen, to interact at the protein level with an ethylene response factor, CitERF13. This was verified using bimolecular fluorescence complementation assays. A similar interaction was also observed between Arabidopsis AtERF017 (a CitERF13 homolog) and AtVHA-c4 (a CitVHA-c4 homolog). A synergistic effect on citric acid levels was observed between V-ATPase proteins and interacting ERFs when analyzed using transient over-expression in tobacco and Arabidopsis mutants. Furthermore, the transcript abundance of CitERF13 was concomitant with CitVHA-c4. CitERF13 or AtERF017 over-expression leads to significant citric acid accumulation. This accumulation was abolished in an AtVHA-c4 mutant background. ERF-VHA interactions appear to be involved in citric acid accumulation, which was observed in both citrus and Arabidopsis.

  3. Theoretical & Experimental Research in Weak, Electromagnetic & Strong Interactions

    Energy Technology Data Exchange (ETDEWEB)

    Nandi, Satyanarayan [Oklahoma State Univ., Stillwater, OK (United States); Babu, Kaladi [Oklahoma State Univ., Stillwater, OK (United States); Rizatdinova, Flera [Oklahoma State Univ., Stillwater, OK (United States); Khanov, Alexander [Oklahoma State Univ., Stillwater, OK (United States); Haley, Joseph [Oklahoma State Univ., Stillwater, OK (United States)

    2015-09-17

    The conducted research spans a wide range of topics in the theoretical, experimental and phenomenological aspects of elementary particle interactions. Theory projects involve topics in both the energy frontier and the intensity frontier. The experimental research involves energy frontier with the ATLAS Collaboration at the Large Hadron Collider (LHC). In theoretical research, novel ideas going beyond the Standard Model with strong theoretical motivations were proposed, and their experimental tests at the LHC and forthcoming neutrino facilities were outlined. These efforts fall into the following broad categories: (i) TeV scale new physics models for LHC Run 2, including left-right symmetry and trinification symmetry, (ii) unification of elementary particles and forces, including the unification of gauge and Yukawa interactions, (iii) supersummetry and mechanisms of supersymmetry breaking, (iv) superworld without supersymmetry, (v) general models of extra dimensions, (vi) comparing signals of extra dimensions with those of supersymmetry, (vii) models with mirror quarks and mirror leptons at the TeV scale, (viii) models with singlet quarks and singlet Higgs and their implications for Higgs physics at the LHC, (ix) new models for the dark matter of the universe, (x) lepton flavor violation in Higgs decays, (xi) leptogenesis in radiative models of neutrino masses, (xii) light mediator models of non-standard neutrino interactions, (xiii) anomalous muon decay and short baseline neutrino anomalies, (xiv) baryogenesis linked to nucleon decay, and (xv) a new model for recently observed diboson resonance at the LHC and its other phenomenological implications. The experimental High Energy Physics group has been, and continues to be, a successful and productive contributor to the ATLAS experiment at the LHC. Members of the group performed search for gluinos decaying to stop and top quarks, new heavy gauge bosons decaying to top and bottom quarks, and vector-like quarks

  4. SOcK, MiSTs, MASK and STicKs: the GCKIII (germinal centre kinase III) kinases and their heterologous protein-protein interactions.

    Science.gov (United States)

    Sugden, Peter H; McGuffin, Liam J; Clerk, Angela

    2013-08-15

    The GCKIII (germinal centre kinase III) subfamily of the mammalian Ste20 (sterile 20)-like group of serine/threonine protein kinases comprises SOK1 (Ste20-like/oxidant-stress-response kinase 1), MST3 (mammalian Ste20-like kinase 3) and MST4. Initially, GCKIIIs were considered in the contexts of the regulation of mitogen-activated protein kinase cascades and apoptosis. More recently, their participation in multiprotein heterocomplexes has become apparent. In the present review, we discuss the structure and phosphorylation of GCKIIIs and then focus on their interactions with other proteins. GCKIIIs possess a highly-conserved, structured catalytic domain at the N-terminus and a less-well conserved C-terminal regulatory domain. GCKIIIs are activated by tonic autophosphorylation of a T-loop threonine residue and their phosphorylation is regulated primarily through protein serine/threonine phosphatases [especially PP2A (protein phosphatase 2A)]. The GCKIII regulatory domains are highly disorganized, but can interact with more structured proteins, particularly the CCM3 (cerebral cavernous malformation 3)/PDCD10 (programmed cell death 10) protein. We explore the role(s) of GCKIIIs (and CCM3/PDCD10) in STRIPAK (striatin-interacting phosphatase and kinase) complexes and their association with the cis-Golgi protein GOLGA2 (golgin A2; GM130). Recently, an interaction of GCKIIIs with MO25 has been identified. This exhibits similarities to the STRADα (STE20-related kinase adaptor α)-MO25 interaction (as in the LKB1-STRADα-MO25 heterotrimer) and, at least for MST3, the interaction may be enhanced by cis-autophosphorylation of its regulatory domain. In these various heterocomplexes, GCKIIIs associate with the Golgi apparatus, the centrosome and the nucleus, as well as with focal adhesions and cell junctions, and are probably involved in cell migration, polarity and proliferation. Finally, we consider the association of GCKIIIs with a number of human diseases, particularly

  5. Expression Patterns and Identified Protein-Protein Interactions Suggest That Cassava CBL-CIPK Signal Networks Function in Responses to Abiotic Stresses.

    Science.gov (United States)

    Mo, Chunyan; Wan, Shumin; Xia, Youquan; Ren, Ning; Zhou, Yang; Jiang, Xingyu

    2018-01-01

    Cassava is an energy crop that is tolerant of multiple abiotic stresses. It has been reported that the interaction between Calcineurin B-like (CBL) protein and CBL-interacting protein kinase (CIPK) is implicated in plant development and responses to various stresses. However, little is known about their functions in cassava. Herein, 8 CBL ( MeCBL ) and 26 CIPK ( MeCIPK ) genes were isolated from cassava by genome searching and cloning of cDNA sequences of Arabidopsis CBL s and CIPK s. Reverse-transcriptase polymerase chain reaction (RT-PCR) analysis showed that the expression levels of MeCBL and MeCIPK genes were different in different tissues throughout the life cycle. The expression patterns of 7 CBL and 26 CIPK genes in response to NaCl, PEG, heat and cold stresses were analyzed by quantitative real-time PCR (qRT-PCR), and it was found that the expression of each was induced by multiple stimuli. Furthermore, we found that many pairs of CBLs and CIPKs could interact with each other via investigating the interactions between 8 CBL and 25 CIPK proteins using a yeast two-hybrid system. Yeast cells co-transformed with cassava MeCIPK24, MeCBL10 , and Na + /H + antiporter MeSOS1 genes exhibited higher salt tolerance compared to those with one or two genes. These results suggest that the cassava CBL-CIPK signal network might play key roles in response to abiotic stresses.

  6. Comparing side chain packing in soluble proteins, protein-protein interfaces, and transmembrane proteins.

    Science.gov (United States)

    Gaines, J C; Acebes, S; Virrueta, A; Butler, M; Regan, L; O'Hern, C S

    2018-05-01

    We compare side chain prediction and packing of core and non-core regions of soluble proteins, protein-protein interfaces, and transmembrane proteins. We first identified or created comparable databases of high-resolution crystal structures of these 3 protein classes. We show that the solvent-inaccessible cores of the 3 classes of proteins are equally densely packed. As a result, the side chains of core residues at protein-protein interfaces and in the membrane-exposed regions of transmembrane proteins can be predicted by the hard-sphere plus stereochemical constraint model with the same high prediction accuracies (>90%) as core residues in soluble proteins. We also find that for all 3 classes of proteins, as one moves away from the solvent-inaccessible core, the packing fraction decreases as the solvent accessibility increases. However, the side chain predictability remains high (80% within 30°) up to a relative solvent accessibility, rSASA≲0.3, for all 3 protein classes. Our results show that ≈40% of the interface regions in protein complexes are "core", that is, densely packed with side chain conformations that can be accurately predicted using the hard-sphere model. We propose packing fraction as a metric that can be used to distinguish real protein-protein interactions from designed, non-binding, decoys. Our results also show that cores of membrane proteins are the same as cores of soluble proteins. Thus, the computational methods we are developing for the analysis of the effect of hydrophobic core mutations in soluble proteins will be equally applicable to analyses of mutations in membrane proteins. © 2018 Wiley Periodicals, Inc.

  7. Functional structural motifs for protein-ligand, protein-protein, and protein-nucleic acid interactions and their connection to supersecondary structures.

    Science.gov (United States)

    Kinjo, Akira R; Nakamura, Haruki

    2013-01-01

    Protein functions are mediated by interactions between proteins and other molecules. One useful approach to analyze protein functions is to compare and classify the structures of interaction interfaces of proteins. Here, we describe the procedures for compiling a database of interface structures and efficiently comparing the interface structures. To do so requires a good understanding of the data structures of the Protein Data Bank (PDB). Therefore, we also provide a detailed account of the PDB exchange dictionary necessary for extracting data that are relevant for analyzing interaction interfaces and secondary structures. We identify recurring structural motifs by classifying similar interface structures, and we define a coarse-grained representation of supersecondary structures (SSS) which represents a sequence of two or three secondary structure elements including their relative orientations as a string of four to seven letters. By examining the correspondence between structural motifs and SSS strings, we show that no SSS string has particularly high propensity to be found interaction interfaces in general, indicating any SSS can be used as a binding interface. When individual structural motifs are examined, there are some SSS strings that have high propensity for particular groups of structural motifs. In addition, it is shown that while the SSS strings found in particular structural motifs for nonpolymer and protein interfaces are as abundant as in other structural motifs that belong to the same subunit, structural motifs for nucleic acid interfaces exhibit somewhat stronger preference for SSS strings. In regard to protein folds, many motif-specific SSS strings were found across many folds, suggesting that SSS may be a useful description to investigate the universality of ligand binding modes.

  8. Expression Patterns and Identified Protein-Protein Interactions Suggest That Cassava CBL-CIPK Signal Networks Function in Responses to Abiotic Stresses

    Directory of Open Access Journals (Sweden)

    Chunyan Mo

    2018-03-01

    Full Text Available Cassava is an energy crop that is tolerant of multiple abiotic stresses. It has been reported that the interaction between Calcineurin B-like (CBL protein and CBL-interacting protein kinase (CIPK is implicated in plant development and responses to various stresses. However, little is known about their functions in cassava. Herein, 8 CBL (MeCBL and 26 CIPK (MeCIPK genes were isolated from cassava by genome searching and cloning of cDNA sequences of Arabidopsis CBLs and CIPKs. Reverse-transcriptase polymerase chain reaction (RT-PCR analysis showed that the expression levels of MeCBL and MeCIPK genes were different in different tissues throughout the life cycle. The expression patterns of 7 CBL and 26 CIPK genes in response to NaCl, PEG, heat and cold stresses were analyzed by quantitative real-time PCR (qRT-PCR, and it was found that the expression of each was induced by multiple stimuli. Furthermore, we found that many pairs of CBLs and CIPKs could interact with each other via investigating the interactions between 8 CBL and 25 CIPK proteins using a yeast two-hybrid system. Yeast cells co-transformed with cassava MeCIPK24, MeCBL10, and Na+/H+ antiporter MeSOS1 genes exhibited higher salt tolerance compared to those with one or two genes. These results suggest that the cassava CBL-CIPK signal network might play key roles in response to abiotic stresses.

  9. Effective Field Theories and Strong Interactions. Final Technical Report

    International Nuclear Information System (INIS)

    Fleming, Sean

    2011-01-01

    The framework of Effective Field Theories (EFTs) allows us to describe strong interactions in terms of degrees of freedom relevant to the energy regimes of interest, in the most general way consistent with the symmetries of QCD. Observables are expanded systematically in powers of M lo /M hi , where M lo (M hi ) denotes a low-(high-)energy scale. This organizational principle is referred to as 'power counting'. Terms of increasing powers in the expansion parameter are referred to as leading order (LO), next-to-leading order (NLO), etc. Details of the QCD dynamics not included explicitly are encoded in interaction parameters, or 'low-energy constants' (LECs), which can in principle be calculated from an explicit solution of QCD - for example via lattice simulations- but can also be determined directly from experimental data. QCD has an intrinsic scale M QCD ≅ 1 GeV, at which the QCD coupling constant α s (M QCD ) becomes large and the dynamics becomes non-perturbative. As a consequence M QCD sets the scale for the masses of most hadrons, such as the nucleon mass m N ≅ 940 MeV. EFTs can roughly be divided into two categories: those that can be matched onto QCD in perturbation theory, which we call high-energy EFTs, and those that cannot be matched perturbatively, which we call low-energy EFTs. In high-energy EFTs, M QCD typically sets the low-energy scale, and all the dynamics associated with this scale reside in matrix elements of EFT operators. These non-perturbative matrix elements are the LECs and are also referred to as long-distance contributions. Each matrix element is multiplied by a short-distance coefficient, which contains the dynamics from the high scale M hi . Since M hi >> M QCD , α s (M hi ) hi ∼ M Q , the heavy-quark mass, and in addition to M QCD there are low scales associated with the typical relative momentum ∼ M Q v and energy ∼ M Q v 2 of the heavy quarks. Depending on the sizes of M Q and the heavy-quark velocity v these scales can

  10. Construction of high-quality Caco-2 three-frame cDNA library and its application to yeast two-hybrid for the human astrovirus protein-protein interaction.

    Science.gov (United States)

    Zhao, Wei; Li, Xin; Liu, Wen-Hui; Zhao, Jian; Jin, Yi-Ming; Sui, Ting-Ting

    2014-09-01

    Human epithelial colorectal adenocarcinoma (Caco-2) cells are widely used as an in vitro model of the human small intestinal mucosa. Caco-2 cells are host cells of the human astrovirus (HAstV) and other enteroviruses. High quality cDNA libraries are pertinent resources and critical tools for protein-protein interaction research, but are currently unavailable for Caco-2 cells. To construct a three-open reading frame, full length-expression cDNA library from the Caco-2 cell line for application to HAstV protein-protein interaction screening, total RNA was extracted from Caco-2 cells. The switching mechanism at the 5' end of the RNA transcript technique was used for cDNA synthesis. Double-stranded cDNA was digested by Sfi I and ligated to reconstruct a pGADT7-Sfi I three-frame vector. The ligation mixture was transformed into Escherichia coli HST08 premium electro cells by electroporation to construct the primary cDNA library. The library capacity was 1.0×10(6)clones. Gel electrophoresis results indicated that the fragments ranged from 0.5kb to 4.2kb. Randomly picked clones show that the recombination rate was 100%. The three-frame primary cDNA library plasmid mixture (5×10(5)cfu) was also transformed into E. coli HST08 premium electro cells, and all clones were harvested to amplify the cDNA library. To detect the sufficiency of the cDNA library, HAstV capsid protein as bait was screened and tested against the Caco-2 cDNA library by a yeast two-hybrid (Y2H) system. A total of 20 proteins were found to interact with the capsid protein. These results showed that a high-quality three-frame cDNA library from Caco-2 cells was successfully constructed. This library was efficient for the application to the Y2H system, and could be used for future research. Copyright © 2014 Elsevier B.V. All rights reserved.

  11. Phases of strongly-interacting matter with functional methods

    International Nuclear Information System (INIS)

    Mitter, M.

    2012-01-01

    Non-perturbative aspects of strongly-interacting matter, in particular at non-vanishing temperatures, are investigated with functional methods. The consequences of confinement in terms of a linearly rising static quark potential arising from an infrared singular quark 4-point function are studied. Such a singularity is only consistent for a specific color structure and implies the existence of similar singularities in special color structures of n-point functions with n>3. A simple explanation for Casimir scaling is found within this mechanism of confinement.The deconfinement transition of fundamentally charged scalar and quark matter is investigated in terms of center symmetry. Novel dual order parameters are introduced that can be obtained from the corresponding matter propagators. In the case of quark matter the new order parameter compares well with the dual chiral condensate, with the advantage that no regularization is necessary even at non-vanishing quark masses.The influence of the axial anomaly on the chiral transition is studied in terms of a 't Hooft determinant with quarks and mesons as effective degrees of freedom in the functional renormalization group. In the case of two quark flavors, the calculated temperature dependent determinant results in a decrease of the anomalous eta'-mass close to the chiral transition temperature. This is connected to a partial Z(2) restoration at the chiral transition instead of the restoration of full axial U(1). With 2+1 quark flavors and a temperature independent 't Hooft term, the chiral transition is found to be of second order with three dimensional O(4) critical exponents in the limit of vanishing up and down quark mass, whereas a first-order transition is seen without U(1) violation. (author) [de

  12. Analysis of the protein-protein interactions between the human acidic ribosomal P-proteins: evaluation by the two hybrid system

    DEFF Research Database (Denmark)

    Tchórzewski, M; Boldyreff, B; Issinger, O

    2000-01-01

    The surface acidic ribosomal proteins (P-proteins), together with ribosomal core protein P0 form a multimeric lateral protuberance on the 60 S ribosomal subunit. This structure, also called stalk, is important for efficient translational activity of the ribosome. In order to shed more light...... forms the 60 S ribosomal stalk: P0-(P1/P2)(2). Additionally, mutual interactions among human and yeast P-proteins were analyzed. Heterodimer formation could be observed between human P2 and yeast P1 proteins....

  13. An automated wide-field time-gated optically sectioning fluorescence lifetime imaging multiwell plate reader for high-content analysis of protein-protein interactions

    Science.gov (United States)

    Alibhai, Dominic; Kumar, Sunil; Kelly, Douglas; Warren, Sean; Alexandrov, Yuriy; Munro, Ian; McGinty, James; Talbot, Clifford; Murray, Edward J.; Stuhmeier, Frank; Neil, Mark A. A.; Dunsby, Chris; French, Paul M. W.

    2011-03-01

    We describe an optically-sectioned FLIM multiwell plate reader that combines Nipkow microscopy with wide-field time-gated FLIM, and its application to high content analysis of FRET. The system acquires sectioned FLIM images in fluorescent protein. It has been applied to study the formation of immature HIV virus like particles (VLPs) in live cells by monitoring Gag-Gag protein interactions using FLIM FRET of HIV-1 Gag transfected with CFP or YFP. VLP formation results in FRET between closely packed Gag proteins, as confirmed by our FLIM analysis that includes automatic image segmentation.

  14. Molecular screening of compounds to the predicted Protein-Protein Interaction site of Rb1-E7 with p53- E6 in HPV

    Science.gov (United States)

    Shaikh, Faraz; Sanehi, Parvish; Rawal, Rakesh

    2012-01-01

    Cervical cancer is malignant neoplasm of the cervix uteri or cervical area. Human Papillomaviruses (HPVs) which are heterogeneous groups of small double stranded DNA viruses are considered as the primary cause of cervical cancer, involved in 90% of all Cervical Cancers. Two early HPV genes, E6 and E7, are known to play crucial role in tumor formation. E6 binds with p53 and prevents its translocation and thereby inhibit the ability of p53 to activate or repress target genes. E7 binds to hypophosphorylated Rb and thereby induces cells to enter into premature S-phase by disrupting Rb-E2F complexes. The strategy of the research work was to target the site of interaction of Rb1 -E7 & p53-E6. A total of 88 compounds were selected for molecular screening, based on comprehensive literature survey for natural compounds with anti-cancer activity. Molecular docking analysis was carried out with Molegro Virtual Docker, to screen the 88 chosen compounds and rank them according to their binding affinity towards the site of interaction of the viral oncoproteins and human tumor suppressor proteins. The docking result revealed that Nicandrenone a member of Withanolides family of chemical compounds as the most likely molecule that can be used as a candidate drug against HPV induced cervical cancer. Abbreviations HPV - Human Papiloma Virus, HTSP - Human Tumor Suppressor Proteins, VOP - Viral oncoproteins. PMID:22829740

  15. Characterizing alpha helical properties of Ebola viral proteins as potential targets for inhibition of alpha-helix mediated protein-protein interactions [v3; ref status: indexed, http://f1000r.es/50u

    Directory of Open Access Journals (Sweden)

    Sandeep Chakraborty

    2015-01-01

    Full Text Available Ebola, considered till recently as a rare and endemic disease, has dramatically transformed into a potentially global humanitarian crisis. The genome of Ebola, a member of the Filoviridae family, encodes seven proteins. Based on the recently implemented software (PAGAL for analyzing the hydrophobicity and amphipathicity properties of alpha helices (AH in proteins, we characterize the helices in the Ebola proteome. We demonstrate that AHs with characteristically unique features are involved in critical interactions with the host proteins. For example, the Ebola virus membrane fusion subunit, GP2, from the envelope glycoprotein ectodomain has an AH with a large hydrophobic moment. The neutralizing antibody (KZ52 derived from a human survivor of the 1995 Kikwit outbreak recognizes a protein epitope on this AH, emphasizing the critical nature of this secondary structure in the virulence of the Ebola virus. Our method ensures a comprehensive list of such `hotspots'. These helices probably are or can be the target of molecules designed to inhibit AH mediated protein-protein interactions. Further, by comparing the AHs in proteins of the related Marburg viruses, we are able to elicit subtle changes in the proteins that might render them ineffective to previously successful drugs. Such differences are difficult to identify by a simple sequence or structural alignment. Thus, analyzing AHs in the small Ebola proteome can aid rational design aimed at countering the `largest Ebola epidemic, affecting multiple countries in West Africa' (http://www.cdc.gov/vhf/ebola/outbreaks/2014-west-africa/index.html.

  16. The colours of strong interaction; L`interaction forte sous toutes ses couleurs

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1996-12-31

    The aim of this session is to draw a consistent framework about the different ways to consider strong interaction. A large part is dedicated to theoretical work and the latest experimental results obtained at the first electron collider HERA are discussed. (A.C.)

  17. The colours of strong interaction; L`interaction forte sous toutes ses couleurs

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1995-12-31

    The aim of this session is to draw a consistent framework about the different ways to consider strong interaction. A large part is dedicated to theoretical work and the latest experimental results obtained at the first electron collider HERA are discussed. (A.C.)

  18. A positive-strand RNA virus uses alternative protein-protein interactions within a viral protease/cofactor complex to switch between RNA replication and virion morphogenesis.

    Science.gov (United States)

    Dubrau, Danilo; Tortorici, M Alejandra; Rey, Félix A; Tautz, Norbert

    2017-02-01

    The viruses of the family Flaviviridae possess a positive-strand RNA genome and express a single polyprotein which is processed into functional proteins. Initially, the nonstructural (NS) proteins, which are not part of the virions, form complexes capable of genome replication. Later on, the NS proteins also play a critical role in virion formation. The molecular basis to understand how the same proteins form different complexes required in both processes is so far unknown. For pestiviruses, uncleaved NS2-3 is essential for virion morphogenesis while NS3 is required for RNA replication but is not functional in viral assembly. Recently, we identified two gain of function mutations, located in the C-terminal region of NS2 and in the serine protease domain of NS3 (NS3 residue 132), which allow NS2 and NS3 to substitute for uncleaved NS2-3 in particle assembly. We report here the crystal structure of pestivirus NS3-4A showing that the NS3 residue 132 maps to a surface patch interacting with the C-terminal region of NS4A (NS4A-kink region) suggesting a critical role of this contact in virion morphogenesis. We show that destabilization of this interaction, either by alanine exchanges at this NS3/4A-kink interface, led to a gain of function of the NS3/4A complex in particle formation. In contrast, RNA replication and thus replicase assembly requires a stable association between NS3 and the NS4A-kink region. Thus, we propose that two variants of NS3/4A complexes exist in pestivirus infected cells each representing a basic building block required for either RNA replication or virion morphogenesis. This could be further corroborated by trans-complementation studies with a replication-defective NS3/4A double mutant that was still functional in viral assembly. Our observations illustrate the presence of alternative overlapping surfaces providing different contacts between the same proteins, allowing the switch from RNA replication to virion formation.

  19. A positive-strand RNA virus uses alternative protein-protein interactions within a viral protease/cofactor complex to switch between RNA replication and virion morphogenesis

    Science.gov (United States)

    Rey, Félix A.

    2017-01-01

    The viruses of the family Flaviviridae possess a positive-strand RNA genome and express a single polyprotein which is processed into functional proteins. Initially, the nonstructural (NS) proteins, which are not part of the virions, form complexes capable of genome replication. Later on, the NS proteins also play a critical role in virion formation. The molecular basis to understand how the same proteins form different complexes required in both processes is so far unknown. For pestiviruses, uncleaved NS2-3 is essential for virion morphogenesis while NS3 is required for RNA replication but is not functional in viral assembly. Recently, we identified two gain of function mutations, located in the C-terminal region of NS2 and in the serine protease domain of NS3 (NS3 residue 132), which allow NS2 and NS3 to substitute for uncleaved NS2-3 in particle assembly. We report here the crystal structure of pestivirus NS3-4A showing that the NS3 residue 132 maps to a surface patch interacting with the C-terminal region of NS4A (NS4A-kink region) suggesting a critical role of this contact in virion morphogenesis. We show that destabilization of this interaction, either by alanine exchanges at this NS3/4A-kink interface, led to a gain of function of the NS3/4A complex in particle formation. In contrast, RNA replication and thus replicase assembly requires a stable association between NS3 and the NS4A-kink region. Thus, we propose that two variants of NS3/4A complexes exist in pestivirus infected cells each representing a basic building block required for either RNA replication or virion morphogenesis. This could be further corroborated by trans-complementation studies with a replication-defective NS3/4A double mutant that was still functional in viral assembly. Our observations illustrate the presence of alternative overlapping surfaces providing different contacts between the same proteins, allowing the switch from RNA replication to virion formation. PMID:28151973

  20. A positive-strand RNA virus uses alternative protein-protein interactions within a viral protease/cofactor complex to switch between RNA replication and virion morphogenesis.

    Directory of Open Access Journals (Sweden)

    Danilo Dubrau

    2017-02-01

    Full Text Available The viruses of the family Flaviviridae possess a positive-strand RNA genome and express a single polyprotein which is processed into functional proteins. Initially, the nonstructural (NS proteins, which are not part of the virions, form complexes capable of genome replication. Later on, the NS proteins also play a critical role in virion formation. The molecular basis to understand how the same proteins form different complexes required in both processes is so far unknown. For pestiviruses, uncleaved NS2-3 is essential for virion morphogenesis while NS3 is required for RNA replication but is not functional in viral assembly. Recently, we identified two gain of function mutations, located in the C-terminal region of NS2 and in the serine protease domain of NS3 (NS3 residue 132, which allow NS2 and NS3 to substitute for uncleaved NS2-3 in particle assembly. We report here the crystal structure of pestivirus NS3-4A showing that the NS3 residue 132 maps to a surface patch interacting with the C-terminal region of NS4A (NS4A-kink region suggesting a critical role of this contact in virion morphogenesis. We show that destabilization of this interaction, either by alanine exchanges at this NS3/4A-kink interface, led to a gain of function of the NS3/4A complex in particle formation. In contrast, RNA replication and thus replicase assembly requires a stable association between NS3 and the NS4A-kink region. Thus, we propose that two variants of NS3/4A complexes exist in pestivirus infected cells each representing a basic building block required for either RNA replication or virion morphogenesis. This could be further corroborated by trans-complementation studies with a replication-defective NS3/4A double mutant that was still functional in viral assembly. Our observations illustrate the presence of alternative overlapping surfaces providing different contacts between the same proteins, allowing the switch from RNA replication to virion formation.

  1. Reticulomics: Protein-Protein Interaction Studies with Two Plasmodesmata-Localized Reticulon Family Proteins Identify Binding Partners Enriched at Plasmodesmata, Endoplasmic Reticulum, and the Plasma Membrane.

    Science.gov (United States)

    Kriechbaumer, Verena; Botchway, Stanley W; Slade, Susan E; Knox, Kirsten; Frigerio, Lorenzo; Oparka, Karl; Hawes, Chris

    2015-11-01

    The endoplasmic reticulum (ER) is a ubiquitous organelle that plays roles in secretory protein production, folding, quality control, and lipid biosynthesis. The cortical ER in plants is pleomorphic and structured as a tubular network capable of morphing into flat cisternae, mainly at three-way junctions, and back to tubules. Plant reticulon family proteins (RTNLB) tubulate the ER by dimerization and oligomerization, creating localized ER membrane tensions that result in membrane curvature. Some RTNLB ER-shaping proteins are present in the plasmodesmata (PD) proteome and may contribute to the formation of the desmotubule, the axial ER-derived structure that traverses primary PD. Here, we investigate the binding partners of two PD-resident reticulon proteins, RTNLB3 and RTNLB6, that are located in primary PD at cytokinesis in tobacco (Nicotiana tabacum). Coimmunoprecipitation of green fluorescent protein-tagged RTNLB3 and RTNLB6 followed by mass spectrometry detected a high percentage of known PD-localized proteins as well as plasma membrane proteins with putative membrane-anchoring roles. Förster resonance energy transfer by fluorescence lifetime imaging microscopy assays revealed a highly significant interaction of the detected PD proteins with the bait RTNLB proteins. Our data suggest that RTNLB proteins, in addition to a role in ER modeling, may play important roles in linking the cortical ER to the plasma membrane. © 2015 American Society of Plant Biologists. All Rights Reserved.

  2. Elicitin-induced distal systemic resistance in plants is mediated through the protein-protein interactions influenced by selected lysine residues

    Directory of Open Access Journals (Sweden)

    Hana eUhlíková

    2016-02-01

    Full Text Available Elicitins are a family of small proteins with sterol-binding activity that are secreted by Phytophthora and Pythium spp. classified as oomycete PAMPs. Although alfa- and beta-elicitins bind with the same affinity to one high affinity binding site on the plasma membrane, beta-elicitins (possessing 6-7 lysine residues are generally 50- to 100-fold more active at inducing distal HR and systemic resistance than the alfa-isoforms (with only 1-3 lysine residues.To examine the role of lysine residues in elicitin biological activity, we employed site-directed mutagenesis to prepare a series of beta-elicitin cryptogein variants with mutations on specific lysine residues. In contrast to direct infiltration of protein into leaves, application to the stem revealed a rough correlation between protein’s charge and biological activity, resulting in protection against Phytophthora parasitica. A detailed analysis of proteins’ movement in plants showed no substantial differences in distribution through phloem indicating differences in consequent apoplastic or symplastic transport. In this process, an important role of homodimer formation together with the ability to form a heterodimer with potential partner represented by endogenous plants LTPs is suggested. Our work demonstrates a key role of selected lysine residues in these interactions and stresses the importance of processes preceding elicitin recognition responsible for induction of distal systemic resistance.

  3. Elucidating the weak protein-protein interaction mechanisms behind the liquid-liquid phase separation of a mAb solution by different types of additives.

    Science.gov (United States)

    Wu, Guoliang; Wang, Shujing; Tian, Zhou; Zhang, Ning; Sheng, Han; Dai, Weiguo; Qian, Feng

    2017-11-01

    Liquid-liquid phase separation (LLPS) has long been observed during the physical stability investigation of therapeutic protein formulations. The buffer conditions and the presence of various excipients are thought to play important roles in the formulation development of monoclonal antibodies (mAbs). In this study, the effects of several small-molecule excipients (histidine, alanine, glycine, sodium phosphate, sodium chloride, sorbitol and sucrose) with diverse physical-chemical properties on LLPS of a model IgG1 (JM2) solutions were investigated by multiple techniques, including UV-vis spectroscopy, circular dichroism, differential scanning calorimetry/fluorimetry, size exclusion chromatography and dynamic light scattering. The LLPS of JM2 was confirmed to be a thermodynamic equilibrium process with no structural changes or irreversible aggregation of proteins. Phase diagrams of various JM2 formulations were constructed, suggesting that the phase behavior of JM2 was dependent on the solution pH, ionic strength and the presence of other excipients such as glycine, alanine, sorbitol and sucrose. Furthermore, we demonstrated that for this mAb, the interaction parameter (k D ) determined at low protein concentration appeared to be a good predictor for the occurrence of LLPS at high concentration. Copyright © 2017 Elsevier B.V. All rights reserved.

  4. Uses of phage display in agriculture: a review of food-related protein-protein interactions discovered by biopanning over diverse baits.

    Science.gov (United States)

    Kushwaha, Rekha; Payne, Christina M; Downie, A Bruce

    2013-01-01

    This review highlights discoveries made using phage display that impact the use of agricultural products. The contribution phage display made to our fundamental understanding of how various protective molecules serve to safeguard plants and seeds from herbivores and microbes is discussed. The utility of phage display for directed evolution of enzymes with enhanced capacities to degrade the complex polymers of the cell wall into molecules useful for biofuel production is surveyed. Food allergies are often directed against components of seeds; this review emphasizes how phage display has been employed to determine the seed component(s) contributing most to the allergenic reaction and how it has played a central role in novel approaches to mitigate patient response. Finally, an overview of the use of phage display in identifying the mature seed proteome protection and repair mechanisms is provided. The identification of specific classes of proteins preferentially bound by such protection and repair proteins leads to hypotheses concerning the importance of safeguarding the translational apparatus from damage during seed quiescence and environmental perturbations during germination. These examples, it is hoped, will spur the use of phage display in future plant science examining protein-ligand interactions.

  5. Uses of Phage Display in Agriculture: A Review of Food-Related Protein-Protein Interactions Discovered by Biopanning over Diverse Baits

    Directory of Open Access Journals (Sweden)

    Rekha Kushwaha

    2013-01-01

    Full Text Available This review highlights discoveries made using phage display that impact the use of agricultural products. The contribution phage display made to our fundamental understanding of how various protective molecules serve to safeguard plants and seeds from herbivores and microbes is discussed. The utility of phage display for directed evolution of enzymes with enhanced capacities to degrade the complex polymers of the cell wall into molecules useful for biofuel production is surveyed. Food allergies are often directed against components of seeds; this review emphasizes how phage display has been employed to determine the seed component(s contributing most to the allergenic reaction and how it has played a central role in novel approaches to mitigate patient response. Finally, an overview of the use of phage display in identifying the mature seed proteome protection and repair mechanisms is provided. The identification of specific classes of proteins preferentially bound by such protection and repair proteins leads to hypotheses concerning the importance of safeguarding the translational apparatus from damage during seed quiescence and environmental perturbations during germination. These examples, it is hoped, will spur the use of phage display in future plant science examining protein-ligand interactions.

  6. Gene Prioritization by Integrated Analysis of Protein Structural and Network Topological Properties for the Protein-Protein Interaction Network of Neurological Disorders

    Directory of Open Access Journals (Sweden)

    Yashna Paul

    2016-01-01

    Full Text Available Neurological disorders are known to show similar phenotypic manifestations like anxiety, depression, and cognitive impairment. There is a need to identify shared genetic markers and molecular pathways in these diseases, which lead to such comorbid conditions. Our study aims to prioritize novel genetic markers that might increase the susceptibility of patients affected with one neurological disorder to other diseases with similar manifestations. Identification of pathways involving common candidate markers will help in the development of improved diagnosis and treatments strategies for patients affected with neurological disorders. This systems biology study for the first time integratively uses 3D-structural protein interface descriptors and network topological properties that characterize proteins in a neurological protein interaction network, to aid the identification of genes that are previously not known to be shared between these diseases. Results of protein prioritization by machine learning have identified known as well as new genetic markers which might have direct or indirect involvement in several neurological disorders. Important gene hubs have also been identified that provide an evidence for shared molecular pathways in the neurological disease network.

  7. Properties of hot and dense strongly interacting matter

    Energy Technology Data Exchange (ETDEWEB)

    Almasi, Gabor Andras

    2017-06-19

    In this thesis we consider effective models of quantum chromodynamics to learn about the chiral- and deconfinement phase transitions. In Chapter 1 we review basic properties of strongly interacting matter and the foundations of finite temperature field theory. We review furthermore the nonperturbative functional renormalization group (FRG) approach. In Chapter 2 we introduce the quark-meson (QM) model and its extensions including the Polyakov-loop variables and repulsive vector interactions between quarks. We then discuss features of the model both in the mean-field approximation and in the renormalization group treatment. A novel method to solve the renormalization group equations based on the Chebyshev polynomials is presented at the end of the chapter. In Chapter 3 the scaling behavior of the order parameter at the chiral phase transition is studied within effective models. We explore universal and nonuniversal structures near the critical point. These include the scaling functions, the leading corrections to scaling and the corresponding size of the scaling window as well as their dependence on an external symmetry breaking field. We consider two models in the mean-field approximation, the QM and the Polyakov-loop-extended quark-meson (PQM) models, and compare their critical properties with a purely bosonic theory, the O(N) linear sigma model in the N → ∞ limit. In these models the order parameter scaling function is found analytically using the high temperature expansion of the thermodynamic potential. The effects of a gluonic background on the nonuniversal scaling parameters are studied within the PQM model. Furthermore, numerical calculations of the scaling function and the scaling window are performed in the QM model using the FRG. Chapter 4 contains a study of the critical properties of net-baryon-number fluctuations at the chiral restoration transition in a medium at finite temperature and net baryon density. The chiral dynamics of quantum

  8. Two Methods For Simulating the Strong-Strong Beam-Beam Interaction in Hadron Colliders

    International Nuclear Information System (INIS)

    Warnock, Robert L.

    2002-01-01

    We present and compare the method of weighted macro particle tracking and the Perron-Frobenius operator technique for simulating the time evolution of two beams coupled via the collective beam-beam interaction in 2-D and 4-D (transverse) phase space. The coherent dipole modes, with and without lattice nonlinearities and external excitation, are studied by means of the Vlasov-Poisson system

  9. Viscosity of high concentration protein formulations of monoclonal antibodies of the IgG1 and IgG4 subclass - Prediction of viscosity through protein-protein interaction measurements

    DEFF Research Database (Denmark)

    Neergaard, Martin S; Kalonia, Devendra S; Parshad, Henrik

    2013-01-01

    The purpose of this work was to explore the relation between protein-protein interactions (PPIs) and solution viscosity at high protein concentration using three monoclonal antibodies (mAbs), two of the IgG4 subclass and one of the IgG1 subclass. A range of methods was used to quantify the PPI...... low or high protein concentration determined using DLS. The PPI measurements were correlated with solution viscosity (measured by DLS using polystyrene nanospheres and ultrasonic shear rheology) as a function of pH (4-9) and ionic strength (10, 50 and 150mM). Our measurements showed that the highest...... solution viscosity was observed under conditions with the most negative kD, the highest apparent radius and the lowest net charge. An increase in ionic strength resulted in a change in the nature of the PPI at low pH from repulsive to attractive. In the neutral to alkaline pH region the mAbs behaved...

  10. Structural model of the hUbA1-UbcH10 quaternary complex: in silico and experimental analysis of the protein-protein interactions between E1, E2 and ubiquitin.

    Directory of Open Access Journals (Sweden)

    Stefania Correale

    Full Text Available UbcH10 is a component of the Ubiquitin Conjugation Enzymes (Ubc; E2 involved in the ubiquitination cascade controlling the cell cycle progression, whereby ubiquitin, activated by E1, is transferred through E2 to the target protein with the involvement of E3 enzymes. In this work we propose the first three dimensional model of the tetrameric complex formed by the human UbA1 (E1, two ubiquitin molecules and UbcH10 (E2, leading to the transthiolation reaction. The 3D model was built up by using an experimentally guided incremental docking strategy that combined homology