Dynamical analysis of yeast protein interaction network during the sake brewing process.

Science.gov (United States)

Mirzarezaee, Mitra; Sadeghi, Mehdi; Araabi, Babak N

2011-12-01

Proteins interact with each other for performing essential functions of an organism. They change partners to get involved in various processes at different times or locations. Studying variations of protein interactions within a specific process would help better understand the dynamic features of the protein interactions and their functions. We studied the protein interaction network of Saccharomyces cerevisiae (yeast) during the brewing of Japanese sake. In this process, yeast cells are exposed to several stresses. Analysis of protein interaction networks of yeast during this process helps to understand how protein interactions of yeast change during the sake brewing process. We used gene expression profiles of yeast cells for this purpose. Results of our experiments revealed some characteristics and behaviors of yeast hubs and non-hubs and their dynamical changes during the brewing process. We found that just a small portion of the proteins (12.8 to 21.6%) is responsible for the functional changes of the proteins in the sake brewing process. The changes in the number of edges and hubs of the yeast protein interaction networks increase in the first stages of the process and it then decreases at the final stages.

Investigation of Fanconi anemia protein interactions by yeast two-hybrid analysis.

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Huber, P A; Medhurst, A L; Youssoufian, H; Mathew, C G

2000-02-05

Fanconi anemia is a chromosomal breakage disorder with eight complementation groups (A-H), and three genes (FANCA, FANCC, and FANCG) have been identified. Initial investigations of the interaction between FANCA and FANCC, principally by co-immunoprecipitation, have proved controversial. We used the yeast two-hybrid assay to test for interactions of the FANCA, FANCC, and FANCG proteins. No activation of the reporter gene was observed in yeast co-expressing FANCA and FANCC as hybrid proteins, suggesting that FANCA does not directly interact with FANCC. However, a high level of activation was found when FANCA was co-expressed with FANCG, indicating strong, direct interaction between these proteins. Both FANCA and FANCG show weak but consistent interaction with themselves, suggesting that their function may involve dimerisation. The site of interaction of FANCG with FANCA was investigated by analysis of 12 mutant fragments of FANCG. Although both N- and C-terminal fragments did interact, binding to FANCA was drastically reduced, suggesting that more than one region of the FANCG protein is required for proper interaction with FANCA. Copyright 2000 Academic Press.

Atom depth analysis delineates mechanisms of protein intermolecular interactions

International Nuclear Information System (INIS)

Alocci, Davide; Bernini, Andrea; Niccolai, Neri

2013-01-01

Highlights: •3D atom depth analysis is proposed to identify different layers in protein structures. •Amino acid contents for each layers have been analyzed for a large protein dataset. •Charged amino acids in the most external layer are present at very different extents. •Atom depth indexes of K residues reflect their side chains flexibility. •Mobile surface charges can be responsible for long range protein–protein recognition. -- Abstract: The systematic analysis of amino acid distribution, performed inside a large set of resolved protein structures, sheds light on possible mechanisms driving non random protein–protein approaches. Protein Data Bank entries have been selected using as filters a series of restrictions ensuring that the shape of protein surface is not modified by interactions with large or small ligands. 3D atom depth has been evaluated for all the atoms of the 2,410 selected structures. The amino acid relative population in each of the structural layers formed by grouping atoms on the basis of their calculated depths, has been evaluated. We have identified seven structural layers, the inner ones reproducing the core of proteins and the outer one incorporating their most protruding moieties. Quantitative analysis of amino acid contents of structural layers identified, as expected, different behaviors. Atoms of Q, R, K, N, D residues are increasingly more abundant in going from core to surfaces. An opposite trend is observed for V, I, L, A, C, and G. An intermediate behavior is exhibited by P, S, T, M, W, H, F and Y. The outer structural layer hosts predominantly E and K residues whose charged moieties, protruding from outer regions of the protein surface, reorient free from steric hindrances, determining specific electrodynamics maps. This feature may represent a protein signature for long distance effects, driving the formation of encounter complexes and the eventual short distance approaches that are required for protein–protein

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...

Topological and functional analysis of nonalcoholic steatohepatitis through protein interaction mapping

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Asadzadeh-Aghdaee, Hamid; Mansouri, Vahid; Peyvandi, Ali Asghar; Moztarzadeh, Fathollah; Okhovatian, Farshad; Lahmi, Farhad; Vafaee, Reza; Zali, Mohammad Reza

2016-01-01

Aim: The corresponding proteins are important for network mapping since the interaction analysis can provide a new interpretation about disease underlying mechanisms as the aim of this study. Backgroud: Nonalcoholic steatohepatitis (NASH) is one of the main causes of liver disease in the world. It has been known with many susceptible proteins that play essential role in its pathogenesis. Methods: In this paper, protein-protein interaction (PPI) network analysis of fatty liver disease retrieved from STRING db by the application of Cytoscape Software. ClueGO analyzed the associated pathways for the selected top proteins. Results: INS, PPARA, LEP, SREBF1, and ALB are the introduced biomarker panel for fatty liver disease. Conclusion: It seems that pathways related to insulin have a prominent role in fatty liver disease. Therefore, investigation in this case is required to confirm the possible linkage of introduced panel and involvement of insulin pathway in the disease. PMID:28224024

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

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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.

An attempt to understand glioma stem cell biology through centrality analysis of a protein interaction network.

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Mallik, Mrinmay Kumar

2018-02-07

Biological networks can be analyzed using "Centrality Analysis" to identify the more influential nodes and interactions in the network. This study was undertaken to create and visualize a biological network comprising of protein-protein interactions (PPIs) amongst proteins which are preferentially over-expressed in glioma cancer stem cell component (GCSC) of glioblastomas as compared to the glioma non-stem cancer cell (GNSC) component and then to analyze this network through centrality analyses (CA) in order to identify the essential proteins in this network and their interactions. In addition, this study proposes a new centrality analysis method pertaining exclusively to transcription factors (TFs) and interactions amongst them. Moreover the relevant molecular functions, biological processes and biochemical pathways amongst these proteins were sought through enrichment analysis. A protein interaction network was created using a list of proteins which have been shown to be preferentially expressed or over-expressed in GCSCs isolated from glioblastomas as compared to the GNSCs. This list comprising of 38 proteins, created using manual literature mining, was submitted to the Reactome FIViz tool, a web based application integrated into Cytoscape, an open source software platform for visualizing and analyzing molecular interaction networks and biological pathways to produce the network. This network was subjected to centrality analyses utilizing ranked lists of six centrality measures using the FIViz application and (for the first time) a dedicated centrality analysis plug-in ; CytoNCA. The interactions exclusively amongst the transcription factors were nalyzed through a newly proposed centrality analysis method called "Gene Expression Associated Degree Centrality Analysis (GEADCA)". Enrichment analysis was performed using the "network function analysis" tool on Reactome. The CA was able to identify a small set of proteins with consistently high centrality ranks that

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

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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.

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

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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.

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

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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.

PSAIA – Protein Structure and Interaction Analyzer

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Vlahoviček Kristian

2008-04-01

Full Text Available Abstract Background PSAIA (Protein Structure and Interaction Analyzer was developed to compute geometric parameters for large sets of protein structures in order to predict and investigate protein-protein interaction sites. Results In addition to most relevant established algorithms, PSAIA offers a new method PIADA (Protein Interaction Atom Distance Algorithm for the determination of residue interaction pairs. We found that PIADA produced more satisfactory results than comparable algorithms implemented in PSAIA. Particular advantages of PSAIA include its capacity to combine different methods to detect the locations and types of interactions between residues and its ability, without any further automation steps, to handle large numbers of protein structures and complexes. Generally, the integration of a variety of methods enables PSAIA to offer easier automation of analysis and greater reliability of results. PSAIA can be used either via a graphical user interface or from the command-line. Results are generated in either tabular or XML format. Conclusion In a straightforward fashion and for large sets of protein structures, PSAIA enables the calculation of protein geometric parameters and the determination of location and type for protein-protein interaction sites. XML formatted output enables easy conversion of results to various formats suitable for statistic analysis. Results from smaller data sets demonstrated the influence of geometry on protein interaction sites. Comprehensive analysis of properties of large data sets lead to new information useful in the prediction of protein-protein interaction sites.

Intuitive Density Functional Theory-Based Energy Decomposition Analysis for Protein-Ligand Interactions.

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Phipps, M J S; Fox, T; Tautermann, C S; Skylaris, C-K

2017-04-11

First-principles quantum mechanical calculations with methods such as density functional theory (DFT) allow the accurate calculation of interaction energies between molecules. These interaction energies can be dissected into chemically relevant components such as electrostatics, polarization, and charge transfer using energy decomposition analysis (EDA) approaches. Typically EDA has been used to study interactions between small molecules; however, it has great potential to be applied to large biomolecular assemblies such as protein-protein and protein-ligand interactions. We present an application of EDA calculations to the study of ligands that bind to the thrombin protein, using the ONETEP program for linear-scaling DFT calculations. Our approach goes beyond simply providing the components of the interaction energy; we are also able to provide visual representations of the changes in density that happen as a result of polarization and charge transfer, thus pinpointing the functional groups between the ligand and protein that participate in each kind of interaction. We also demonstrate with this approach that we can focus on studying parts (fragments) of ligands. The method is relatively insensitive to the protocol that is used to prepare the structures, and the results obtained are therefore robust. This is an application to a real protein drug target of a whole new capability where accurate DFT calculations can produce both energetic and visual descriptors of interactions. These descriptors can be used to provide insights for tailoring interactions, as needed for example in drug design.

Information assessment on predicting protein-protein interactions

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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

Quantitative analysis of the interaction between the envelope protein domains and the core protein of human hepatitis B virus

International Nuclear Information System (INIS)

Choi, Kyoung-Jae; Lim, Chun-Woo; Yoon, Moon-Young; Ahn, Byung-Yoon; Yu, Yeon Gyu

2004-01-01

Interaction between preformed nucleocapsids and viral envelope proteins is critical for the assembly of virus particles in infected cells. The pre-S1 and pre-S2 and cytosolic regions of the human hepatitis B virus envelope protein had been implicated in the interaction with the core protein of nucleocapsids. The binding affinities of specific subdomains of the envelope protein to the core protein were quantitatively measured by both ELISA and BIAcore assay. While a marginal binding was detected with the pre-S1 or pre-S2, the core protein showed high affinities to pre-S with apparent dissociation constants (K D app ) of 7.3 ± 0.9 and 8.2 ± 0.4 μM by ELISA and BIAcore assay, respectively. The circular dichroism analysis suggested that conformational change occurs in pre-S through interaction with core protein. These results substantiate the importance of specific envelope domains in virion assembly, and demonstrate that the interaction between viral proteins can be quantitatively measured in vitro

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

Structural and functional analysis of VQ motif-containing proteins in Arabidopsis as interacting proteins of WRKY transcription factors.

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Cheng, Yuan; Zhou, Yuan; Yang, Yan; Chi, Ying-Jun; Zhou, Jie; Chen, Jian-Ye; Wang, Fei; Fan, Baofang; Shi, Kai; Zhou, Yan-Hong; Yu, Jing-Quan; Chen, Zhixiang

2012-06-01

WRKY transcription factors are encoded by a large gene superfamily with a broad range of roles in plants. Recently, several groups have reported that proteins containing a short VQ (FxxxVQxLTG) motif interact with WRKY proteins. We have recently discovered that two VQ proteins from Arabidopsis (Arabidopsis thaliana), SIGMA FACTOR-INTERACTING PROTEIN1 and SIGMA FACTOR-INTERACTING PROTEIN2, act as coactivators of WRKY33 in plant defense by specifically recognizing the C-terminal WRKY domain and stimulating the DNA-binding activity of WRKY33. In this study, we have analyzed the entire family of 34 structurally divergent VQ proteins from Arabidopsis. Yeast (Saccharomyces cerevisiae) two-hybrid assays showed that Arabidopsis VQ proteins interacted specifically with the C-terminal WRKY domains of group I and the sole WRKY domains of group IIc WRKY proteins. Using site-directed mutagenesis, we identified structural features of these two closely related groups of WRKY domains that are critical for interaction with VQ proteins. Quantitative reverse transcription polymerase chain reaction revealed that expression of a majority of Arabidopsis VQ genes was responsive to pathogen infection and salicylic acid treatment. Functional analysis using both knockout mutants and overexpression lines revealed strong phenotypes in growth, development, and susceptibility to pathogen infection. Altered phenotypes were substantially enhanced through cooverexpression of genes encoding interacting VQ and WRKY proteins. These findings indicate that VQ proteins play an important role in plant growth, development, and response to environmental conditions, most likely by acting as cofactors of group I and IIc WRKY transcription factors.

The Development of Protein Microarrays and Their Applications in DNA-Protein and Protein-Protein Interaction Analyses of Arabidopsis Transcription Factors

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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

Core Data of Yeast Interacting Proteins Database (Original Version) - Yeast Interacting Proteins Database | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available y are in the reverse direction. *1 A comprehensive two-hybrid analysis to explore the yeast protein interact...s. 2000 Jan 1;28(1):73-6. *2 The yeast proteome database (YPD) and Caenorhabditis elegans proteome database (WormPD): comprehensive...000 Jan 1;28(1):73-6. *3 A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisia

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

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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.

Topology and weights in a protein domain interaction network--a novel way to predict protein interactions.

Science.gov (United States)

Wuchty, Stefan

2006-05-23

While the analysis of unweighted biological webs as diverse as genetic, protein and metabolic networks allowed spectacular insights in the inner workings of a cell, biological networks are not only determined by their static grid of links. In fact, we expect that the heterogeneity in the utilization of connections has a major impact on the organization of cellular activities as well. We consider a web of interactions between protein domains of the Protein Family database (PFAM), which are weighted by a probability score. We apply metrics that combine the static layout and the weights of the underlying interactions. We observe that unweighted measures as well as their weighted counterparts largely share the same trends in the underlying domain interaction network. However, we only find weak signals that weights and the static grid of interactions are connected entities. Therefore assuming that a protein interaction is governed by a single domain interaction, we observe strong and significant correlations of the highest scoring domain interaction and the confidence of protein interactions in the underlying interactions of yeast and fly. Modeling an interaction between proteins if we find a high scoring protein domain interaction we obtain 1, 428 protein interactions among 361 proteins in the human malaria parasite Plasmodium falciparum. Assessing their quality by a logistic regression method we observe that increasing confidence of predicted interactions is accompanied by high scoring domain interactions and elevated levels of functional similarity and evolutionary conservation. Our results indicate that probability scores are randomly distributed, allowing to treat static grid and weights of domain interactions as separate entities. In particular, these finding confirms earlier observations that a protein interaction is a matter of a single interaction event on domain level. As an immediate application, we show a simple way to predict potential protein interactions

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.

Analysis of Protein-RNA and Protein-Peptide Interactions in Equine Infectious Anemia

Energy Technology Data Exchange (ETDEWEB)

Lee, Jae-Hyung [Iowa State Univ., Ames, IA (United States)

2007-01-01

Macromolecular interactions are essential for virtually all cellular functions including signal transduction processes, metabolic processes, regulation of gene expression and immune responses. This dissertation focuses on the characterization of two important macromolecular interactions involved in the relationship between Equine Infectious Anemia Virus (EIAV) and its host cell in horse: (1) the interaction between the EIAV Rev protein and its binding site, the Rev-responsive element (RRE) and (2) interactions between equine MHC class I molecules and epitope peptides derived from EIAV proteins. EIAV, one of the most divergent members of the lentivirus family, has a single-stranded RNA genome and carries several regulatory and structural proteins within its viral particle. Rev is an essential EIAV regulatory encoded protein that interacts with the viral RRE, a specific binding site in the viral mRNA. Using a combination of experimental and computational methods, the interactions between EIAV Rev and RRE were characterized in detail. EIAV Rev was shown to have a bipartite RNA binding domain contain two arginine rich motifs (ARMs). The RRE secondary structure was determined and specific structural motifs that act as cis-regulatory elements for EIAV Rev-RRE interaction were identified. Interestingly, a structural motif located in the high affinity Rev binding site is well conserved in several diverse lentiviral genoes, including HIV-1. Macromolecular interactions involved in the immune response of the horse to EIAV infection were investigated by analyzing complexes between MHC class I proteins and epitope peptides derived from EIAV Rev, Env and Gag proteins. Computational modeling results provided a mechanistic explanation for the experimental finding that a single amino acid change in the peptide binding domain of the quine MHC class I molecule differentially affectes the recognitino of specific epitopes by EIAV-specific CTL. Together, the findings in this

Visualization of Host-Polerovirus Interaction Topologies Using Protein Interaction Reporter Technology.

Science.gov (United States)

DeBlasio, Stacy L; Chavez, Juan D; Alexander, Mariko M; Ramsey, John; Eng, Jimmy K; Mahoney, Jaclyn; Gray, Stewart M; Bruce, James E; Cilia, Michelle

2016-02-15

Demonstrating direct interactions between host and virus proteins during infection is a major goal and challenge for the field of virology. Most protein interactions are not binary or easily amenable to structural determination. Using infectious preparations of a polerovirus (Potato leafroll virus [PLRV]) and protein interaction reporter (PIR), a revolutionary technology that couples a mass spectrometric-cleavable chemical cross-linker with high-resolution mass spectrometry, we provide the first report of a host-pathogen protein interaction network that includes data-derived, topological features for every cross-linked site that was identified. We show that PLRV virions have hot spots of protein interaction and multifunctional surface topologies, revealing how these plant viruses maximize their use of binding interfaces. Modeling data, guided by cross-linking constraints, suggest asymmetric packing of the major capsid protein in the virion, which supports previous epitope mapping studies. Protein interaction topologies are conserved with other species in the Luteoviridae and with unrelated viruses in the Herpesviridae and Adenoviridae. Functional analysis of three PLRV-interacting host proteins in planta using a reverse-genetics approach revealed a complex, molecular tug-of-war between host and virus. Structural mimicry and diversifying selection-hallmarks of host-pathogen interactions-were identified within host and viral binding interfaces predicted by our models. These results illuminate the functional diversity of the PLRV-host protein interaction network and demonstrate the usefulness of PIR technology for precision mapping of functional host-pathogen protein interaction topologies. The exterior shape of a plant virus and its interacting host and insect vector proteins determine whether a virus will be transmitted by an insect or infect a specific host. Gaining this information is difficult and requires years of experimentation. We used protein interaction

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

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.

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

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

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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.

In silico study of protein to protein interaction analysis of AMP-activated protein kinase and mitochondrial activity in three different farm animal species

Science.gov (United States)

Prastowo, S.; Widyas, N.

2018-03-01

AMP-activated protein kinase (AMPK) is cellular energy censor which works based on ATP and AMP concentration. This protein interacts with mitochondria in determine its activity to generate energy for cell metabolism purposes. For that, this paper aims to compare the protein to protein interaction of AMPK and mitochondrial activity genes in the metabolism of known animal farm (domesticated) that are cattle (Bos taurus), pig (Sus scrofa) and chicken (Gallus gallus). In silico study was done using STRING V.10 as prominent protein interaction database, followed with biological function comparison in KEGG PATHWAY database. Set of genes (12 in total) were used as input analysis that are PRKAA1, PRKAA2, PRKAB1, PRKAB2, PRKAG1, PRKAG2, PRKAG3, PPARGC1, ACC, CPT1B, NRF2 and SOD. The first 7 genes belong to gene in AMPK family, while the last 5 belong to mitochondrial activity genes. The protein interaction result shows 11, 8 and 5 metabolism pathways in Bos taurus, Sus scrofa and Gallus gallus, respectively. The top pathway in Bos taurus is AMPK signaling pathway (10 genes), Sus scrofa is Adipocytokine signaling pathway (8 genes) and Gallus gallus is FoxO signaling pathway (5 genes). Moreover, the common pathways found in those 3 species are Adipocytokine signaling pathway, Insulin signaling pathway and FoxO signaling pathway. Genes clustered in Adipocytokine and Insulin signaling pathway are PRKAA2, PPARGC1A, PRKAB1 and PRKAG2. While, in FoxO signaling pathway are PRKAA2, PRKAB1, PRKAG2. According to that, we found PRKAA2, PRKAB1 and PRKAG2 are the common genes. Based on the bioinformatics analysis, we can demonstrate that protein to protein interaction shows distinct different of metabolism in different species. However, further validation is needed to give a clear explanation.

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.

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...

Topology and weights in a protein domain interaction network – a novel way to predict protein interactions

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Wuchty Stefan

2006-05-01

Full Text Available Abstract Background While the analysis of unweighted biological webs as diverse as genetic, protein and metabolic networks allowed spectacular insights in the inner workings of a cell, biological networks are not only determined by their static grid of links. In fact, we expect that the heterogeneity in the utilization of connections has a major impact on the organization of cellular activities as well. Results We consider a web of interactions between protein domains of the Protein Family database (PFAM, which are weighted by a probability score. We apply metrics that combine the static layout and the weights of the underlying interactions. We observe that unweighted measures as well as their weighted counterparts largely share the same trends in the underlying domain interaction network. However, we only find weak signals that weights and the static grid of interactions are connected entities. Therefore assuming that a protein interaction is governed by a single domain interaction, we observe strong and significant correlations of the highest scoring domain interaction and the confidence of protein interactions in the underlying interactions of yeast and fly. Modeling an interaction between proteins if we find a high scoring protein domain interaction we obtain 1, 428 protein interactions among 361 proteins in the human malaria parasite Plasmodium falciparum. Assessing their quality by a logistic regression method we observe that increasing confidence of predicted interactions is accompanied by high scoring domain interactions and elevated levels of functional similarity and evolutionary conservation. Conclusion Our results indicate that probability scores are randomly distributed, allowing to treat static grid and weights of domain interactions as separate entities. In particular, these finding confirms earlier observations that a protein interaction is a matter of a single interaction event on domain level. As an immediate application, we

Water-Protein Interactions: The Secret of Protein Dynamics

Directory of Open Access Journals (Sweden)

Silvia Martini

2013-01-01

Full Text Available Water-protein interactions help to maintain flexible conformation conditions which are required for multifunctional protein recognition processes. The intimate relationship between the protein surface and hydration water can be analyzed by studying experimental water properties measured in protein systems in solution. In particular, proteins in solution modify the structure and the dynamics of the bulk water at the solute-solvent interface. The ordering effects of proteins on hydration water are extended for several angstroms. In this paper we propose a method for analyzing the dynamical properties of the water molecules present in the hydration shells of proteins. The approach is based on the analysis of the effects of protein-solvent interactions on water protons NMR relaxation parameters. NMR relaxation parameters, especially the nonselective (R1NS and selective (R1SE spin-lattice relaxation rates of water protons, are useful for investigating the solvent dynamics at the macromolecule-solvent interfaces as well as the perturbation effects caused by the water-macromolecule interactions on the solvent dynamical properties. In this paper we demonstrate that Nuclear Magnetic Resonance Spectroscopy can be used to determine the dynamical contributions of proteins to the water molecules belonging to their hydration shells.

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

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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.

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

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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.

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)

The drug-minded protein interaction database (DrumPID) for efficient target analysis and drug development.

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Kunz, Meik; Liang, Chunguang; Nilla, Santosh; Cecil, Alexander; Dandekar, Thomas

2016-01-01

The drug-minded protein interaction database (DrumPID) has been designed to provide fast, tailored information on drugs and their protein networks including indications, protein targets and side-targets. Starting queries include compound, target and protein interactions and organism-specific protein families. Furthermore, drug name, chemical structures and their SMILES notation, affected proteins (potential drug targets), organisms as well as diseases can be queried including various combinations and refinement of searches. Drugs and protein interactions are analyzed in detail with reference to protein structures and catalytic domains, related compound structures as well as potential targets in other organisms. DrumPID considers drug functionality, compound similarity, target structure, interactome analysis and organismic range for a compound, useful for drug development, predicting drug side-effects and structure-activity relationships.Database URL:http://drumpid.bioapps.biozentrum.uni-wuerzburg.de. © The Author(s) 2016. Published by Oxford University Press.

Computational analysis of RNA-protein interaction interfaces via the Voronoi diagram.

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Mahdavi, Sedigheh; Mohades, Ali; Salehzadeh Yazdi, Ali; Jahandideh, Samad; Masoudi-Nejad, Ali

2012-01-21

Cellular functions are mediated by various biological processes including biomolecular interactions, such as protein-protein, DNA-protein and RNA-protein interactions in which RNA-Protein interactions are indispensable for many biological processes like cell development and viral replication. Unlike the protein-protein and protein-DNA interactions, accurate mechanisms and structures of the RNA-Protein complexes are not fully understood. A large amount of theoretical evidence have shown during the past several years that computational geometry is the first pace in understanding the binding profiles and plays a key role in the study of intricate biological structures, interactions and complexes. In this paper, RNA-Protein interaction interface surface is computed via the weighted Voronoi diagram of atoms. Using two filter operations provides a natural definition for interface atoms as classic methods. Unbounded parts of Voronoi facets that are far from the complex are trimmed using modified convex hull of atom centers. This algorithm is implemented to a database with different RNA-Protein complexes extracted from Protein Data Bank (PDB). Afterward, the features of interfaces have been computed and compared with classic method. The results show high correlation coefficients between interface size in the Voronoi model and the classical model based on solvent accessibility, as well as high accuracy and precision in comparison to classical model. Copyright © 2011 Elsevier Ltd. All rights reserved.

Extraction of Protein Interaction Data: A Comparative Analysis of Methods in Use

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Jose Hena

2007-01-01

Full Text Available Several natural language processing tools, both commercial and freely available, are used to extract protein interactions from publications. Methods used by these tools include pattern matching to dynamic programming with individual recall and precision rates. A methodical survey of these tools, keeping in mind the minimum interaction information a researcher would need, in comparison to manual analysis has not been carried out. We compared data generated using some of the selected NLP tools with manually curated protein interaction data (PathArt and IMaps to comparatively determine the recall and precision rate. The rates were found to be lower than the published scores when a normalized definition for interaction is considered. Each data point captured wrongly or not picked up by the tool was analyzed. Our evaluation brings forth critical failures of NLP tools and provides pointers for the development of an ideal NLP tool.

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)

A functional carbohydrate chip platform for analysis of carbohydrate-protein interaction

International Nuclear Information System (INIS)

Seo, Jeong Hyun; Kim, Chang Sup; Hwang, Byeong Hee; Cha, Hyung Joon

2010-01-01

A carbohydrate chip based on glass or other transparent surfaces has been suggested as a potential tool for high-throughput analysis of carbohydrate-protein interactions. Here we proposed a facile, efficient, and cost-effective method whereby diverse carbohydrate types are modified in a single step and directly immobilized onto a glass surface, with retention of functional orientation. We modified various types of carbohydrates by reductive amination, in which reducing sugar groups were coupled with 4-(2-aminoethyl)aniline, which has di-amine groups at both ends. The modified carbohydrates were covalently attached to an amino-reactive NHS-activated glass surface by formation of stable amide bonds. This proposed method was applied for efficient construction of a carbohydrate microarray to analyze carbohydrate-protein interactions. The carbohydrate chip prepared using our method can be successfully used in diverse biomimetic studies of carbohydrates, including carbohydrate-biomolecule interactions, and carbohydrate sensor chip or microarray development for diagnosis and screening.

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

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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

Data management of protein interaction networks

CERN Document Server

Cannataro, Mario

2012-01-01

Interactomics: a complete survey from data generation to knowledge extraction With the increasing use of high-throughput experimental assays, more and more protein interaction databases are becoming available. As a result, computational analysis of protein-to-protein interaction (PPI) data and networks, now known as interactomics, has become an essential tool to determine functionally associated proteins. From wet lab technologies to data management to knowledge extraction, this timely book guides readers through the new science of interactomics, giving them the tools needed to: Generate

Discerning molecular interactions: A comprehensive review on biomolecular interaction databases and network analysis tools.

Science.gov (United States)

Miryala, Sravan Kumar; Anbarasu, Anand; Ramaiah, Sudha

2018-02-05

Computational analysis of biomolecular interaction networks is now gaining a lot of importance to understand the functions of novel genes/proteins. Gene interaction (GI) network analysis and protein-protein interaction (PPI) network analysis play a major role in predicting the functionality of interacting genes or proteins and gives an insight into the functional relationships and evolutionary conservation of interactions among the genes. An interaction network is a graphical representation of gene/protein interactome, where each gene/protein is a node, and interaction between gene/protein is an edge. In this review, we discuss the popular open source databases that serve as data repositories to search and collect protein/gene interaction data, and also tools available for the generation of interaction network, visualization and network analysis. Also, various network analysis approaches like topological approach and clustering approach to study the network properties and functional enrichment server which illustrates the functions and pathway of the genes and proteins has been discussed. Hence the distinctive attribute mentioned in this review is not only to provide an overview of tools and web servers for gene and protein-protein interaction (PPI) network analysis but also to extract useful and meaningful information from the interaction networks. Copyright © 2017 Elsevier B.V. All rights reserved.

Hydrophobic Interaction Chromatography for Bottom-Up Proteomics Analysis of Single Proteins and Protein Complexes.

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Rackiewicz, Michal; Große-Hovest, Ludger; Alpert, Andrew J; Zarei, Mostafa; Dengjel, Jörn

2017-06-02

Hydrophobic interaction chromatography (HIC) is a robust standard analytical method to purify proteins while preserving their biological activity. It is widely used to study post-translational modifications of proteins and drug-protein interactions. In the current manuscript we employed HIC to separate proteins, followed by bottom-up LC-MS/MS experiments. We used this approach to fractionate antibody species followed by comprehensive peptide mapping as well as to study protein complexes in human cells. HIC-reversed-phase chromatography (RPC)-mass spectrometry (MS) is a powerful alternative to fractionate proteins for bottom-up proteomics experiments making use of their distinct hydrophobic properties.

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

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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.

Structural and Functional Analysis of VQ Motif-Containing Proteins in Arabidopsis as Interacting Proteins of WRKY Transcription Factors1[W][OA

Science.gov (United States)

Cheng, Yuan; Zhou, Yuan; Yang, Yan; Chi, Ying-Jun; Zhou, Jie; Chen, Jian-Ye; Wang, Fei; Fan, Baofang; Shi, Kai; Zhou, Yan-Hong; Yu, Jing-Quan; Chen, Zhixiang

2012-01-01

WRKY transcription factors are encoded by a large gene superfamily with a broad range of roles in plants. Recently, several groups have reported that proteins containing a short VQ (FxxxVQxLTG) motif interact with WRKY proteins. We have recently discovered that two VQ proteins from Arabidopsis (Arabidopsis thaliana), SIGMA FACTOR-INTERACTING PROTEIN1 and SIGMA FACTOR-INTERACTING PROTEIN2, act as coactivators of WRKY33 in plant defense by specifically recognizing the C-terminal WRKY domain and stimulating the DNA-binding activity of WRKY33. In this study, we have analyzed the entire family of 34 structurally divergent VQ proteins from Arabidopsis. Yeast (Saccharomyces cerevisiae) two-hybrid assays showed that Arabidopsis VQ proteins interacted specifically with the C-terminal WRKY domains of group I and the sole WRKY domains of group IIc WRKY proteins. Using site-directed mutagenesis, we identified structural features of these two closely related groups of WRKY domains that are critical for interaction with VQ proteins. Quantitative reverse transcription polymerase chain reaction revealed that expression of a majority of Arabidopsis VQ genes was responsive to pathogen infection and salicylic acid treatment. Functional analysis using both knockout mutants and overexpression lines revealed strong phenotypes in growth, development, and susceptibility to pathogen infection. Altered phenotypes were substantially enhanced through cooverexpression of genes encoding interacting VQ and WRKY proteins. These findings indicate that VQ proteins play an important role in plant growth, development, and response to environmental conditions, most likely by acting as cofactors of group I and IIc WRKY transcription factors. PMID:22535423

Aquaporin Protein-Protein Interactions

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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.

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.

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.

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.

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

Prediction of the anti-inflammatory mechanisms of curcumin by module-based protein interaction network analysis

Directory of Open Access Journals (Sweden)

Yanxiong Gan

2015-11-01

Full Text Available Curcumin, the medically active component from Curcuma longa (Turmeric, is widely used to treat inflammatory diseases. Protein interaction network (PIN analysis was used to predict its mechanisms of molecular action. Targets of curcumin were obtained based on ChEMBL and STITCH databases. Protein–protein interactions (PPIs were extracted from the String database. The PIN of curcumin was constructed by Cytoscape and the function modules identified by gene ontology (GO enrichment analysis based on molecular complex detection (MCODE. A PIN of curcumin with 482 nodes and 1688 interactions was constructed, which has scale-free, small world and modular properties. Based on analysis of these function modules, the mechanism of curcumin is proposed. Two modules were found to be intimately associated with inflammation. With function modules analysis, the anti-inflammatory effects of curcumin were related to SMAD, ERG and mediation by the TLR family. TLR9 may be a potential target of curcumin to treat inflammation.

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.

Weighted Protein Interaction Network Analysis of Frontotemporal Dementia.

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Ferrari, Raffaele; Lovering, Ruth C; Hardy, John; Lewis, Patrick A; Manzoni, Claudia

2017-02-03

The genetic analysis of complex disorders has undoubtedly led to the identification of a wealth of associations between genes and specific traits. However, moving from genetics to biochemistry one gene at a time has, to date, rather proved inefficient and under-powered to comprehensively explain the molecular basis of phenotypes. Here we present a novel approach, weighted protein-protein interaction network analysis (W-PPI-NA), to highlight key functional players within relevant biological processes associated with a given trait. This is exemplified in the current study by applying W-PPI-NA to frontotemporal dementia (FTD): We first built the state of the art FTD protein network (FTD-PN) and then analyzed both its topological and functional features. The FTD-PN resulted from the sum of the individual interactomes built around FTD-spectrum genes, leading to a total of 4198 nodes. Twenty nine of 4198 nodes, called inter-interactome hubs (IIHs), represented those interactors able to bridge over 60% of the individual interactomes. Functional annotation analysis not only reiterated and reinforced previous findings from single genes and gene-coexpression analyses but also indicated a number of novel potential disease related mechanisms, including DNA damage response, gene expression regulation, and cell waste disposal and potential biomarkers or therapeutic targets including EP300. These processes and targets likely represent the functional core impacted in FTD, reflecting the underlying genetic architecture contributing to disease. The approach presented in this study can be applied to other complex traits for which risk-causative genes are known as it provides a promising tool for setting the foundations for collating genomics and wet laboratory data in a bidirectional manner. This is and will be critical to accelerate molecular target prioritization and drug discovery.

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.

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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

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

Nanoparticles for Protein Sensing in Primary Containers: Interaction Analysis and Application.

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Pérez Medina Martínez, Víctor; Espinosa-de la Garza, Carlos E; Méndez-Silva, Diego A; Bolívar-Vichido, Mariana; Flores-Ortiz, Luis F; Pérez, Néstor O

2018-05-01

Silver nanoparticles (AgNPs) are known to interact with proteins, leading to modifications of the plasmonic absorption that can be used to monitor this interaction, entailing a promising application for sensing adsorption of therapeutic proteins in primary containers. First, transmission electron microscopy in combination with plasmonic absorption and light scattering responses were used to characterize AgNPs and protein-AgNP complexes, including its concentration dependence, using two therapeutic molecules as models: a monoclonal antibody (mAb) and a synthetic copolymer (SC). Upon interaction, a protein corona was formed around AgNPs with the consequent shifting and broadening of their characteristic surface plasmon resonance (SPR) band (400 nm) to 410 nm and longer wavelenghts. Additional studies revealed secondary and three-dimensional structure modifications of model proteins upon interaction with AgNPs by circular dichroism and fluorescence techniques, respectively. Based on the modification of the SPR condition of AgNPs upon interaction with proteins, we developed a novel protein-sensing application of AgNPs in primary containers. This strategy was used to conduct a compatibility assessment of model proteins towards five commercially available prefillable glass syringe (PFS) models. mAb- and SC-exposed PFSs showed that 74 and 94% of cases were positive for protein adsorption, respectively. Interestingly, protein adsorption on 15% of total tested PFSs was negligible (below the nanogram level). Our results highlight the need of a case-by-case compatibility assessment of therapeutic proteins and their primary containers. This strategy has the potential to be easily applied on other containers and implemented during early-stage product development by pharmaceutical companies and for routine use during batch release by packaging manufacturers.

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

NAPS: Network Analysis of Protein Structures

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Chakrabarty, Broto; Parekh, Nita

2016-01-01

Traditionally, protein structures have been analysed by the secondary structure architecture and fold arrangement. An alternative approach that has shown promise is modelling proteins as a network of non-covalent interactions between amino acid residues. The network representation of proteins provide a systems approach to topological analysis of complex three-dimensional structures irrespective of secondary structure and fold type and provide insights into structure-function relationship. We have developed a web server for network based analysis of protein structures, NAPS, that facilitates quantitative and qualitative (visual) analysis of residue–residue interactions in: single chains, protein complex, modelled protein structures and trajectories (e.g. from molecular dynamics simulations). The user can specify atom type for network construction, distance range (in Å) and minimal amino acid separation along the sequence. NAPS provides users selection of node(s) and its neighbourhood based on centrality measures, physicochemical properties of amino acids or cluster of well-connected residues (k-cliques) for further analysis. Visual analysis of interacting domains and protein chains, and shortest path lengths between pair of residues are additional features that aid in functional analysis. NAPS support various analyses and visualization views for identifying functional residues, provide insight into mechanisms of protein folding, domain-domain and protein–protein interactions for understanding communication within and between proteins. URL:http://bioinf.iiit.ac.in/NAPS/. PMID:27151201

Bioinformatic analysis of xenobiotic reactive metabolite target proteins and their interacting partners

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Hanzlik Robert P

2009-06-01

Full Text Available Abstract Background Protein covalent binding by reactive metabolites of drugs, chemicals and natural products can lead to acute cytotoxicity. Recent rapid progress in reactive metabolite target protein identification has shown that adduction is surprisingly selective and inspired the hope that analysis of target proteins might reveal protein factors that differentiate target- vs. non-target proteins and illuminate mechanisms connecting covalent binding to cytotoxicity. Results Sorting 171 known reactive metabolite target proteins revealed a number of GO categories and KEGG pathways to be significantly enriched in targets, but in most cases the classes were too large, and the "percent coverage" too small, to allow meaningful conclusions about mechanisms of toxicity. However, a similar analysis of the directlyinteracting partners of 28 common targets of multiple reactive metabolites revealed highly significant enrichments in terms likely to be highly relevant to cytotoxicity (e.g., MAP kinase pathways, apoptosis, response to unfolded protein. Machine learning was used to rank the contribution of 211 computed protein features to determining protein susceptibility to adduction. Protein lysine (but not cysteine content and protein instability index (i.e., rate of turnover in vivo were among the features most important to determining susceptibility. Conclusion As yet there is no good explanation for why some low-abundance proteins become heavily adducted while some abundant proteins become only lightly adducted in vivo. Analyzing the directly interacting partners of target proteins appears to yield greater insight into mechanisms of toxicity than analyzing target proteins per se. The insights provided can readily be formulated as hypotheses to test in future experimental studies.

gRINN: a tool for calculation of residue interaction energies and protein energy network analysis of molecular dynamics simulations.

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Serçinoglu, Onur; Ozbek, Pemra

2018-05-25

Atomistic molecular dynamics (MD) simulations generate a wealth of information related to the dynamics of proteins. If properly analyzed, this information can lead to new insights regarding protein function and assist wet-lab experiments. Aiming to identify interactions between individual amino acid residues and the role played by each in the context of MD simulations, we present a stand-alone software called gRINN (get Residue Interaction eNergies and Networks). gRINN features graphical user interfaces (GUIs) and a command-line interface for generating and analyzing pairwise residue interaction energies and energy correlations from protein MD simulation trajectories. gRINN utilizes the features of NAMD or GROMACS MD simulation packages and automatizes the steps necessary to extract residue-residue interaction energies from user-supplied simulation trajectories, greatly simplifying the analysis for the end-user. A GUI, including an embedded molecular viewer, is provided for visualization of interaction energy time-series, distributions, an interaction energy matrix, interaction energy correlations and a residue correlation matrix. gRINN additionally offers construction and analysis of Protein Energy Networks, providing residue-based metrics such as degrees, betweenness-centralities, closeness centralities as well as shortest path analysis. gRINN is free and open to all users without login requirement at http://grinn.readthedocs.io.

PINK1-Interacting Proteins: Proteomic Analysis of Overexpressed PINK1

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Aleksandar Rakovic

2011-01-01

Full Text Available Recent publications suggest that the Parkinson's disease- (PD- related PINK1/Parkin pathway promotes elimination of dysfunctional mitochondria by autophagy. We used tandem affinity purification (TAP, SDS-PAGE, and mass spectrometry as a first step towards identification of possible substrates for PINK1. The cellular abundance of selected identified interactors was investigated by Western blotting. Furthermore, one candidate gene was sequenced in 46 patients with atypical PD. In addition to two known binding partners (HSP90, CDC37, 12 proteins were identified using the TAP assay; four of which are mitochondrially localized (GRP75, HSP60, LRPPRC, and TUFM. Western blot analysis showed no differences in cellular abundance of these proteins comparing PINK1 mutant and control fibroblasts. When sequencing LRPPRC, four exonic synonymous changes and 20 polymorphisms in noncoding regions were detected. Our study provides a list of putative PINK1 binding partners, confirming previously described interactions, but also introducing novel mitochondrial proteins as potential components of the PINK1/Parkin mitophagy pathway.

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

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

Analysis of protein interactions at native chloroplast membranes by ellipsometry.

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Verena Kriechbaumer

Full Text Available Membrane bound receptors play vital roles in cell signaling, and are the target for many drugs, yet their interactions with ligands are difficult to study by conventional techniques due to the technical difficulty of monitoring these interactions in lipid environments. In particular, the ability to analyse the behaviour of membrane proteins in their native membrane environment is limited. Here, we have developed a quantitative approach to detect specific interactions between low-abundance chaperone receptors within native chloroplast membranes and their soluble chaperone partners. Langmuir-Schaefer film deposition was used to deposit native chloroplasts onto gold-coated glass slides, and interactions between the molecular chaperones Hsp70 and Hsp90 and their receptors in the chloroplast membranes were detected and quantified by total internal reflection ellipsometry (TIRE. We show that native chloroplast membranes deposited on gold-coated glass slides using Langmuir-Schaefer films retain functional receptors capable of binding chaperones with high specificity and affinity. Taking into account the low chaperone receptor abundance in native membranes, these binding properties are consistent with data generated using soluble forms of the chloroplast chaperone receptors, OEP61 and Toc64. Therefore, we conclude that chloroplasts have the capacity to selectively bind chaperones, consistent with the notion that chaperones play an important role in protein targeting to chloroplasts. Importantly, this method of monitoring by TIRE does not require any protein labelling. This novel combination of techniques should be applicable to a wide variety of membranes and membrane protein receptors, thus presenting the opportunity to quantify protein interactions involved in fundamental cellular processes, and to screen for drugs that target membrane proteins.

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.

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

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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.

Specificity and affinity quantification of protein-protein interactions.

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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.

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

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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.

Identification of proteins that may directly interact with human RPA.

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Nakaya, Ryou; Takaya, Junichiro; Onuki, Takeshi; Moritani, Mariko; Nozaki, Naohito; Ishimi, Yukio

2010-11-01

RPA, which consisted of three subunits (RPA1, 2 and 3), plays essential roles in DNA transactions. At the DNA replication forks, RPA binds to single-stranded DNA region to stabilize the structure and to assemble other replication proteins. Interactions between RPA and several replication proteins have been reported but the analysis is not comprehensive. We systematically performed the qualitative analysis to identify RPA interaction partners to understand the protein-protein interaction at the replication forks. We expressed in insect cells the three subunits of human RPA, together with one replication protein, which is present at the forks under normal conditions and/or under the replication stress conditions, to examine the interaction. Among 30 proteins examined in total, it was found that at least 14 proteins interacted with RPA. RPA interacted with MCM3-7, MCM-BP and CDC45 proteins among the proteins that play roles in the initiation and the elongation of the DNA replication. RPA bound with TIPIN, CLASPIN and RAD17, which are involved in the DNA replication checkpoint functions. RPA also bound with cyclin-dependent kinases and an amino-terminal fragment of Rb protein that negatively regulates DNA replication. These results suggest that RPA interacts with the specific proteins among those that play roles in the regulation of the replication fork progression.

From nonspecific DNA-protein encounter complexes to the prediction of DNA-protein interactions.

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Mu Gao

2009-03-01

Full Text Available DNA-protein interactions are involved in many essential biological activities. Because there is no simple mapping code between DNA base pairs and protein amino acids, the prediction of DNA-protein interactions is a challenging problem. Here, we present a novel computational approach for predicting DNA-binding protein residues and DNA-protein interaction modes without knowing its specific DNA target sequence. Given the structure of a DNA-binding protein, the method first generates an ensemble of complex structures obtained by rigid-body docking with a nonspecific canonical B-DNA. Representative models are subsequently selected through clustering and ranking by their DNA-protein interfacial energy. Analysis of these encounter complex models suggests that the recognition sites for specific DNA binding are usually favorable interaction sites for the nonspecific DNA probe and that nonspecific DNA-protein interaction modes exhibit some similarity to specific DNA-protein binding modes. Although the method requires as input the knowledge that the protein binds DNA, in benchmark tests, it achieves better performance in identifying DNA-binding sites than three previously established methods, which are based on sophisticated machine-learning techniques. We further apply our method to protein structures predicted through modeling and demonstrate that our method performs satisfactorily on protein models whose root-mean-square Calpha deviation from native is up to 5 A from their native structures. This study provides valuable structural insights into how a specific DNA-binding protein interacts with a nonspecific DNA sequence. The similarity between the specific DNA-protein interaction mode and nonspecific interaction modes may reflect an important sampling step in search of its specific DNA targets by a DNA-binding protein.

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

Interaction between plate make and protein in protein crystallisation screening.

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Gordon J King

Full Text Available BACKGROUND: Protein crystallisation screening involves the parallel testing of large numbers of candidate conditions with the aim of identifying conditions suitable as a starting point for the production of diffraction quality crystals. Generally, condition screening is performed in 96-well plates. While previous studies have examined the effects of protein construct, protein purity, or crystallisation condition ingredients on protein crystallisation, few have examined the effect of the crystallisation plate. METHODOLOGY/PRINCIPAL FINDINGS: We performed a statistically rigorous examination of protein crystallisation, and evaluated interactions between crystallisation success and plate row/column, different plates of same make, different plate makes and different proteins. From our analysis of protein crystallisation, we found a significant interaction between plate make and the specific protein being crystallised. CONCLUSIONS/SIGNIFICANCE: Protein crystal structure determination is the principal method for determining protein structure but is limited by the need to produce crystals of the protein under study. Many important proteins are difficult to crystallize, so that identification of factors that assist crystallisation could open up the structure determination of these more challenging targets. Our findings suggest that protein crystallisation success may be improved by matching a protein with its optimal plate make.

Predicting and validating protein interactions using network structure.

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Pao-Yang Chen

2008-07-01

Full Text Available Protein interactions play a vital part in the function of a cell. As experimental techniques for detection and validation of protein interactions are time consuming, there is a need for computational methods for this task. Protein interactions appear to form a network with a relatively high degree of local clustering. In this paper we exploit this clustering by suggesting a score based on triplets of observed protein interactions. The score utilises both protein characteristics and network properties. Our score based on triplets is shown to complement existing techniques for predicting protein interactions, outperforming them on data sets which display a high degree of clustering. The predicted interactions score highly against test measures for accuracy. Compared to a similar score derived from pairwise interactions only, the triplet score displays higher sensitivity and specificity. By looking at specific examples, we show how an experimental set of interactions can be enriched and validated. As part of this work we also examine the effect of different prior databases upon the accuracy of prediction and find that the interactions from the same kingdom give better results than from across kingdoms, suggesting that there may be fundamental differences between the networks. These results all emphasize that network structure is important and helps in the accurate prediction of protein interactions. The protein interaction data set and the program used in our analysis, and a list of predictions and validations, are available at http://www.stats.ox.ac.uk/bioinfo/resources/PredictingInteractions.

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

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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.

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...

Specificity and evolvability in eukaryotic protein interaction networks.

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Pedro Beltrao

2007-02-01

Full Text Available Progress in uncovering the protein interaction networks of several species has led to questions of what underlying principles might govern their organization. Few studies have tried to determine the impact of protein interaction network evolution on the observed physiological differences between species. Using comparative genomics and structural information, we show here that eukaryotic species have rewired their interactomes at a fast rate of approximately 10(-5 interactions changed per protein pair, per million years of divergence. For Homo sapiens this corresponds to 10(3 interactions changed per million years. Additionally we find that the specificity of binding strongly determines the interaction turnover and that different biological processes show significantly different link dynamics. In particular, human proteins involved in immune response, transport, and establishment of localization show signs of positive selection for change of interactions. Our analysis suggests that a small degree of molecular divergence can give rise to important changes at the network level. We propose that the power law distribution observed in protein interaction networks could be partly explained by the cell's requirement for different degrees of protein binding specificity.

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...

An automated wide-field time-gated optically sectioning fluorescence lifetime imaging multiwell plate reader for high-content analysis of protein-protein interactions

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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.

Evolutionary reprograming of protein-protein interaction specificity.

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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.

Determination and Quantification of Molecular Interactions in Protein Films: A Review

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Felicia Hammann

2014-12-01

Full Text Available Protein based films are nowadays also prepared with the aim of replacing expensive, crude oil-based polymers as environmentally friendly and renewable alternatives. The protein structure determines the ability of protein chains to form intra- and intermolecular bonds, whereas the degree of cross-linking depends on the amino acid composition and molecular weight of the protein, besides the conditions used in film preparation and processing. The functionality varies significantly depending on the type of protein and affects the resulting film quality and properties. This paper reviews the methods used in examination of molecular interactions in protein films and discusses how these intermolecular interactions can be quantified. The qualitative determination methods can be distinguished by structural analysis of solutions (electrophoretic analysis, size exclusion chromatography and analysis of solid films (spectroscopy techniques, X-ray scattering methods. To quantify molecular interactions involved, two methods were found to be the most suitable: protein film swelling and solubility. The importance of non-covalent and covalent interactions in protein films can be investigated using different solvents. The research was focused on whey protein, whereas soy protein and wheat gluten were included as further examples of proteins.

Determination and Quantification of Molecular Interactions in Protein Films: A Review.

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Hammann, Felicia; Schmid, Markus

2014-12-10

Protein based films are nowadays also prepared with the aim of replacing expensive, crude oil-based polymers as environmentally friendly and renewable alternatives. The protein structure determines the ability of protein chains to form intra- and intermolecular bonds, whereas the degree of cross-linking depends on the amino acid composition and molecular weight of the protein, besides the conditions used in film preparation and processing. The functionality varies significantly depending on the type of protein and affects the resulting film quality and properties. This paper reviews the methods used in examination of molecular interactions in protein films and discusses how these intermolecular interactions can be quantified. The qualitative determination methods can be distinguished by structural analysis of solutions (electrophoretic analysis, size exclusion chromatography) and analysis of solid films (spectroscopy techniques, X-ray scattering methods). To quantify molecular interactions involved, two methods were found to be the most suitable: protein film swelling and solubility. The importance of non-covalent and covalent interactions in protein films can be investigated using different solvents. The research was focused on whey protein, whereas soy protein and wheat gluten were included as further examples of proteins.

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

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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.

KFC Server: interactive forecasting of protein interaction hot spots.

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Darnell, Steven J; LeGault, Laura; Mitchell, Julie C

2008-07-01

The KFC Server is a web-based implementation of the KFC (Knowledge-based FADE and Contacts) model-a machine learning approach for the prediction of binding hot spots, or the subset of residues that account for most of a protein interface's; binding free energy. The server facilitates the automated analysis of a user submitted protein-protein or protein-DNA interface and the visualization of its hot spot predictions. For each residue in the interface, the KFC Server characterizes its local structural environment, compares that environment to the environments of experimentally determined hot spots and predicts if the interface residue is a hot spot. After the computational analysis, the user can visualize the results using an interactive job viewer able to quickly highlight predicted hot spots and surrounding structural features within the protein structure. The KFC Server is accessible at http://kfc.mitchell-lab.org.

Prediction of localization and interactions of apoptotic proteins

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Matula Pavel

2009-07-01

Full Text Available Abstract During apoptosis several mitochondrial proteins are released. Some of them participate in caspase-independent nuclear DNA degradation, especially apoptosis-inducing factor (AIF and endonuclease G (endoG. Another interesting protein, which was expected to act similarly as AIF due to the high sequence homology with AIF is AIF-homologous mitochondrion-associated inducer of death (AMID. We studied the structure, cellular localization, and interactions of several proteins in silico and also in cells using fluorescent microscopy. We found the AMID protein to be cytoplasmic, most probably incorporated into the cytoplasmic side of the lipid membranes. Bioinformatic predictions were conducted to analyze the interactions of the studied proteins with each other and with other possible partners. We conducted molecular modeling of proteins with unknown 3D structures. These models were then refined by MolProbity server and employed in molecular docking simulations of interactions. Our results show data acquired using a combination of modern in silico methods and image analysis to understand the localization, interactions and functions of proteins AMID, AIF, endonuclease G, and other apoptosis-related proteins.

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

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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

-depth characterizations. Characterizations involved both in vivo and in vitro independent methods to confirm protein-protein interactions and the evaluation of novel phenotypes resulting from creation of transgenic poplar and Arabidopsis plants engineered for increased or decreased expression of the selected genes. Transgenic poplar trees were studied in growth chamber, greenhouse, and two separate replicated field trials involving over 25 distinct wood-associated proteins. In-depth characterizations yielding positive results include the following. First, a NAC domain transcription factor (NAC154) that is a promoter of stress response and dormancy in trees was discovered. Increasing expression of NAC154 caused stunted growth and premature senescence, while decreasing expression led to both delayed bud and leaf expansion in spring and delayed leaf drop (i.e., prolonged leaf retention) in fall. Second, we discovered and characterized a new connection between a negative regulator of wood formation, the NAC domain transcription factor XND1, and an important regulator of cell division and cell differentiation, RBR. Third, we identified a new network of interacting wood-associated transcription factors belonging to the MYB and HD families. One of the HD family proteins, WOX13, was used to prepare transgenic poplar for high-level expression, resulting in significantly increased lateral branch growth. Finally, we modeled and performed in vitro analyses of the insect protein rubber resilin and we prepared transgenic Arabidopsis plants for expression of resilin to test the feasibility of using resilin to modify lignin cross-linking in wood and reduce recalcitrance and improve yield of fermentable sugars for biofuels production. Analysis of these and additional transgenics created with this support is continuing.

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

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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.

Protein- protein interaction detection system using fluorescent protein microdomains

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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.

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

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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.

Characterization of host proteins interacting with the lymphocytic choriomeningitis virus L protein.

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Khamina, Kseniya; Lercher, Alexander; Caldera, Michael; Schliehe, Christopher; Vilagos, Bojan; Sahin, Mehmet; Kosack, Lindsay; Bhattacharya, Anannya; Májek, Peter; Stukalov, Alexey; Sacco, Roberto; James, Leo C; Pinschewer, Daniel D; Bennett, Keiryn L; Menche, Jörg; Bergthaler, Andreas

2017-12-01

RNA-dependent RNA polymerases (RdRps) play a key role in the life cycle of RNA viruses and impact their immunobiology. The arenavirus lymphocytic choriomeningitis virus (LCMV) strain Clone 13 provides a benchmark model for studying chronic infection. A major genetic determinant for its ability to persist maps to a single amino acid exchange in the viral L protein, which exhibits RdRp activity, yet its functional consequences remain elusive. To unravel the L protein interactions with the host proteome, we engineered infectious L protein-tagged LCMV virions by reverse genetics. A subsequent mass-spectrometric analysis of L protein pulldowns from infected human cells revealed a comprehensive network of interacting host proteins. The obtained LCMV L protein interactome was bioinformatically integrated with known host protein interactors of RdRps from other RNA viruses, emphasizing interconnected modules of human proteins. Functional characterization of selected interactors highlighted proviral (DDX3X) as well as antiviral (NKRF, TRIM21) host factors. To corroborate these findings, we infected Trim21-/- mice with LCMV and found impaired virus control in chronic infection. These results provide insights into the complex interactions of the arenavirus LCMV and other viral RdRps with the host proteome and contribute to a better molecular understanding of how chronic viruses interact with their host.

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.

Next-Generation Sequencing for Binary Protein-Protein Interactions

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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.

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

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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.

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

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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.

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

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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.

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

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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.

Structural analysis of protein-ligand interactions: the binding of endogenous compounds and of synthetic drugs.

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Gallina, Anna M; Bork, Peer; Bordo, Domenico

2014-02-01

The large number of macromolecular structures deposited with the Protein Data Bank (PDB) describing complexes between proteins and either physiological compounds or synthetic drugs made it possible a systematic analysis of the interactions occurring between proteins and their ligands. In this work, the binding pockets of about 4000 PDB protein-ligand complexes were investigated and amino acid and interaction types were analyzed. The residues observed with lowest frequency in protein sequences, Trp, His, Met, Tyr, and Phe, turned out to be the most abundant in binding pockets. Significant differences between drug-like and physiological compounds were found. On average, physiological compounds establish with respect to drugs about twice as many hydrogen bonds with protein atoms, whereas drugs rely more on hydrophobic interactions to establish target selectivity. The large number of PDB structures describing homologous proteins in complex with the same ligand made it possible to analyze the conservation of binding pocket residues among homologous protein structures bound to the same ligand, showing that Gly, Glu, Arg, Asp, His, and Thr are more conserved than other amino acids. Also in the cases in which the same ligand is bound to unrelated proteins, the binding pockets showed significant conservation in the residue types. In this case, the probability of co-occurrence of the same amino acid type in the binding pockets could be up to thirteen times higher than that expected on a random basis. The trends identified in this study may provide an useful guideline in the process of drug design and lead optimization. Copyright © 2014 John Wiley & Sons, Ltd.

Energetics of the protein-DNA-water interaction

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Marabotti Anna

2007-01-01

Full Text Available Abstract Background To understand the energetics of the interaction between protein and DNA we analyzed 39 crystallographically characterized complexes with the HINT (Hydropathic INTeractions computational model. HINT is an empirical free energy force field based on solvent partitioning of small molecules between water and 1-octanol. Our previous studies on protein-ligand complexes demonstrated that free energy predictions were significantly improved by taking into account the energetic contribution of water molecules that form at least one hydrogen bond with each interacting species. Results An initial correlation between the calculated HINT scores and the experimentally determined binding free energies in the protein-DNA system exhibited a relatively poor r2 of 0.21 and standard error of ± 1.71 kcal mol-1. However, the inclusion of 261 waters that bridge protein and DNA improved the HINT score-free energy correlation to an r2 of 0.56 and standard error of ± 1.28 kcal mol-1. Analysis of the water role and energy contributions indicate that 46% of the bridging waters act as linkers between amino acids and nucleotide bases at the protein-DNA interface, while the remaining 54% are largely involved in screening unfavorable electrostatic contacts. Conclusion This study quantifies the key energetic role of bridging waters in protein-DNA associations. In addition, the relevant role of hydrophobic interactions and entropy in driving protein-DNA association is indicated by analyses of interaction character showing that, together, the favorable polar and unfavorable polar/hydrophobic-polar interactions (i.e., desolvation mostly cancel.

Functional analysis of the Hikeshi-like protein and its interaction with HSP70 in Arabidopsis

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Koizumi, Shinya; Ohama, Naohiko; Mizoi, Junya [Laboratory of Plant Molecular Physiology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657 (Japan); Shinozaki, Kazuo [RIKEN Plant Science Center, 1-7-22 Suehiro-cho, Tsurumi, Yokohama, Kanagawa 230-0045 (Japan); Yamaguchi-Shinozaki, Kazuko, E-mail: akys@mail.ecc.u-tokyo.ac.jp [Laboratory of Plant Molecular Physiology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657 (Japan)

2014-07-18

Highlights: • HKL, a Hikeshi homologous gene is identified in Arabidopsis. • HKL interacts with two HSP70 isoforms and regulates the subcellular localization of HSC70-1. • The two HSP70 translocate into nucleus in response to heat stress. • Overexpression of HKL confers thermotolerance in transgenic plants. - Abstract: Heat shock proteins (HSPs) refold damaged proteins and are an essential component of the heat shock response. Previously, the 70 kDa heat shock protein (HSP70) has been reported to translocate into the nucleus in a heat-dependent manner in many organisms. In humans, the heat-induced translocation of HSP70 requires the nuclear carrier protein Hikeshi. In the Arabidopsis genome, only one gene encodes a protein with high homology to Hikeshi, and we named this homolog Hikeshi-like (HKL) protein. In this study, we show that two Arabidopsis HSP70 isoforms accumulate in the nucleus in response to heat shock and that HKL interacts with these HSP70s. Our histochemical analysis revealed that HKL is predominantly expressed in meristematic tissues, suggesting the potential importance of HKL during cell division in Arabidopsis. In addition, we show that HKL regulates HSP70 localization, and HKL overexpression conferred thermotolerance to transgenic Arabidopsis plants. Our results suggest that HKL plays a positive role in the thermotolerance of Arabidopsis plants and cooperatively interacts with HSP70.

Can infrared spectroscopy provide information on protein-protein interactions?

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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.

Analysis of Nanobody-Epitope Interactions in Living Cells via Quantitative Protein Transport Assays.

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Früholz, Simone; Pimpl, Peter

2017-01-01

Over the past few decades, quantitative protein transport analyses have been used to elucidate the sorting and transport of proteins in the endomembrane system of plants. Here, we have applied our knowledge about transport routes and the corresponding sorting signals to establish an in vivo system for testing specific interactions between soluble proteins.Here, we describe the use of quantitative protein transport assays in tobacco mesophyll protoplasts to test for interactions occurring between a GFP-binding nanobody and its GFP epitope. For this, we use a secreted GFP-tagged α-amylase as a reporter together with a vacuolar-targeted RFP-tagged nanobody. The interaction between these proteins is then revealed by a transport alteration of the secretory reporter due to the interaction-triggered attachment of the vacuolar sorting signal.

MPact: the MIPS protein interaction resource on yeast.

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Güldener, Ulrich; Münsterkötter, Martin; Oesterheld, Matthias; Pagel, Philipp; Ruepp, Andreas; Mewes, Hans-Werner; Stümpflen, Volker

2006-01-01

In recent years, the Munich Information Center for Protein Sequences (MIPS) yeast protein-protein interaction (PPI) dataset has been used in numerous analyses of protein networks and has been called a gold standard because of its quality and comprehensiveness [H. Yu, N. M. Luscombe, H. X. Lu, X. Zhu, Y. Xia, J. D. Han, N. Bertin, S. Chung, M. Vidal and M. Gerstein (2004) Genome Res., 14, 1107-1118]. MPact and the yeast protein localization catalog provide information related to the proximity of proteins in yeast. Beside the integration of high-throughput data, information about experimental evidence for PPIs in the literature was compiled by experts adding up to 4300 distinct PPIs connecting 1500 proteins in yeast. As the interaction data is a complementary part of CYGD, interactive mapping of data on other integrated data types such as the functional classification catalog [A. Ruepp, A. Zollner, D. Maier, K. Albermann, J. Hani, M. Mokrejs, I. Tetko, U. Güldener, G. Mannhaupt, M. Münsterkötter and H. W. Mewes (2004) Nucleic Acids Res., 32, 5539-5545] is possible. A survey of signaling proteins and comparison with pathway data from KEGG demonstrates that based on these manually annotated data only an extensive overview of the complexity of this functional network can be obtained in yeast. The implementation of a web-based PPI-analysis tool allows analysis and visualization of protein interaction networks and facilitates integration of our curated data with high-throughput datasets. The complete dataset as well as user-defined sub-networks can be retrieved easily in the standardized PSI-MI format. The resource can be accessed through http://mips.gsf.de/genre/proj/mpact.

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

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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.

Exploration of the dynamic properties of protein complexes predicted from spatially constrained protein-protein interaction networks.

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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

Interaction proteomics analysis of polycomb proteins defines distinct PRC1 complexes in mammalian cells

DEFF Research Database (Denmark)

Vandamme, Julien; Völkel, Pamela; Rosnoblet, Claire

2011-01-01

Polycomb group (PcG) proteins maintain transcriptional repression of hundreds of genes involved in development, signaling or cancer using chromatin-based epigenetic mechanisms. Biochemical studies in Drosophila have revealed that PcG proteins associate in at least two classes of protein complexes...... known as Polycomb repressive complexes 1 and 2 (PRC1 and PRC2). Drosophila core PRC1 is composed of four subunits, Polycomb (Pc), Sex combs extra (Sce), Polyhomeotic (Ph), and Posterior sex combs (Psc). Each of these proteins has multiple orthologs in vertebrates classified respectively as the CBX, RING...... in order to identify interacting partners of CBX family proteins under the same experimental conditions. Our analysis identified with high confidence about 20 proteins co-eluted with CBX2 and CBX7 tagged proteins, about 40 with CBX4, and around 60 with CBX6 and CBX8. We provide evidences that the CBX...

Comprehensive analysis of LANA interacting proteins essential for viral genome tethering and persistence.

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Subhash C Verma

Full Text Available Kaposi's sarcoma associated herpesvirus is tightly linked to multiple human malignancies including Kaposi's sarcoma (KS, Primary Effusion Lymphoma (PEL and Multicentric Castleman's Disease (MCD. KSHV like other herpesviruses establishes life-long latency in the infected host by persisting as chromatin and tethering to host chromatin through the virally encoded protein Latency Associated Nuclear Antigen (LANA. LANA, a multifunctional protein, is capable of binding to a large number of cellular proteins responsible for transcriptional regulation of various cellular and viral pathways involved in blocking cell death and promoting cell proliferation. This leads to enhanced cell division and replication of the viral genome, which segregates faithfully in the dividing tumor cells. The mechanism of genome segregation is well known and the binding of LANA to nucleosomal proteins, throughout the cell cycle, suggests that these interactions play an important role in efficient segregation. Various biochemical methods have identified a large number of LANA binding proteins, including histone H2A/H2B, histone H1, MeCP2, DEK, CENP-F, NuMA, Bub1, HP-1, and Brd4. These nucleosomal proteins may have various functions in tethering of the viral genome during specific phases of the viral life cycle. Therefore, we performed a comprehensive analysis of their interaction with LANA using a number of different assays. We show that LANA binds to core nucleosomal histones and also associates with other host chromatin proteins including histone H1 and high mobility group proteins (HMGs. We used various biochemical assays including co-immunoprecipitation and in-vivo localization by split GFP and fluorescence resonance energy transfer (FRET to demonstrate their association.

Selection of peptides interfering with protein-protein interaction.

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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.

Visualization of protein interaction networks: problems and solutions

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Agapito Giuseppe

2013-01-01

Full Text Available Abstract Background Visualization concerns the representation of data visually and is an important task in scientific research. Protein-protein interactions (PPI are discovered using either wet lab techniques, such mass spectrometry, or in silico predictions tools, resulting in large collections of interactions stored in specialized databases. The set of all interactions of an organism forms a protein-protein interaction network (PIN and is an important tool for studying the behaviour of the cell machinery. Since graphic representation of PINs may highlight important substructures, e.g. protein complexes, visualization is more and more used to study the underlying graph structure of PINs. Although graphs are well known data structures, there are different open problems regarding PINs visualization: the high number of nodes and connections, the heterogeneity of nodes (proteins and edges (interactions, the possibility to annotate proteins and interactions with biological information extracted by ontologies (e.g. Gene Ontology that enriches the PINs with semantic information, but complicates their visualization. Methods In these last years many software tools for the visualization of PINs have been developed. Initially thought for visualization only, some of them have been successively enriched with new functions for PPI data management and PIN analysis. The paper analyzes the main software tools for PINs visualization considering four main criteria: (i technology, i.e. availability/license of the software and supported OS (Operating System platforms; (ii interoperability, i.e. ability to import/export networks in various formats, ability to export data in a graphic format, extensibility of the system, e.g. through plug-ins; (iii visualization, i.e. supported layout and rendering algorithms and availability of parallel implementation; (iv analysis, i.e. availability of network analysis functions, such as clustering or mining of the graph, and the

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.

Generating functional analysis of complex formation and dissociation in large protein interaction networks

International Nuclear Information System (INIS)

Coolen, A C C; Rabello, S

2009-01-01

We analyze large systems of interacting proteins, using techniques from the non-equilibrium statistical mechanics of disordered many-particle systems. Apart from protein production and removal, the most relevant microscopic processes in the proteome are complex formation and dissociation, and the microscopic degrees of freedom are the evolving concentrations of unbound proteins (in multiple post-translational states) and of protein complexes. Here we only include dimer-complexes, for mathematical simplicity, and we draw the network that describes which proteins are reaction partners from an ensemble of random graphs with an arbitrary degree distribution. We show how generating functional analysis methods can be used successfully to derive closed equations for dynamical order parameters, representing an exact macroscopic description of the complex formation and dissociation dynamics in the infinite system limit. We end this paper with a discussion of the possible routes towards solving the nontrivial order parameter equations, either exactly (in specific limits) or approximately.

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

Yeast Interacting Proteins Database: YLR447C, YOR047C [Yeast Interacting Proteins Database

Lifescience Database Archive (English)

Full Text Available xpression; interacts with protein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding protein Sp...; interacts with protein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding protein Spt15p; act

BRCA1 interacts directly with the Fanconi anemia protein FANCA.

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Folias, Alexandra; Matkovic, Mara; Bruun, Donald; Reid, Sonja; Hejna, James; Grompe, Markus; D'Andrea, Alan; Moses, Robb

2002-10-01

Fanconi anemia (FA) is a rare autosomal recessive disease characterized by skeletal defects, anemia, chromosomal instability and increased risk of leukemia. At the cellular level FA is characterized by increased sensitivity to agents forming interstrand crosslinks (ICL) in DNA. Six FA genes have been cloned and interactions among individual FANC proteins have been found. The FANCD2 protein co-localizes in nuclear foci with the BRCA1 protein following DNA damage and during S-phase, requiring the FANCA, C, E and G proteins to do so. This finding may reflect a direct role for the BRCA1 protein in double strand break (DSB) repair and interaction with the FANC proteins. Therefore interactions between BRCA1 and the FANC proteins were investigated. Among the known FANC proteins, we find evidence for direct interaction only between the FANCA protein and BRCA1. The evidence rests on three different tests: yeast two-hybrid analysis, coimmunoprecipitation from in vitro synthesis, and coimmunoprecipitation from cell extracts. The amino terminal portion of FANCA and the central part (aa 740-1083) of BRCA1 contain the sites of interaction. The interaction does not depend on DNA damage, thus FANCA and BRCA1 are constitutively interacting. The demonstrated interaction directly connects BRCA1 to the FA pathway of DNA repair.

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

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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

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

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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.

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

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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.

Strong Ligand-Protein Interactions Derived from Diffuse Ligand Interactions with Loose Binding Sites.

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Marsh, Lorraine

2015-01-01

Many systems in biology rely on binding of ligands to target proteins in a single high-affinity conformation with a favorable ΔG. Alternatively, interactions of ligands with protein regions that allow diffuse binding, distributed over multiple sites and conformations, can exhibit favorable ΔG because of their higher entropy. Diffuse binding may be biologically important for multidrug transporters and carrier proteins. A fine-grained computational method for numerical integration of total binding ΔG arising from diffuse regional interaction of a ligand in multiple conformations using a Markov Chain Monte Carlo (MCMC) approach is presented. This method yields a metric that quantifies the influence on overall ligand affinity of ligand binding to multiple, distinct sites within a protein binding region. This metric is essentially a measure of dispersion in equilibrium ligand binding and depends on both the number of potential sites of interaction and the distribution of their individual predicted affinities. Analysis of test cases indicates that, for some ligand/protein pairs involving transporters and carrier proteins, diffuse binding contributes greatly to total affinity, whereas in other cases the influence is modest. This approach may be useful for studying situations where "nonspecific" interactions contribute to biological function.

A conserved mammalian protein interaction network.

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Åsa Pérez-Bercoff

Full Text Available Physical interactions between proteins mediate a variety of biological functions, including signal transduction, physical structuring of the cell and regulation. While extensive catalogs of such interactions are known from model organisms, their evolutionary histories are difficult to study given the lack of interaction data from phylogenetic outgroups. Using phylogenomic approaches, we infer a upper bound on the time of origin for a large set of human protein-protein interactions, showing that most such interactions appear relatively ancient, dating no later than the radiation of placental mammals. By analyzing paired alignments of orthologous and putatively interacting protein-coding genes from eight mammals, we find evidence for weak but significant co-evolution, as measured by relative selective constraint, between pairs of genes with interacting proteins. However, we find no strong evidence for shared instances of directional selection within an interacting pair. Finally, we use a network approach to show that the distribution of selective constraint across the protein interaction network is non-random, with a clear tendency for interacting proteins to share similar selective constraints. Collectively, the results suggest that, on the whole, protein interactions in mammals are under selective constraint, presumably due to their functional roles.

Detailed analysis of RNA-protein interactions within the bacterial ribosomal protein L5/5S rRNA complex.

Science.gov (United States)

Perederina, Anna; Nevskaya, Natalia; Nikonov, Oleg; Nikulin, Alexei; Dumas, Philippe; Yao, Min; Tanaka, Isao; Garber, Maria; Gongadze, George; Nikonov, Stanislav

2002-12-01

The crystal structure of ribosomal protein L5 from Thermus thermophilus complexed with a 34-nt fragment comprising helix III and loop C of Escherichia coli 5S rRNA has been determined at 2.5 A resolution. The protein specifically interacts with the bulged nucleotides at the top of loop C of 5S rRNA. The rRNA and protein contact surfaces are strongly stabilized by intramolecular interactions. Charged and polar atoms forming the network of conserved intermolecular hydrogen bonds are located in two narrow planar parallel layers belonging to the protein and rRNA, respectively. The regions, including these atoms conserved in Bacteria and Archaea, can be considered an RNA-protein recognition module. Comparison of the T. thermophilus L5 structure in the RNA-bound form with the isolated Bacillus stearothermophilus L5 structure shows that the RNA-recognition module on the protein surface does not undergo significant changes upon RNA binding. In the crystal of the complex, the protein interacts with another RNA molecule in the asymmetric unit through the beta-sheet concave surface. This protein/RNA interface simulates the interaction of L5 with 23S rRNA observed in the Haloarcula marismortui 50S ribosomal subunit.

Interactive protein manipulation

International Nuclear Information System (INIS)

2003-01-01

We describe an interactive visualization and modeling program for the creation of protein structures ''from scratch''. The input to our program is an amino acid sequence -decoded from a gene- and a sequence of predicted secondary structure types for each amino acid-provided by external structure prediction programs. Our program can be used in the set-up phase of a protein structure prediction process; the structures created with it serve as input for a subsequent global internal energy minimization, or another method of protein structure prediction. Our program supports basic visualization methods for protein structures, interactive manipulation based on inverse kinematics, and visualization guides to aid a user in creating ''good'' initial structures

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.

Interactions among tobacco sieve element occlusion (SEO) proteins.

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Jekat, Stephan B; Ernst, Antonia M; Zielonka, Sascia; Noll, Gundula A; Prüfer, Dirk

2012-12-01

Angiosperms transport their photoassimilates through sieve tubes, which comprise longitudinally-connected sieve elements. In dicots and also some monocots, the sieve elements contain parietal structural proteins known as phloem proteins or P-proteins. Following injury, P proteins disperse and accumulate as viscous plugs at the sieve plates to prevent the loss of valuable transport sugars. Tobacco (Nicotiana tabacum) P-proteins are multimeric complexes comprising subunits encoded by members of the SEO (sieve element occlusion) gene family. The existence of multiple subunits suggests that P-protein assembly involves interactions between SEO proteins, but this process is largely uncharacterized and it is unclear whether the different subunits perform unique roles or are redundant. We therefore extended our analysis of the tobacco P-proteins NtSEO1 and NtSEO2 to investigate potential interactions between them, and found that both proteins can form homomeric and heteromeric complexes in planta.

PREFACE: Physics approaches to protein interactions and gene regulation Physics approaches to protein interactions and gene regulation

Science.gov (United States)

Nussinov, Ruth; Panchenko, Anna R.; Przytycka, Teresa

2011-06-01

the same time reversibility and diversity in their interactions. Interestingly, as is shown in the paper by Mészáros et al, even though some disordered regions and proteins have a tendency to fold upon binding, the structures of their complexes still reveal their inherent flexibility. Indeed, disordered proteins and their complexes have certain properties which distinguish them from proteins with well-defined structures. This is evident from the papers by Lobanov and Galzitskaya, and Mészáros et al, which show that such characteristic features of disordered proteins allow their successful computational prediction from the sequence alone. Computational prediction of protein disorder has been used in another study by Takeda et al where the authors investigate the role of disorder in the function of a specific actin capping protein. The paper presents normal mode analysis with the elastic network model to examine the mechanisms of intrinsic flexibility and its biological role in actin function. Analysis of the underlying mechanisms and key factors in protein recognition might be essential for the prediction of protein-protein interactions. The papers by Tuncbag et al and Hashimoto et al demonstrate how incorporating the physico-chemical properties of binding interfaces and their atomic details obtained from protein crystal structures might be used to increase the accuracy of predicted protein-protein interactions and provide data on relative orientations of interacting proteins and on the locations of binding sites. Moreover, analysis of protein-protein interactions might require further fine-tuning for different types of assemblies, like that shown in the example of homooligomers by Hashimoto et al. Studies of protein-protein interactions at the molecular level have contributed considerably to understanding the principles of large-scale organization of the cellular interactome. Using graph theory as a unifying language, many characteristic properties of bimolecular

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

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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.

Yeast Interacting Proteins Database: YGR013W, YKL012W [Yeast Interacting Proteins Database

Lifescience Database Archive (English)

Full Text Available tion U1 snRNP protein involved in splicing, interacts with the branchpoint-binding protein during the formation of the second commitm... PRP40 U1 snRNP protein involved in splicing, interacts with the branchpoint-binding protein during the form...ation of the second commitment complex Rows with this prey as prey (1) Rows with

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

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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.

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

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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.

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

Spectroscopic analysis on the binding interaction between tetracycline hydrochloride and bovine proteins β-casein, α-lactalbumin

Energy Technology Data Exchange (ETDEWEB)

Bi, Hongna; Tang, Lin, E-mail: tanglin@sdnu.edu.cn; Gao, Xin; Jia, Jingjing; Lv, Henghui

2016-10-15

We investigated the binding interaction between tetracycline hydrochloride (TCH) and bovine proteins β-casein (β-CN), α-lactalbumin (α-LA) in aqueous solution by multi-spectroscopic methods and molecular modeling techniques. Fluorescence and time-resolved fluorescence showed that TCH effectively quenched the intrinsic fluorescence of bovine proteins via static quenching, while there was a single class of binding site on protein. Thermodynamic parameters revealed that electrostatic forces played major roles in the interaction between β-CN and TCH, whereas α-LA-TCH complex were stabilized by hydrogen bonds and van der Waals forces. Moreover, circular dichroism spectra (CD spectra), ultraviolet visible absorption spectra (UV–vis absorption spectra), and fluorescence Excitation-Emission Matrix (EEM) spectra results indicated the secondary structure of bovine proteins was changed in the presence of TCH with the α-helix percentage of protein-TCH complexes decreased. Molecular modeling analysis supported the experimental results well. In addition, the research of surface hydrophobicity further verified tertiary structure of proteins was changed in the presence of TCH and the possible changes of protein function. These results achieved from experiments may be valuable in the milk industry and food safety.

Spectroscopic analysis on the binding interaction between tetracycline hydrochloride and bovine proteins β-casein, α-lactalbumin

International Nuclear Information System (INIS)

Bi, Hongna; Tang, Lin; Gao, Xin; Jia, Jingjing; Lv, Henghui

2016-01-01

We investigated the binding interaction between tetracycline hydrochloride (TCH) and bovine proteins β-casein (β-CN), α-lactalbumin (α-LA) in aqueous solution by multi-spectroscopic methods and molecular modeling techniques. Fluorescence and time-resolved fluorescence showed that TCH effectively quenched the intrinsic fluorescence of bovine proteins via static quenching, while there was a single class of binding site on protein. Thermodynamic parameters revealed that electrostatic forces played major roles in the interaction between β-CN and TCH, whereas α-LA-TCH complex were stabilized by hydrogen bonds and van der Waals forces. Moreover, circular dichroism spectra (CD spectra), ultraviolet visible absorption spectra (UV–vis absorption spectra), and fluorescence Excitation-Emission Matrix (EEM) spectra results indicated the secondary structure of bovine proteins was changed in the presence of TCH with the α-helix percentage of protein-TCH complexes decreased. Molecular modeling analysis supported the experimental results well. In addition, the research of surface hydrophobicity further verified tertiary structure of proteins was changed in the presence of TCH and the possible changes of protein function. These results achieved from experiments may be valuable in the milk industry and food safety.

Interactive protein manipulation

Energy Technology Data Exchange (ETDEWEB)

SNCrivelli@lbl.gov

2003-07-01

We describe an interactive visualization and modeling program for the creation of protein structures ''from scratch''. The input to our program is an amino acid sequence -decoded from a gene- and a sequence of predicted secondary structure types for each amino acid-provided by external structure prediction programs. Our program can be used in the set-up phase of a protein structure prediction process; the structures created with it serve as input for a subsequent global internal energy minimization, or another method of protein structure prediction. Our program supports basic visualization methods for protein structures, interactive manipulation based on inverse kinematics, and visualization guides to aid a user in creating ''good'' initial structures.

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.

[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.

Yeast Interacting Proteins Database: YGL237C, YOR047C [Yeast Interacting Proteins Database

Lifescience Database Archive (English)

Full Text Available ene expression; interacts with protein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding prote... expression; interacts with protein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding protein

Yeast Interacting Proteins Database: YKL002W, YOR047C [Yeast Interacting Proteins Database

Lifescience Database Archive (English)

Full Text Available ene expression; interacts with protein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding prote...xpression; interacts with protein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding protein Sp

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.

Semantic integration to identify overlapping functional modules in protein interaction networks

Directory of Open Access Journals (Sweden)

Ramanathan Murali

2007-07-01

Full Text Available Abstract Background The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms. Results We have developed novel metrics, called semantic similarity and semantic interactivity, which use Gene Ontology (GO annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. We presented a flow-based modularization algorithm to efficiently identify overlapping modules in the weighted interaction networks. The experimental results show that the semantic similarity and semantic interactivity of interacting pairs were positively correlated with functional co-occurrence. The effectiveness of the algorithm for identifying modules was evaluated using functional categories from the MIPS database. We demonstrated that our algorithm had higher accuracy compared to other competing approaches. Conclusion The integration of protein interaction networks with GO annotation data and the capability of detecting overlapping modules substantially improve the accuracy of module identification.

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...

A sampling framework for incorporating quantitative mass spectrometry data in protein interaction analysis.

Science.gov (United States)

Tucker, George; Loh, Po-Ru; Berger, Bonnie

2013-10-04

Comprehensive protein-protein interaction (PPI) maps are a powerful resource for uncovering the molecular basis of genetic interactions and providing mechanistic insights. Over the past decade, high-throughput experimental techniques have been developed to generate PPI maps at proteome scale, first using yeast two-hybrid approaches and more recently via affinity purification combined with mass spectrometry (AP-MS). Unfortunately, data from both protocols are prone to both high false positive and false negative rates. To address these issues, many methods have been developed to post-process raw PPI data. However, with few exceptions, these methods only analyze binary experimental data (in which each potential interaction tested is deemed either observed or unobserved), neglecting quantitative information available from AP-MS such as spectral counts. We propose a novel method for incorporating quantitative information from AP-MS data into existing PPI inference methods that analyze binary interaction data. Our approach introduces a probabilistic framework that models the statistical noise inherent in observations of co-purifications. Using a sampling-based approach, we model the uncertainty of interactions with low spectral counts by generating an ensemble of possible alternative experimental outcomes. We then apply the existing method of choice to each alternative outcome and aggregate results over the ensemble. We validate our approach on three recent AP-MS data sets and demonstrate performance comparable to or better than state-of-the-art methods. Additionally, we provide an in-depth discussion comparing the theoretical bases of existing approaches and identify common aspects that may be key to their performance. Our sampling framework extends the existing body of work on PPI analysis using binary interaction data to apply to the richer quantitative data now commonly available through AP-MS assays. This framework is quite general, and many enhancements are likely

Prediction of protein–protein interactions: unifying evolution and structure at protein interfaces

International Nuclear Information System (INIS)

Tuncbag, Nurcan; Gursoy, Attila; Keskin, Ozlem

2011-01-01

The vast majority of the chores in the living cell involve protein–protein interactions. Providing details of protein interactions at the residue level and incorporating them into protein interaction networks are crucial toward the elucidation of a dynamic picture of cells. Despite the rapid increase in the number of structurally known protein complexes, we are still far away from a complete network. Given experimental limitations, computational modeling of protein interactions is a prerequisite to proceed on the way to complete structural networks. In this work, we focus on the question 'how do proteins interact?' rather than 'which proteins interact?' and we review structure-based protein–protein interaction prediction approaches. As a sample approach for modeling protein interactions, PRISM is detailed which combines structural similarity and evolutionary conservation in protein interfaces to infer structures of complexes in the protein interaction network. This will ultimately help us to understand the role of protein interfaces in predicting bound conformations

A Global Protein Kinase and Phosphatase Interaction Network in Yeast

Science.gov (United States)

Breitkreutz, Ashton; Choi, Hyungwon; Sharom, Jeffrey R.; Boucher, Lorrie; Neduva, Victor; Larsen, Brett; Lin, Zhen-Yuan; Breitkreutz, Bobby-Joe; Stark, Chris; Liu, Guomin; Ahn, Jessica; Dewar-Darch, Danielle; Reguly, Teresa; Tang, Xiaojing; Almeida, Ricardo; Qin, Zhaohui Steve; Pawson, Tony; Gingras, Anne-Claude; Nesvizhskii, Alexey I.; Tyers, Mike

2011-01-01

The interactions of protein kinases and phosphatases with their regulatory subunits and substrates underpin cellular regulation. We identified a kinase and phosphatase interaction (KPI) network of 1844 interactions in budding yeast by mass spectrometric analysis of protein complexes. The KPI network contained many dense local regions of interactions that suggested new functions. Notably, the cell cycle phosphatase Cdc14 associated with multiple kinases that revealed roles for Cdc14 in mitogen-activated protein kinase signaling, the DNA damage response, and metabolism, whereas interactions of the target of rapamycin complex 1 (TORC1) uncovered new effector kinases in nitrogen and carbon metabolism. An extensive backbone of kinase-kinase interactions cross-connects the proteome and may serve to coordinate diverse cellular responses. PMID:20489023

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.

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....

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

Yeast Interacting Proteins Database: YPR103W, YOR047C [Yeast Interacting Proteins Database

Lifescience Database Archive (English)

Full Text Available tein involved in control of glucose-regulated gene expression; interacts with protein kinase Snf1p, glucose sensors...gulated gene expression; interacts with protein kinase Snf1p, glucose sensors Snf

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

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

Applications of hydrophilic interaction chromatography to amino acids, peptides, and proteins.

Science.gov (United States)

Periat, Aurélie; Krull, Ira S; Guillarme, Davy

2015-02-01

This review summarizes the recent advances in the analysis of amino acids, peptides, and proteins using hydrophilic interaction chromatography. Various reports demonstrate the successful analysis of amino acids under such conditions. However, a baseline resolution of the 20 natural amino acids has not yet been published and for this reason, there is often a need to use mass spectrometry for detection to further improve selectivity. Hydrophilic interaction chromatography is also recognized as a powerful technique for peptide analysis, and there are a lot of papers showing its applicability for proteomic applications (peptide mapping). It is expected that its use for peptide mapping will continue to grow in the future, particularly because this analytical strategy can be combined with reversed-phase liquid chromatography, in a two-dimensional setup, to reach very high resolving power. Finally, the interest in hydrophilic interaction chromatography for intact proteins analysis is less evident due to possible solubility issues and a lack of suitable hydrophilic interaction chromatography stationary phases. To date, it has been successfully employed only for the characterization of membrane proteins, histones, and the separation of glycosylated isoforms of an intact glycoprotein. From our point of view, the number of hydrophilic interaction chromatography columns compatible with intact proteins (higher upper temperature limit, large pore size, etc.) is still too limited. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Structural and energetic study of cation-π-cation interactions in proteins.

Science.gov (United States)

Pinheiro, Silvana; Soteras, Ignacio; Gelpí, Josep Lluis; Dehez, François; Chipot, Christophe; Luque, F Javier; Curutchet, Carles

2017-04-12

Cation-π interactions of aromatic rings and positively charged groups are among the most important interactions in structural biology. The role and energetic characteristics of these interactions are well established. However, the occurrence of cation-π-cation interactions is an unexpected motif, which raises intriguing questions about its functional role in proteins. We present a statistical analysis of the occurrence, composition and geometrical preferences of cation-π-cation interactions identified in a set of non-redundant protein structures taken from the Protein Data Bank. Our results demonstrate that this structural motif is observed at a small, albeit non-negligible frequency in proteins, and suggest a preference to establish cation-π-cation motifs with Trp, followed by Tyr and Phe. Furthermore, we have found that cation-π-cation interactions tend to be highly conserved, which supports their structural or functional role. Finally, we have performed an energetic analysis of a representative subset of cation-π-cation complexes combining quantum-chemical and continuum solvation calculations. Our results point out that the protein environment can strongly screen the cation-cation repulsion, leading to an attractive interaction in 64% of the complexes analyzed. Together with the high degree of conservation observed, these results suggest a potential stabilizing role in the protein fold, as demonstrated recently for a miniature protein (Craven et al., J. Am. Chem. Soc. 2016, 138, 1543). From a computational point of view, the significant contribution of non-additive three-body terms challenges the suitability of standard additive force fields for describing cation-π-cation motifs in molecular simulations.

Detection of Protein Interactions in T3S Systems Using Yeast Two-Hybrid Analysis.

Science.gov (United States)

Nilles, Matthew L

2017-01-01

Two-hybrid systems, sometimes termed interaction traps, are genetic systems designed to find and analyze interactions between proteins. The most common systems are yeast based (commonly Saccharomyces cerevisae) and rely on the functional reconstitution of the GAL4 transcriptional activator. Reporter genes, such as the lacZ gene of Escherichia coli (encodes β-galactosidase), are placed under GAL4-dependent transcriptional control to provide quick and reliable detection of protein interactions. In this method the use of a yeast-based two-hybrid system is described to study protein interactions between components of type III secretion systems.

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.

Interaction of a non-histone chromatin protein (high-mobility group protein 2) with DNA

International Nuclear Information System (INIS)

Goodwin, G.H.; Shooter, K.V.; Johns, E.W.

1975-01-01

The interaction with DNA of the calf thymus chromatin non-histone protein termed the high-mobility group protein 2 has been studied by sedimentation analysis in the ultracentrifuge and by measuring the binding of the 125 I-labelled protein to DNA. The results have been compared with those obtained previously by us [Eur. J. Biochem. (1974) 47, 263-270] for the interaction of high-mobility group protein 1 with DNA. Although the binding parameters are similar for these two proteins, high-mobility group protein 2 differs from high-mobility group protein 1 in that the former appears to change the shape of the DNA to a more compact form. The molecular weight of high-mobility group protein 2 has been determined by equilibrium sedimentation and a mean value of 26,000 was obtained. A low level of nuclease activity detected in one preparation of high-mobility group protein 2 has been investigated. (orig.) [de

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.

Yeast Interacting Proteins Database: YMR280C, YOR047C [Yeast Interacting Proteins Database

Lifescience Database Archive (English)

Full Text Available olved in control of glucose-regulated gene expression; interacts with protein kinase Snf1p, glucose sensor... glucose-regulated gene expression; interacts with protein kinase Snf1p, glucose sensors Snf3p and Rgt2p, an

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...

A membrane protein / signaling protein interaction network for Arabidopsis version AMPv2

Directory of Open Access Journals (Sweden)

Sylvie Lalonde

2010-09-01

Full Text Available Interactions between membrane proteins and the soluble fraction are essential for signal transduction and for regulating nutrient transport. To gain insights into the membrane-based interactome, 3,852 open reading frames (ORFs out of a target list of 8,383 representing membrane and signaling proteins from Arabidopsis thaliana were cloned into a Gateway compatible vector. The mating-based split-ubiquitin system was used to screen for potential protein-protein interactions (pPPIs among 490 Arabidopsis ORFs. A binary robotic screen between 142 receptor-like kinases, 72 transporters, 57 soluble protein kinases and phosphatases, 40 glycosyltransferases, 95 proteins of various functions and 89 proteins with unknown function detected 387 out of 90,370 possible PPIs. A secondary screen confirmed 343 (of 387 pPPIs between 179 proteins, yielding a scale-free network (r2=0.863. Eighty of 142 transmembrane receptor-like kinases (RLK tested positive, identifying three homomers, 63 heteromers and 80 pPPIs with other proteins. Thirty-one out of 142 RLK interactors (including RLKs had previously been found to be phosphorylated; thus interactors may be substrates for respective RLKs. None of the pPPIs described here had been reported in the major interactome databases, including potential interactors of G protein-coupled receptors, phospholipase C, and AMT ammonium transporters. Two RLKs found as putative interactors of AMT1;1 were independently confirmed using a split luciferase assay in Arabidopsis protoplasts. These RLKs may be involved in ammonium-dependent phosphorylation of the C-terminus and regulation of ammonium uptake activity. The robotic screening method established here will enable a systematic analysis of membrane protein interactions in fungi, plants and metazoa.

Drosophila protein interaction map (DPiM): a paradigm for metazoan protein complex interactions.

Science.gov (United States)

Guruharsha, K G; Obar, Robert A; Mintseris, Julian; Aishwarya, K; Krishnan, R T; Vijayraghavan, K; Artavanis-Tsakonas, Spyros

2012-01-01

Proteins perform essential cellular functions as part of protein complexes, often in conjunction with RNA, DNA, metabolites and other small molecules. The genome encodes thousands of proteins but not all of them are expressed in every cell type; and expressed proteins are not active at all times. Such diversity of protein expression and function accounts for the level of biological intricacy seen in nature. Defining protein-protein interactions in protein complexes, and establishing the when, what and where of potential interactions, is therefore crucial to understanding the cellular function of any protein-especially those that have not been well studied by traditional molecular genetic approaches. We generated a large-scale resource of affinity-tagged expression-ready clones and used co-affinity purification combined with tandem mass-spectrometry to identify protein partners of nearly 5,000 Drosophila melanogaster proteins. The resulting protein complex "map" provided a blueprint of metazoan protein complex organization. Here we describe how the map has provided valuable insights into protein function in addition to generating hundreds of testable hypotheses. We also discuss recent technological advancements that will be critical in addressing the next generation of questions arising from the map.

[Identification and analysis of the proteins interacted with Prestin in cochlear outer hair cells of guinea pig].

Science.gov (United States)

Luo, X; Wang, J Y; Zhang, F L; Xia, Y

2018-01-07

Objective: To explore the regulation and mechanism of Prestin protein by identifying the proteins interacted with Prestin in cochlear outer hair cell(OHC) and analyzing their biological function. Methods: Co-immunoprecipitation combined mass spectrometry technology was used to isolate and identify the proteins interacted with Prestin protein of OHC, bioinformatics was used to construct Prestin protein interaction network. The proteins interacted with Prestin in OHC of guinea pig were determined by matching primary interaction mass spectrometry with protein interaction network, and annotated their functions. Results: The results of co-immunoprecipitation combined with mass spectrometry showed that 116 kinds of credible proteins could interact with Prestin. By constructing Prestin protein interaction network, matching the results of mass spectrometry and analyzing of sub-cellular localization, eight kinds of proteins were confirmed that they interacted with Prestin directly, namely EEF2, HSP90AB1, FN1, FLNA, EEF1A1, HSP90B1, ATP5A1, and ERH, respectively, which were mainly involved in the synthesis and transportation, transmembrane folding and localization, structural stability and signal transduction of Prestin protein. Conclusion: EEF2, HSP90AB1, FN1, FLNA, EEF1A1, HSP90B1, ATP5A1 and ERH provide molecular basis for sensory amplification function of OHCs by participating in biotransformation, transmembrane folding and localization, signal transduction and other biological processes of Prestin protein.

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 .

A protein interaction map of the kalimantacin biosynthesis assembly line

Directory of Open Access Journals (Sweden)

Birgit Uytterhoeven

2016-11-01

Full Text Available The antimicrobial secondary metabolite kalimantacin is produced by a hybrid polyketide/ non-ribosomal peptide system in Pseudomonas fluorescens BCCM_ID9359. In this study, the kalimantacin biosynthesis gene cluster is analyzed by yeast two-hybrid analysis, creating a protein-protein interaction map of the entire assembly line. In total, 28 potential interactions were identified, of which 13 could be confirmed further. These interactions include the dimerization of ketosynthase domains, a link between assembly line modules 9 and 10, and a specific interaction between the trans-acting enoyl reductase BatK and the carrier proteins of modules 8 and 10. These interactions reveal fundamental insight into the biosynthesis of secondary metabolites.This study is the first to reveal interactions in a complete biosynthetic pathway. Similar future studies could build a strong basis for engineering strategies in such clusters.

Detailed analysis of RNA-protein interactions within the bacterial ribosomal protein L5/5S rRNA complex.

OpenAIRE

Perederina, Anna; Nevskaya, Natalia; Nikonov, Oleg; Nikulin, Alexei; Dumas, Philippe; Yao, Min; Tanaka, Isao; Garber, Maria; Gongadze, George; Nikonov, Stanislav

2002-01-01

The crystal structure of ribosomal protein L5 from Thermus thermophilus complexed with a 34-nt fragment comprising helix III and loop C of Escherichia coli 5S rRNA has been determined at 2.5 A resolution. The protein specifically interacts with the bulged nucleotides at the top of loop C of 5S rRNA. The rRNA and protein contact surfaces are strongly stabilized by intramolecular interactions. Charged and polar atoms forming the network of conserved intermolecular hydrogen bonds are located in ...

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

Proteomic analysis of JAZ interacting proteins under methyl jasmonate treatment in finger millet.

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Sen, Saswati; Kundu, Sangeeta; Dutta, Samir Kr

2016-11-01

Jasmonic acid (JA) signaling pathway in plants is activated against various developmental processes as well as biotic and abiotic stresses. The Jasmonate ZIM-domain (JAZ) protein family, the key regulator of plant JA signaling pathway, also participates in phytohormone crosstalk. This is the first study revealing the in vivo interactions of finger millet (Eleusine coracana (L.) Gaertn.) JAZ protein (EcJAZ) under methyl jasmonate (MJ) treatment. The aim of the study was to explore not only the JA signaling pathway but also the phytohormone signaling crosstalk of finger millet, a highly important future crop. From the MJ-treated finger millet seedlings, the EcJAZ interacting proteins were purified by affinity chromatography with the EcJAZ-matrix. Twenty-one proteins of varying functionalities were successfully identified by MALDI-TOF-TOF Mass spectrometry. Apart from the previously identified JAZ binding proteins, most prominently, EcJAZ was found to interact with transcription factors like NAC, GATA and also with Cold responsive protein (COR), etc. that might have extended the range of functionalities of JAZ proteins. Moreover, to evaluate the interactions of EcJAZ in the JA-co-receptor complex, we generated ten in-silico models containing the EcJAZ degron and the COI1-SKP1 of five monocot cereals viz., rice, wheat, maize, Sorghum and Setaria with JA-Ile or coronatine. Our results indicated that the EcJAZ protein of finger millet could act as the signaling hub for the JA and other phytohormone signaling pathways, in response to a diverse set of stressors and developmental cues to provide survival fitness to the plant. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Kinase Substrate Sensor (KISS), a mammalian in situ protein interaction sensor.

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Lievens, Sam; Gerlo, Sarah; Lemmens, Irma; De Clercq, Dries J H; Risseeuw, Martijn D P; Vanderroost, Nele; De Smet, Anne-Sophie; Ruyssinck, Elien; Chevet, Eric; Van Calenbergh, Serge; Tavernier, Jan

2014-12-01

Probably every cellular process is governed by protein-protein interaction (PPIs), which are often highly dynamic in nature being modulated by in- or external stimuli. Here we present KISS, for KInase Substrate Sensor, a mammalian two-hybrid approach designed to map intracellular PPIs and some of the dynamic features they exhibit. Benchmarking experiments indicate that in terms of sensitivity and specificity KISS is on par with other binary protein interaction technologies while being complementary with regard to the subset of PPIs it is able to detect. We used KISS to evaluate interactions between different types of proteins, including transmembrane proteins, expressed at their native subcellular location. In situ analysis of endoplasmic reticulum stress-induced clustering of the endoplasmic reticulum stress sensor ERN1 and ligand-dependent β-arrestin recruitment to GPCRs illustrated the method's potential to study functional PPI modulation in complex cellular processes. Exploring its use as a tool for in cell evaluation of pharmacological interference with PPIs, we showed that reported effects of known GPCR antagonists and PPI inhibitors are properly recapitulated. In a three-hybrid setup, KISS was able to map interactions between small molecules and proteins. Taken together, we established KISS as a sensitive approach for in situ analysis of protein interactions and their modulation in a changing cellular context or in response to pharmacological challenges. © 2014 by The American Society for Biochemistry and Molecular Biology, Inc.

Analysis of protein stability and ligand interactions by thermal shift assay.

Science.gov (United States)

Huynh, Kathy; Partch, Carrie L

2015-02-02

Purification of recombinant proteins for biochemical assays and structural studies is time-consuming and presents inherent difficulties that depend on the optimization of protein stability. The use of dyes to monitor thermal denaturation of proteins with sensitive fluorescence detection enables rapid and inexpensive determination of protein stability using real-time PCR instruments. By screening a wide range of solution conditions and additives in a 96-well format, the thermal shift assay easily identifies conditions that significantly enhance the stability of recombinant proteins. The same approach can be used as an initial low-cost screen to discover new protein-ligand interactions by capitalizing on increases in protein stability that typically occur upon ligand binding. This unit presents a methodological workflow for small-scale, high-throughput thermal denaturation of recombinant proteins in the presence of SYPRO Orange dye. Copyright © 2015 John Wiley & Sons, Inc.

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.

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

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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.

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.

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.

Protein structure similarity from principle component correlation analysis

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Chou James

2006-01-01

Full Text Available Abstract Background Owing to rapid expansion of protein structure databases in recent years, methods of structure comparison are becoming increasingly effective and important in revealing novel information on functional properties of proteins and their roles in the grand scheme of evolutionary biology. Currently, the structural similarity between two proteins is measured by the root-mean-square-deviation (RMSD in their best-superimposed atomic coordinates. RMSD is the golden rule of measuring structural similarity when the structures are nearly identical; it, however, fails to detect the higher order topological similarities in proteins evolved into different shapes. We propose new algorithms for extracting geometrical invariants of proteins that can be effectively used to identify homologous protein structures or topologies in order to quantify both close and remote structural similarities. Results We measure structural similarity between proteins by correlating the principle components of their secondary structure interaction matrix. In our approach, the Principle Component Correlation (PCC analysis, a symmetric interaction matrix for a protein structure is constructed with relationship parameters between secondary elements that can take the form of distance, orientation, or other relevant structural invariants. When using a distance-based construction in the presence or absence of encoded N to C terminal sense, there are strong correlations between the principle components of interaction matrices of structurally or topologically similar proteins. Conclusion The PCC method is extensively tested for protein structures that belong to the same topological class but are significantly different by RMSD measure. The PCC analysis can also differentiate proteins having similar shapes but different topological arrangements. Additionally, we demonstrate that when using two independently defined interaction matrices, comparison of their maximum

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...

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.

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.

Scale-free behaviour of amino acid pair interactions in folded proteins

DEFF Research Database (Denmark)

Petersen, Steffen B.; Neves-Petersen, Maria Teresa; Mortensen, Rasmus J.

2012-01-01

The protein structure is a cumulative result of interactions between amino acid residues interacting with each other through space and/or chemical bonds. Despite the large number of high resolution protein structures, the ‘‘protein structure code’’ has not been fully identified. Our manuscript...... presents a novel approach to protein structure analysis in order to identify rules for spatial packing of amino acid pairs in proteins. We have investigated 8706 high resolution non-redundant protein chains and quantified amino acid pair interactions in terms of solvent accessibility, spatial and sequence...... which amino acid paired residues contributed to the cells with a population above 50, pairs of Ala, Ile, Leu and Val dominate the results. This result is statistically highly significant. We postulate that such pairs form ‘‘structural stability points’’ in the protein structure. Our data shows...

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

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.

Protein multi-scale organization through graph partitioning and robustness analysis: application to the myosin–myosin light chain interaction

International Nuclear Information System (INIS)

Delmotte, A; Barahona, M; Tate, E W; Yaliraki, S N

2011-01-01

Despite the recognized importance of the multi-scale spatio-temporal organization of proteins, most computational tools can only access a limited spectrum of time and spatial scales, thereby ignoring the effects on protein behavior of the intricate coupling between the different scales. Starting from a physico-chemical atomistic network of interactions that encodes the structure of the protein, we introduce a methodology based on multi-scale graph partitioning that can uncover partitions and levels of organization of proteins that span the whole range of scales, revealing biological features occurring at different levels of organization and tracking their effect across scales. Additionally, we introduce a measure of robustness to quantify the relevance of the partitions through the generation of biochemically-motivated surrogate random graph models. We apply the method to four distinct conformations of myosin tail interacting protein, a protein from the molecular motor of the malaria parasite, and study properties that have been experimentally addressed such as the closing mechanism, the presence of conserved clusters, and the identification through computational mutational analysis of key residues for binding

Dendrimer-protein interactions versus dendrimer-based nanomedicine.

Science.gov (United States)

Shcharbin, Dzmitry; Shcharbina, Natallia; Dzmitruk, Volha; Pedziwiatr-Werbicka, Elzbieta; Ionov, Maksim; Mignani, Serge; de la Mata, F Javier; Gómez, Rafael; Muñoz-Fernández, Maria Angeles; Majoral, Jean-Pierre; Bryszewska, Maria

2017-04-01

Dendrimers are hyperbranched polymers belonging to the huge class of nanomedical devices. Their wide application in biology and medicine requires understanding of the fundamental mechanisms of their interactions with biological systems. Summarizing, electrostatic force plays the predominant role in dendrimer-protein interactions, especially with charged dendrimers. Other kinds of interactions have been proven, such as H-bonding, van der Waals forces, and even hydrophobic interactions. These interactions depend on the characteristics of both participants: flexibility and surface charge of a dendrimer, rigidity of protein structure and the localization of charged amino acids at its surface. pH and ionic strength of solutions can significantly modulate interactions. Ligands and cofactors attached to a protein can also change dendrimer-protein interactions. Binding of dendrimers to a protein can change its secondary structure, conformation, intramolecular mobility and functional activity. However, this strongly depends on rigidity versus flexibility of a protein's structure. In addition, the potential applications of dendrimers to nanomedicine are reviwed related to dendrimer-protein interactions. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

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)....

Interaction between a plasma membrane-localized ankyrin-repeat protein ITN1 and a nuclear protein RTV1

Energy Technology Data Exchange (ETDEWEB)

Sakamoto, Hikaru [Department of Bioproduction, Faculty of Bioindustry, Tokyo University of Agriculture, 196 Yasaka, Abashiri-shi, Hokkaido 093-2422 (Japan); Sakata, Keiko; Kusumi, Kensuke [Department of Biology, Faculty of Sciences, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581 (Japan); Kojima, Mikiko; Sakakibara, Hitoshi [RIKEN Plant Science Center, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045 (Japan); Iba, Koh, E-mail: koibascb@kyushu-u.org [Department of Biology, Faculty of Sciences, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581 (Japan)

2012-06-29

Highlights: Black-Right-Pointing-Pointer ITN1, a plasma membrane ankyrin protein, interacts with a nuclear DNA-binding protein RTV1. Black-Right-Pointing-Pointer The nuclear transport of RTV1 is partially inhibited by interaction with ITN1. Black-Right-Pointing-Pointer RTV1 can promote the nuclear localization of ITN1. Black-Right-Pointing-Pointer Both overexpression of RTV1 and the lack of ITN1 increase salicylic acids sensitivity in plants. -- Abstract: The increased tolerance to NaCl 1 (ITN1) protein is a plasma membrane (PM)-localized protein involved in responses to NaCl stress in Arabidopsis. The predicted structure of ITN1 is composed of multiple transmembrane regions and an ankyrin-repeat domain that is known to mediate protein-protein interactions. To elucidate the molecular functions of ITN1, we searched for interacting partners using a yeast two-hybrid assay, and a nuclear-localized DNA-binding protein, RTV1, was identified as a candidate. Bimolecular fluorescence complementation analysis revealed that RTV1 interacted with ITN1 at the PM and nuclei in vivo. RTV1 tagged with red fluorescent protein localized to nuclei and ITN1 tagged with green fluorescent protein localized to PM; however, both proteins localized to both nuclei and the PM when co-expressed. These findings suggest that RTV1 and ITN1 regulate the subcellular localization of each other.

Protein interactions during flour mixing using wheat flour with altered starch.

Science.gov (United States)

Wang, Xiaolong; Appels, Rudi; Zhang, Xiaoke; Diepeveen, Dean; Torok, Kitti; Tomoskozi, Sandor; Bekes, Ferenc; Ma, Wujun; Sharp, Peter; Islam, Shahidul

2017-09-15

Wheat grain proteins responses to mixing and thermal treatment were investigated using Mixolab-dough analysis systems with flour from two cultivars, Ventura-26 (normal amylose content) and Ventura-19 (reduced amylose content). Size exclusion high performance liquid chromatography (SE-HPLC) and two-dimensional gel electrophoresis (2-DGE) analysis revealed that, stress associated and metabolic proteins largely interacted with dough matrix of Ventura-26 after 26min (56°C); gliadins, avenin-like b proteins, LMW-GSs, and partial globulins showed stronger interactions within the dough matrix of Ventura-26 at 32min/C3 (80°C), thereafter, however, stronger protein interactions were observed within the dough matrix of Ventura-19 at 38min/C4 (85°C) and 43min (80°C). Thirty-seven proteins associated with changes in dough matrix due to reduced amylose content were identified by mass spectrometry and mainly annotated to the chromosome group 1, 4, and 6. The findings provide new entry points for modifying final product attributes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Targeting protein-protein interactions for parasite control.

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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

Protein complex prediction based on k-connected subgraphs in protein interaction network

OpenAIRE

Habibi, Mahnaz; Eslahchi, Changiz; Wong, Limsoon

2010-01-01

Abstract Background Protein complexes play an important role in cellular mechanisms. Recently, several methods have been presented to predict protein complexes in a protein interaction network. In these methods, a protein complex is predicted as a dense subgraph of protein interactions. However, interactions data are incomplete and a protein complex does not have to be a complete or dense subgraph. Results We propose a more appropriate protein complex prediction method, CFA, that is based on ...

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

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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

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.

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.

Building blocks for protein interaction devices

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Grünberg, Raik; Ferrar, Tony S.; van der Sloot, Almer M.; Constante, Marco; Serrano, Luis

2010-01-01

Here, we propose a framework for the design of synthetic protein networks from modular protein–protein or protein–peptide interactions and provide a starter toolkit of protein building blocks. Our proof of concept experiments outline a general work flow for part–based protein systems engineering. We streamlined the iterative BioBrick cloning protocol and assembled 25 synthetic multidomain proteins each from seven standardized DNA fragments. A systematic screen revealed two main factors controlling protein expression in Escherichia coli: obstruction of translation initiation by mRNA secondary structure or toxicity of individual domains. Eventually, 13 proteins were purified for further characterization. Starting from well-established biotechnological tools, two general–purpose interaction input and two readout devices were built and characterized in vitro. Constitutive interaction input was achieved with a pair of synthetic leucine zippers. The second interaction was drug-controlled utilizing the rapamycin-induced binding of FRB(T2098L) to FKBP12. The interaction kinetics of both devices were analyzed by surface plasmon resonance. Readout was based on Förster resonance energy transfer between fluorescent proteins and was quantified for various combinations of input and output devices. Our results demonstrate the feasibility of parts-based protein synthetic biology. Additionally, we identify future challenges and limitations of modular design along with approaches to address them. PMID:20215443

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

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

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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.

Evidence for the interaction of the regulatory protein Ki-1/57 with p53 and its interacting proteins

International Nuclear Information System (INIS)

Nery, Flavia C.; Rui, Edmilson; Kuniyoshi, Tais M.; Kobarg, Joerg

2006-01-01

Ki-1/57 is a cytoplasmic and nuclear phospho-protein of 57 kDa and interacts with the adaptor protein RACK1, the transcription factor MEF2C, and the chromatin remodeling factor CHD3, suggesting that it might be involved in the regulation of transcription. Here, we describe yeast two-hybrid studies that identified a total of 11 proteins interacting with Ki-1/57, all of which interact or are functionally associated with p53 or other members of the p53 family of proteins. We further found that Ki-1/57 is able to interact with p53 itself in the yeast two-hybrid system when the interaction was tested directly. This interaction could be confirmed by pull down assays with purified proteins in vitro and by reciprocal co-immunoprecipitation assays from the human Hodgkin analogous lymphoma cell line L540. Furthermore, we found that the phosphorylation of p53 by PKC abolishes its interaction with Ki-1/57 in vitro

Probing protein interactions with hydrogen/deuterium exchange and mass spectrometry—A review

International Nuclear Information System (INIS)

Percy, Andrew J.; Rey, Martial; Burns, Kyle M.; Schriemer, David C.

2012-01-01

Highlights: ► Protein chemistry generates mass shifts useful for structure–function studies. ► H/DX supports a powerful mass shift method for protein interaction analysis. ► H/DX mass shifts are useful for determining binding data (K d , off-rates). ► Improved H/DX–MS workflows can accommodate complex protein systems. - Abstract: Assessing the functional outcome of protein interactions in structural terms is a goal of structural biology, however most techniques have a limited capacity for making structure–function determinations with both high resolution and high throughput. Mass spectrometry can be applied as a reader of protein chemistries in order to fill this void, and enable methodologies whereby protein structure–function determinations may be made on a proteome-wide level. Protein hydrogen/deuterium exchange (H/DX) offers a chemical labeling strategy suitable for tracking changes in “dynamic topography” and thus represents a powerful means of monitoring protein structure–function relationships. This review presents the exchange method in the context of interaction analysis. Applications involving interface detection, quantitation of binding, and conformational responses to ligation are discussed, and commentary on recent analytical developments is provided.

Protein complex prediction based on k-connected subgraphs in protein interaction network

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Habibi Mahnaz

2010-09-01

Full Text Available Abstract Background Protein complexes play an important role in cellular mechanisms. Recently, several methods have been presented to predict protein complexes in a protein interaction network. In these methods, a protein complex is predicted as a dense subgraph of protein interactions. However, interactions data are incomplete and a protein complex does not have to be a complete or dense subgraph. Results We propose a more appropriate protein complex prediction method, CFA, that is based on connectivity number on subgraphs. We evaluate CFA using several protein interaction networks on reference protein complexes in two benchmark data sets (MIPS and Aloy, containing 1142 and 61 known complexes respectively. We compare CFA to some existing protein complex prediction methods (CMC, MCL, PCP and RNSC in terms of recall and precision. We show that CFA predicts more complexes correctly at a competitive level of precision. Conclusions Many real complexes with different connectivity level in protein interaction network can be predicted based on connectivity number. Our CFA program and results are freely available from http://www.bioinf.cs.ipm.ir/softwares/cfa/CFA.rar.

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

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

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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

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

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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

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

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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.

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

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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.

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

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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.

Interaction of Proteins Identified in Human Thyroid Cells

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Pietsch, Jessica; Riwaldt, Stefan; Bauer, Johann; Sickmann, Albert; Weber, Gerhard; Grosse, Jirka; Infanger, Manfred; Eilles, Christoph; Grimm, Daniela

2013-01-01

Influence of gravity forces on the regulation of protein expression by healthy and malignant thyroid cells was studied with the aim to identify protein interactions. Western blot analyses of a limited number of proteins suggested a time-dependent regulation of protein expression by simulated microgravity. After applying free flow isoelectric focusing and mass spectrometry to search for differently expressed proteins by thyroid cells exposed to simulated microgravity for three days, a considerable number of candidates for gravi-sensitive proteins were detected. In order to show how proteins sensitive to microgravity could directly influence other proteins, we investigated all polypeptide chains identified with Mascot scores above 100, looking for groups of interacting proteins. Hence, UniProtKB entry numbers of all detected proteins were entered into the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and processed. The program indicated that we had detected various groups of interacting proteins in each of the three cell lines studied. The major groups of interacting proteins play a role in pathways of carbohydrate and protein metabolism, regulation of cell growth and cell membrane structuring. Analyzing these groups, networks of interaction could be established which show how a punctual influence of simulated microgravity may propagate via various members of interaction chains. PMID:23303277

Interaction of Proteins Identified in Human Thyroid Cells

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Jessica Pietsch

2013-01-01

Full Text Available Influence of gravity forces on the regulation of protein expression by healthy and malignant thyroid cells was studied with the aim to identify protein interactions. Western blot analyses of a limited number of proteins suggested a time-dependent regulation of protein expression by simulated microgravity. After applying free flow isoelectric focusing and mass spectrometry to search for differently expressed proteins by thyroid cells exposed to simulated microgravity for three days, a considerable number of candidates for gravi-sensitive proteins were detected. In order to show how proteins sensitive to microgravity could directly influence other proteins, we investigated all polypeptide chains identified with Mascot scores above 100, looking for groups of interacting proteins. Hence, UniProtKB entry numbers of all detected proteins were entered into the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING and processed. The program indicated that we had detected various groups of interacting proteins in each of the three cell lines studied. The major groups of interacting proteins play a role in pathways of carbohydrate and protein metabolism, regulation of cell growth and cell membrane structuring. Analyzing these groups, networks of interaction could be established which show how a punctual influence of simulated microgravity may propagate via various members of interaction chains.

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

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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.

Effective Identification of Akt Interacting Proteins by Two-Step Chemical Crosslinking, Co-Immunoprecipitation and Mass Spectrometry

Science.gov (United States)

Huang, Bill X.; Kim, Hee-Yong

2013-01-01

Akt is a critical protein for cell survival and known to interact with various proteins. However, Akt binding partners that modulate or regulate Akt activation have not been fully elucidated. Identification of Akt-interacting proteins has been customarily achieved by co-immunoprecipitation combined with western blot and/or MS analysis. An intrinsic problem of the method is loss of interacting proteins during procedures to remove non-specific proteins. Moreover, antibody contamination often interferes with the detection of less abundant proteins. Here, we developed a novel two-step chemical crosslinking strategy to overcome these problems which resulted in a dramatic improvement in identifying Akt interacting partners. Akt antibody was first immobilized on protein A/G beads using disuccinimidyl suberate and allowed to bind to cellular Akt along with its interacting proteins. Subsequently, dithiobis[succinimidylpropionate], a cleavable crosslinker, was introduced to produce stable complexes between Akt and binding partners prior to the SDS-PAGE and nanoLC-MS/MS analysis. This approach enabled identification of ten Akt partners from cell lysates containing as low as 1.5 mg proteins, including two new potential Akt interacting partners. None of these but one protein was detectable without crosslinking procedures. The present method provides a sensitive and effective tool to probe Akt-interacting proteins. This strategy should also prove useful for other protein interactions, particularly those involving less abundant or weakly associating partners. PMID:23613850

Intercellular protein-protein interactions at synapses.

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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.

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

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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.

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

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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

Evidence of probabilistic behaviour in protein interaction networks

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Reifman Jaques

2008-01-01

Full Text Available Abstract Background Data from high-throughput experiments of protein-protein interactions are commonly used to probe the nature of biological organization and extract functional relationships between sets of proteins. What has not been appreciated is that the underlying mechanisms involved in assembling these networks may exhibit considerable probabilistic behaviour. Results We find that the probability of an interaction between two proteins is generally proportional to the numerical product of their individual interacting partners, or degrees. The degree-weighted behaviour is manifested throughout the protein-protein interaction networks studied here, except for the high-degree, or hub, interaction areas. However, we find that the probabilities of interaction between the hubs are still high. Further evidence is provided by path length analyses, which show that these hubs are separated by very few links. Conclusion The results suggest that protein-protein interaction networks incorporate probabilistic elements that lead to scale-rich hierarchical architectures. These observations seem to be at odds with a biologically-guided organization. One interpretation of the findings is that we are witnessing the ability of proteins to indiscriminately bind rather than the protein-protein interactions that are actually utilized by the cell in biological processes. Therefore, the topological study of a degree-weighted network requires a more refined methodology to extract biological information about pathways, modules, or other inferred relationships among proteins.

Topology-function conservation in protein-protein interaction networks.

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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.

Taylor Dispersion Analysis as a promising tool for assessment of peptide-peptide interactions.

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Høgstedt, Ulrich B; Schwach, Grégoire; van de Weert, Marco; Østergaard, Jesper

2016-10-10

Protein-protein and peptide-peptide (self-)interactions are of key importance in understanding the physiochemical behavior of proteins and peptides in solution. However, due to the small size of peptide molecules, characterization of these interactions is more challenging than for proteins. In this work, we show that protein-protein and peptide-peptide interactions can advantageously be investigated by measurement of the diffusion coefficient using Taylor Dispersion Analysis. Through comparison to Dynamic Light Scattering it was shown that Taylor Dispersion Analysis is well suited for the characterization of protein-protein interactions of solutions of α-lactalbumin and human serum albumin. The peptide-peptide interactions of three selected peptides were then investigated in a concentration range spanning from 0.5mg/ml up to 80mg/ml using Taylor Dispersion Analysis. The peptide-peptide interactions determination indicated that multibody interactions significantly affect the PPIs at concentration levels above 25mg/ml for the two charged peptides. Relative viscosity measurements, performed using the capillary based setup applied for Taylor Dispersion Analysis, showed that the viscosity of the peptide solutions increased with concentration. Our results indicate that a viscosity difference between run buffer and sample in Taylor Dispersion Analysis may result in overestimation of the measured diffusion coefficient. Thus, Taylor Dispersion Analysis provides a practical, but as yet primarily qualitative, approach to assessment of the colloidal stability of both peptide and protein formulations. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

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.

Enhancing the Functional Content of Eukaryotic Protein Interaction Networks

Science.gov (United States)

Pandey, Gaurav; Arora, Sonali; Manocha, Sahil; Whalen, Sean

2014-01-01

Protein interaction networks are a promising type of data for studying complex biological systems. However, despite the rich information embedded in these networks, these networks face important data quality challenges of noise and incompleteness that adversely affect the results obtained from their analysis. Here, we apply a robust measure of local network structure called common neighborhood similarity (CNS) to address these challenges. Although several CNS measures have been proposed in the literature, an understanding of their relative efficacies for the analysis of interaction networks has been lacking. We follow the framework of graph transformation to convert the given interaction network into a transformed network corresponding to a variety of CNS measures evaluated. The effectiveness of each measure is then estimated by comparing the quality of protein function predictions obtained from its corresponding transformed network with those from the original network. Using a large set of human and fly protein interactions, and a set of over GO terms for both, we find that several of the transformed networks produce more accurate predictions than those obtained from the original network. In particular, the measure and other continuous CNS measures perform well this task, especially for large networks. Further investigation reveals that the two major factors contributing to this improvement are the abilities of CNS measures to prune out noisy edges and enhance functional coherence in the transformed networks. PMID:25275489

Graph theoretic analysis of protein interaction networks of eukaryotes

Science.gov (United States)

Goh, K.-I.; Kahng, B.; Kim, D.

2005-11-01

Owing to the recent progress in high-throughput experimental techniques, the datasets of large-scale protein interactions of prototypical multicellular species, the nematode worm Caenorhabditis elegans and the fruit fly Drosophila melanogaster, have been assayed. The datasets are obtained mainly by using the yeast hybrid method, which contains false-positive and false-negative simultaneously. Accordingly, while it is desirable to test such datasets through further wet experiments, here we invoke recent developed network theory to test such high-throughput datasets in a simple way. Based on the fact that the key biological processes indispensable to maintaining life are conserved across eukaryotic species, and the comparison of structural properties of the protein interaction networks (PINs) of the two species with those of the yeast PIN, we find that while the worm and yeast PIN datasets exhibit similar structural properties, the current fly dataset, though most comprehensively screened ever, does not reflect generic structural properties correctly as it is. The modularity is suppressed and the connectivity correlation is lacking. Addition of interologs to the current fly dataset increases the modularity and enhances the occurrence of triangular motifs as well. The connectivity correlation function of the fly, however, remains distinct under such interolog additions, for which we present a possible scenario through an in silico modeling.

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

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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

Full Data of Yeast Interacting Proteins Database (Original Version) - Yeast Interacting Proteins Database | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us Yeast Interacting Proteins Database Full Data of Yeast Interacting Proteins Database (Origin...al Version) Data detail Data name Full Data of Yeast Interacting Proteins Database (Original Version) DOI 10....18908/lsdba.nbdc00742-004 Description of data contents The entire data in the Yeast Interacting Proteins Database...eir interactions are required. Several sources including YPD (Yeast Proteome Database, Costanzo, M. C., Hoga...ematic name in the SGD (Saccharomyces Genome Database; http://www.yeastgenome.org /). Bait gene name The gen

Yeast Interacting Proteins Database: YGL127C, YOR047C [Yeast Interacting Proteins Database

Lifescience Database Archive (English)

Full Text Available ith protein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding protein Spt15p; acts as a regula...rotein involved in control of glucose-regulated gene expression; interacts with protein kinase Snf1p, glucose sensors

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

Directory of Open Access Journals (Sweden)

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.

Molecular Analysis of AFP and HSA Interactions with PTEN Protein

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Mingyue Zhu

2015-01-01

Full Text Available Human cytoplasmic alpha-fetoprotein (AFP has been classified as a member of the albuminoid gene family. The protein sequence of AFP has significant homology to that of human serum albumin (HSA, but its biological characteristics are vastly different from HSA. The AFP functions as a regulator in the phosphatidylinositol 3-kinase (PI3K/protein kinase B (AKT pathway, but HSA plays a key role as a transport protein. To probe their molecular mechanisms, we have applied colocalization, coimmunoprecipitation (co-IP, and molecular docking approaches to analyze the differences between AFP and HSA. The data from colocalization and co-IP displayed a strong interaction between AFP and PTEN (phosphatase and tensin homolog, demonstrating that AFP did bind to PTEN, but HSA did not. The molecular docking study further showed that the AFP domains I and III could contact with PTEN. In silicon substitutions of AFP binding site residues at position 490M/K and 105L/R corresponding to residues K490 and R105 in HSA resulted in steric clashes with PTEN residues R150 and K46, respectively. These steric clashes may explain the reason why HSA cannot bind to PTEN. Ultimately, the experimental results and the molecular modeling data from the interactions of AFP and HSA with PTEN will help us to identify targets for designing drugs and vaccines against human hepatocellular carcinoma.

[Pharmacological mechanism analysis of oligopeptide from Pinctada fucata based on in silico proteolysis and protein interaction network].

Science.gov (United States)

Chen, Yan-Kun; Qiao, Lian-Sheng; Huo, Xiao-Qian; Zhang, Xu; Han, Na; Zhang, Yan-Ling

2017-09-01

Pinctada fucata oligopeptide is one of key pharmaceutical effective constituents of P. fucata. It is significant to analyze its pharmacological effect and mechanism. This study aims to discover the potential oligopeptides from P. fucata and analyze the mechanism of P. fucata oligopeptide based on in silico technologies and protein interaction network(PIN). First, main protein sequences of P. fucata were collected, and oligopeptides were obtained using in silico gastrointestinal tract proteolysis. Then, key potential targets of P. fucata oligopeptides were obtained through pharmacophore screening. The protein-protein interaction(PPI) of targets was achieved and implemented to construct PIN and analyze the mechanism of P. fucata oligopeptides. P. fucata oligopeptide database was constructed based on in silico technologies, including 458 oligopeptides. Twelve modules were identified from PIN by a graph theoretic clustering algorithm Molecular Complex Detection(MCODE) and analyzed by Gene ontology(GO) enrichment. The results indicated that P. fucata oligopeptides have an effect in treating neurological diseases, such as Alzheimer's disease. In silico proteolysis could be used to analyze the protein sequences of traditional Chinese medicine(TCM). According to the combination of in silico proteolysis and PIN, the biological activity of oligopeptides could be interpreted rapidly based on the known TCM protein sequence. The study provides the methodology basis for rapidly and efficiently implementing the mechanism analysis of TCM oligopeptides. Copyright© by the Chinese Pharmaceutical Association.

Yeast Interacting Proteins Database: YOR047C, YKL038W [Yeast Interacting Proteins Database

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Full Text Available racts with protein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding protein Spt15p; acts as a...Bait description Protein involved in control of glucose-regulated gene expression; interacts with protein kinase Snf1p, glucose senso...rs Snf3p and Rgt2p, and TATA-binding protein Spt15p; acts as a regulator of the tra

Yeast Interacting Proteins Database: YFR049W, YOR047C [Yeast Interacting Proteins Database

Lifescience Database Archive (English)

Full Text Available protein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding protein Spt15p; acts as a regulator... (0) YOR047C STD1 Protein involved in control of glucose-regulated gene expression; interacts with protein kinase Snf1p, glucose sens...ors Snf3p and Rgt2p, and TATA-binding protein Spt15p; ac

Yeast Interacting Proteins Database: YGL145W, YNL258C [Yeast Interacting Proteins Database

Lifescience Database Archive (English)

Full Text Available ripheral membrane protein required for Golgi-to-ER retrograde traffic; component ... membrane protein required for Golgi-to-ER retrograde traffic; component of the ER target site that interact

A rice kinase-protein interaction map.

Science.gov (United States)

Ding, Xiaodong; Richter, Todd; Chen, Mei; Fujii, Hiroaki; Seo, Young Su; Xie, Mingtang; Zheng, Xianwu; Kanrar, Siddhartha; Stevenson, Rebecca A; Dardick, Christopher; Li, Ying; Jiang, Hao; Zhang, Yan; Yu, Fahong; Bartley, Laura E; Chern, Mawsheng; Bart, Rebecca; Chen, Xiuhua; Zhu, Lihuang; Farmerie, William G; Gribskov, Michael; Zhu, Jian-Kang; Fromm, Michael E; Ronald, Pamela C; Song, Wen-Yuan

2009-03-01

Plants uniquely contain large numbers of protein kinases, and for the vast majority of the 1,429 kinases predicted in the rice (Oryza sativa) genome, little is known of their functions. Genetic approaches often fail to produce observable phenotypes; thus, new strategies are needed to delineate kinase function. We previously developed a cost-effective high-throughput yeast two-hybrid system. Using this system, we have generated a protein interaction map of 116 representative rice kinases and 254 of their interacting proteins. Overall, the resulting interaction map supports a large number of known or predicted kinase-protein interactions from both plants and animals and reveals many new functional insights. Notably, we found a potential widespread role for E3 ubiquitin ligases in pathogen defense signaling mediated by receptor-like kinases, particularly by the kinases that may have evolved from recently expanded kinase subfamilies in rice. We anticipate that the data provided here will serve as a foundation for targeted functional studies in rice and other plants. The application of yeast two-hybrid and TAPtag analyses for large-scale plant protein interaction studies is also discussed.

Analysis and Ranking of Protein-Protein Docking Models Using Inter-Residue Contacts and Inter-Molecular Contact Maps

KAUST Repository

Oliva, Romina; Chermak, Edrisse; Cavallo, Luigi

2015-01-01

In view of the increasing interest both in inhibitors of protein-protein interactions and in protein drugs themselves, analysis of the three-dimensional structure of protein-protein complexes is assuming greater relevance in drug design. In the many cases where an experimental structure is not available, protein-protein docking becomes the method of choice for predicting the arrangement of the complex. However, reliably scoring protein-protein docking poses is still an unsolved problem. As a consequence, the screening of many docking models is usually required in the analysis step, to possibly single out the correct ones. Here, making use of exemplary cases, we review our recently introduced methods for the analysis of protein complex structures and for the scoring of protein docking poses, based on the use of inter-residue contacts and their visualization in inter-molecular contact maps. We also show that the ensemble of tools we developed can be used in the context of rational drug design targeting protein-protein interactions.

Analysis and Ranking of Protein-Protein Docking Models Using Inter-Residue Contacts and Inter-Molecular Contact Maps

KAUST Repository

Oliva, Romina

2015-07-01

In view of the increasing interest both in inhibitors of protein-protein interactions and in protein drugs themselves, analysis of the three-dimensional structure of protein-protein complexes is assuming greater relevance in drug design. In the many cases where an experimental structure is not available, protein-protein docking becomes the method of choice for predicting the arrangement of the complex. However, reliably scoring protein-protein docking poses is still an unsolved problem. As a consequence, the screening of many docking models is usually required in the analysis step, to possibly single out the correct ones. Here, making use of exemplary cases, we review our recently introduced methods for the analysis of protein complex structures and for the scoring of protein docking poses, based on the use of inter-residue contacts and their visualization in inter-molecular contact maps. We also show that the ensemble of tools we developed can be used in the context of rational drug design targeting protein-protein interactions.

Protein-x of hepatitis B virus in interaction with CCAAT/enhancer-binding protein α (C/EBPα - an in silico analysis approach

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Mohamadkhani Ashraf

2011-10-01

Full Text Available Abstract Background Even though many functions of protein-x from the Hepatitis B virus (HBV have been revealed, the nature of protein-x is yet unknown. This protein is well-known for its transactivation activity through interaction with several cellular transcription factors, it is also known as an oncogene. In this work, we have presented computational approaches to design a model to show the structure of protein-x and its respective binding sites associated with the CCAAT/enhancer-binding protein α (C/EBPα. C/EBPα belongs to the bZip family of transcription factors, which activates transcription of several genes through its binding sites in liver and fat cells. The C/EBPα has been shown to bind and modulate enhancer I and the enhancer II/core promoter of HBV. In this study using the bioinformatics tools we tried to present a reliable model for the protein-x interaction with C/EBPα. Results The amino acid sequence of protein-x was extracted from UniProt [UniProt:Q80IU5] and the x-ray crystal structure of the partial CCAAT-enhancer α [PDB:1NWQ] was retrieved from the Protein Data Bank (PDB. Similarity search for protein-x was carried out by psi-blast and bl2seq using NCBI [GenBank: BAC65106.1] and Local Meta-Threading-Server (LOMETS was used as a threading server for determining the maximum tertiary structure similarities. Advanced MODELLER was implemented to design a comparative model, however, due to the lack of a suitable template, Quark was used for ab initio tertiary structure prediction. The PDB-blast search indicated a maximum of 23% sequence identity and 33% similarity with crystal structure of the porcine reproductive and respiratory syndrome virus leader protease Nsp1α [PDB:3IFU]. This meant that protein-x does not have a suitable template to predict its tertiary structure using comparative modeling tools, therefore we used QUARK as an ab initio 3D prediction approach. Docking results from the ab initio tertiary structure of

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

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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.

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

Mass spectrometric identification of proteins that interact through specific domains of the poly(A) binding protein.

Science.gov (United States)

Richardson, Roy; Denis, Clyde L; Zhang, Chongxu; Nielsen, Maria E O; Chiang, Yueh-Chin; Kierkegaard, Morten; Wang, Xin; Lee, Darren J; Andersen, Jens S; Yao, Gang

2012-09-01

Poly(A) binding protein (PAB1) is involved in a number of RNA metabolic functions in eukaryotic cells and correspondingly is suggested to associate with a number of proteins. We have used mass spectrometric analysis to identify 55 non-ribosomal proteins that specifically interact with PAB1 from Saccharomyces cerevisiae. Because many of these factors may associate only indirectly with PAB1 by being components of the PAB1-mRNP structure, we additionally conducted mass spectrometric analyses on seven metabolically defined PAB1 deletion derivatives to delimit the interactions between these proteins and PAB1. These latter analyses identified 13 proteins whose associations with PAB1 were reduced by deleting one or another of PAB1's defined domains. Included in this list of 13 proteins were the translation initiation factors eIF4G1 and eIF4G2, translation termination factor eRF3, and PBP2, all of whose previously known direct interactions with specific PAB1 domains were either confirmed, delimited, or extended. The remaining nine proteins that interacted through a specific PAB1 domain were CBF5, SLF1, UPF1, CBC1, SSD1, NOP77, yGR250c, NAB6, and GBP2. In further study, UPF1, involved in nonsense-mediated decay, was confirmed to interact with PAB1 through the RRM1 domain. We additionally established that while the RRM1 domain of PAB1 was required for UPF1-induced acceleration of deadenylation during nonsense-mediated decay, it was not required for the more critical step of acceleration of mRNA decapping. These results begin to identify the proteins most likely to interact with PAB1 and the domains of PAB1 through which these contacts are made.

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

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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.

Interaction study of rice stripe virus proteins reveals a region of the nucleocapsid protein (NP) required for NP self-interaction and nuclear localization.

Science.gov (United States)

Lian, Sen; Cho, Won Kyong; Jo, Yeonhwa; Kim, Sang-Min; Kim, Kook-Hyung

2014-04-01

Rice stripe virus (RSV), which belongs to the genus Tenuivirus, is an emergent virus problem. The RSV genome is composed of four single-strand RNAs (RNA1-RNA4) and encodes seven proteins. We investigated interactions between six of the RSV proteins by yeast-two hybrid (Y2H) assay in vitro and by bimolecular fluorescence complementation (BiFC) in planta. Y2H identified self-interaction of the nucleocapsid protein (NP) and NS3, while BiFC revealed self-interaction of NP, NS3, and NCP. To identify regions(s) and/or crucial amino acid (aa) residues required for NP self-interaction, we generated various truncated and aa substitution mutants. Y2H assay showed that the N-terminal region of NP (aa 1-56) is necessary for NP self-interaction. Further analysis with substitution mutants demonstrated that additional aa residues located at 42-47 affected their interaction with full-length NP. These results indicate that the N-terminal region (aa 1-36 and 42-47) is required for NP self-interaction. BiFC and co-localization studies showed that the region required for NP self-interaction is also required for NP localization at the nucleus. Overall, our results indicate that the N-terminal region (aa 1-47) of the NP is important for NP self-interaction and that six aa residues (42-47) are essential for both NP self-interaction and nuclear localization. Copyright © 2014 Elsevier B.V. All rights reserved.

The genotype-environment interaction variance in rice-seed protein determination

International Nuclear Information System (INIS)

Ismachin, M.

1976-01-01

Many environmental factors influence the protein content of cereal seed. This fact procured difficulties in breeding for protein. Yield is another example on which so many environmental factors are of influence. The length of time required by the plant to reach maturity, is also affected by the environmental factors; even though its effect is not too decisive. In this investigation the genotypic variance and the genotype-environment interaction variance which contribute to the total variance or phenotypic variance was analysed, with purpose to give an idea to the breeder how selection should be made. It was found that genotype-environment interaction variance is larger than the genotypic variance in contribution to total variance of protein-seed determination or yield. In the analysis of the time required to reach maturity it was found that genotypic variance is larger than the genotype-environment interaction variance. It is therefore clear, why selection for time required to reach maturity is much easier than selection for protein or yield. Selected protein in one location may be different from that to other locations. (author)

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.

Holistic Approach to Partial Covalent Interactions in Protein Structure Prediction and Design with Rosetta.

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Combs, Steven A; Mueller, Benjamin K; Meiler, Jens

2018-05-29

Partial covalent interactions (PCIs) in proteins, which include hydrogen bonds, salt bridges, cation-π, and π-π interactions, contribute to thermodynamic stability and facilitate interactions with other biomolecules. Several score functions have been developed within the Rosetta protein modeling framework that identify and evaluate these PCIs through analyzing the geometry between participating atoms. However, we hypothesize that PCIs can be unified through a simplified electron orbital representation. To test this hypothesis, we have introduced orbital based chemical descriptors for PCIs into Rosetta, called the PCI score function. Optimal geometries for the PCIs are derived from a statistical analysis of high-quality protein structures obtained from the Protein Data Bank (PDB), and the relative orientation of electron deficient hydrogen atoms and electron-rich lone pair or π orbitals are evaluated. We demonstrate that nativelike geometries of hydrogen bonds, salt bridges, cation-π, and π-π interactions are recapitulated during minimization of protein conformation. The packing density of tested protein structures increased from the standard score function from 0.62 to 0.64, closer to the native value of 0.70. Overall, rotamer recovery improved when using the PCI score function (75%) as compared to the standard Rosetta score function (74%). The PCI score function represents an improvement over the standard Rosetta score function for protein model scoring; in addition, it provides a platform for future directions in the analysis of small molecule to protein interactions, which depend on partial covalent interactions.

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. .

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

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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.

Prediction and characterization of protein-protein interaction networks in swine

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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/.

Yeast Interacting Proteins Database: YOR358W, YOR047C [Yeast Interacting Proteins Database

Lifescience Database Archive (English)

Full Text Available ; interacts with protein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding protein Spt15p; act...rotein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding protein Spt15p; acts as a regulator o

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

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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.

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

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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.

Analysis of protein targets in pathogen-host interaction in infectious diseases: a case study on Plasmodium falciparum and Homo sapiens interaction network.

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Saha, Sovan; Sengupta, Kaustav; Chatterjee, Piyali; Basu, Subhadip; Nasipuri, Mita

2017-09-23

Infection and disease progression is the outcome of protein interactions between pathogen and host. Pathogen, the role player of Infection, is becoming a severe threat to life as because of its adaptability toward drugs and evolutionary dynamism in nature. Identifying protein targets by analyzing protein interactions between host and pathogen is the key point. Proteins with higher degree and possessing some topologically significant graph theoretical measures are found to be drug targets. On the other hand, exceptional nodes may be involved in infection mechanism because of some pathway process and biologically unknown factors. In this article, we attempt to investigate characteristics of host-pathogen protein interactions by presenting a comprehensive review of computational approaches applied on different infectious diseases. As an illustration, we have analyzed a case study on infectious disease malaria, with its causative agent Plasmodium falciparum acting as 'Bait' and host, Homo sapiens/human acting as 'Prey'. In this pathogen-host interaction network based on some interconnectivity and centrality properties, proteins are viewed as central, peripheral, hub and non-hub nodes and their significance on infection process. Besides, it is observed that because of sparseness of the pathogen and host interaction network, there may be some topologically unimportant but biologically significant proteins, which can also act as Bait/Prey. So, functional similarity or gene ontology mapping can help us in this case to identify these proteins. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Cytoprophet: a Cytoscape plug-in for protein and domain interaction networks inference.

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Morcos, Faruck; Lamanna, Charles; Sikora, Marcin; Izaguirre, Jesús

2008-10-01

Cytoprophet is a software tool that allows prediction and visualization of protein and domain interaction networks. It is implemented as a plug-in of Cytoscape, an open source software framework for analysis and visualization of molecular networks. Cytoprophet implements three algorithms that predict new potential physical interactions using the domain composition of proteins and experimental assays. The algorithms for protein and domain interaction inference include maximum likelihood estimation (MLE) using expectation maximization (EM); the set cover approach maximum specificity set cover (MSSC) and the sum-product algorithm (SPA). After accepting an input set of proteins with Uniprot ID/Accession numbers and a selected prediction algorithm, Cytoprophet draws a network of potential interactions with probability scores and GO distances as edge attributes. A network of domain interactions between the domains of the initial protein list can also be generated. Cytoprophet was designed to take advantage of the visual capabilities of Cytoscape and be simple to use. An example of inference in a signaling network of myxobacterium Myxococcus xanthus is presented and available at Cytoprophet's website. http://cytoprophet.cse.nd.edu.

Toponomics analysis of functional interactions of the ubiquitin ligase PAM (Protein Associated with Myc) during spinal nociceptive processing.

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Pierre, Sandra; Maeurer, Christian; Coste, Ovidiu; Becker, Wiebke; Schmidtko, Achim; Holland, Sabrina; Wittpoth, Claus; Geisslinger, Gerd; Scholich, Klaus

2008-12-01

Protein associated with Myc (PAM) is a giant E3 ubiquitin ligase of 510 kDa. Although the role of PAM during neuronal development is well established, very little is known about its function in the regulation of synaptic strength. Here we used multiepitope ligand cartography (MELC) to study protein network profiles associated with PAM during the modulation of synaptic strength. MELC is a novel imaging technology that utilizes biomathematical tools to describe protein networks after consecutive immunohistochemical visualization of up to 100 proteins on the same sample. As an in vivo model to modulate synaptic strength we used the formalin test, a common model for acute and inflammatory pain. MELC analysis was performed with 37 different antibodies or fluorescence tags on spinal cord slices and led to the identification of 1390 PAM-related motifs that distinguish untreated and formalin-treated spinal cords. The majority of these motifs related to ubiquitin-dependent processes and/or the actin cytoskeleton. We detected an intermittent colocalization of PAM and ubiquitin with TSC2, a known substrate of PAM, and the glutamate receptors mGluR5 and GLUR1. Importantly these complexes were detected exclusively in the presence of F-actin. A direct PAM/F-actin interaction was confirmed by colocalization and cosedimentation. The binding of PAM toward F-actin varied strongly between the PAM splice forms found in rat spinal cords. PAM did not ubiquitylate actin or alter actin polymerization and depolymerization. However, F-actin decreased the ubiquitin ligase activity of purified PAM. Because PAM activation is known to involve its translocation, the binding of PAM to F-actin may serve to control its subcellular localization as well as its activity. Taken together we show that defining protein network profiles by topological proteomics analysis is a useful tool to identify previously unknown protein/protein interactions that underlie synaptic processes.

Small sets of interacting proteins suggest functional linkage mechanisms via Bayesian analogical reasoning.

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Airoldi, Edoardo M; Heller, Katherine A; Silva, Ricardo

2011-07-01

Proteins and protein complexes coordinate their activity to execute cellular functions. In a number of experimental settings, including synthetic genetic arrays, genetic perturbations and RNAi screens, scientists identify a small set of protein interactions of interest. A working hypothesis is often that these interactions are the observable phenotypes of some functional process, which is not directly observable. Confirmatory analysis requires finding other pairs of proteins whose interaction may be additional phenotypical evidence about the same functional process. Extant methods for finding additional protein interactions rely heavily on the information in the newly identified set of interactions. For instance, these methods leverage the attributes of the individual proteins directly, in a supervised setting, in order to find relevant protein pairs. A small set of protein interactions provides a small sample to train parameters of prediction methods, thus leading to low confidence. We develop RBSets, a computational approach to ranking protein interactions rooted in analogical reasoning; that is, the ability to learn and generalize relations between objects. Our approach is tailored to situations where the training set of protein interactions is small, and leverages the attributes of the individual proteins indirectly, in a Bayesian ranking setting that is perhaps closest to propensity scoring in mathematical psychology. We find that RBSets leads to good performance in identifying additional interactions starting from a small evidence set of interacting proteins, for which an underlying biological logic in terms of functional processes and signaling pathways can be established with some confidence. Our approach is scalable and can be applied to large databases with minimal computational overhead. Our results suggest that analogical reasoning within a Bayesian ranking problem is a promising new approach for real-time biological discovery. Java code is available at

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

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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.

On the importance of polar interactions for complexes containing intrinsically disordered proteins.

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Eric T C Wong

Full Text Available There is a growing recognition for the importance of proteins with large intrinsically disordered (ID segments in cell signaling and regulation. ID segments in these proteins often harbor regions that mediate molecular recognition. Coupled folding and binding of the recognition regions has been proposed to confer high specificity to interactions involving ID segments. However, researchers recently questioned the origin of the interaction specificity of ID proteins because of the overrepresentation of hydrophobic residues in their interaction interfaces. Here, we focused on the role of polar and charged residues in interactions mediated by ID segments. Making use of the extended nature of most ID segments when in complex with globular proteins, we first identified large numbers of complexes between globular proteins and ID segments by using radius-of-gyration-based selection criteria. Consistent with previous studies, we found the interfaces of these complexes to be enriched in hydrophobic residues, and that these residues contribute significantly to the stability of the interaction interface. However, our analyses also show that polar interactions play a larger role in these complexes than in structured protein complexes. Computational alanine scanning and salt-bridge analysis indicate that interfaces in ID complexes are highly complementary with respect to electrostatics, more so than interfaces of globular proteins. Follow-up calculations of the electrostatic contributions to the free energy of binding uncovered significantly stronger Coulombic interactions in complexes harbouring ID segments than in structured protein complexes. However, they are counter-balanced by even higher polar-desolvation penalties. We propose that polar interactions are a key contributing factor to the observed high specificity of ID segment-mediated interactions.

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

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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.

Proteomic analysis of protein interactions between Eimeria maxima sporozoites and chicken jejunal epithelial cells by shotgun LC-MS/MS.

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Huang, Jingwei; Liu, Tingqi; Li, Ke; Song, Xiaokai; Yan, Ruofeng; Xu, Lixin; Li, Xiangrui

2018-04-04

Eimeria maxima initiates infection by invading the jejunal epithelial cells of chicken. However, the proteins involved in invasion remain unknown. The research of the molecules that participate in the interactions between E. maxima sporozoites and host target cells will fill a gap in our understanding of the invasion system of this parasitic pathogen. In the present study, chicken jejunal epithelial cells were isolated and cultured in vitro. Western blot was employed to analyze the soluble proteins of E. maxima sporozoites that bound to chicken jejunal epithelial cells. Co-immunoprecipitation (co-IP) assay was used to separate the E. maxima proteins that bound to chicken jejunal epithelial cells. Shotgun LC-MS/MS technique was used for proteomics identification and Gene Ontology was employed for the bioinformatics analysis. The results of Western blot analysis showed that four proteins bands from jejunal epithelial cells co-cultured with soluble proteins of E. maxima sporozoites were recognized by the positive sera, with molecular weights of 70, 90, 95 and 130 kDa. The co-IP dilutions were analyzed by shotgun LC-MS/MS. A total of 204 proteins were identified in the E. maxima protein database using the MASCOT search engine. Thirty-five proteins including microneme protein 3 and 7 had more than two unique peptide counts and were annotated using Gene Ontology for molecular function, biological process and cellular localization. The results revealed that of the 35 annotated peptides, 22 (62.86%) were associated with binding activity and 15 (42.86%) were involved in catalytic activity. Our findings provide an insight into the interaction between E. maxima and the corresponding host cells and it is important for the understanding of molecular mechanisms underlying E. maxima invasion.

Identifying potential survival strategies of HIV-1 through virus-host protein interaction networks

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Boucher Charles AB

2010-07-01

Full Text Available Abstract Background The National Institute of Allergy and Infectious Diseases has launched the HIV-1 Human Protein Interaction Database in an effort to catalogue all published interactions between HIV-1 and human proteins. In order to systematically investigate these interactions functionally and dynamically, we have constructed an HIV-1 human protein interaction network. This network was analyzed for important proteins and processes that are specific for the HIV life-cycle. In order to expose viral strategies, network motif analysis was carried out showing reoccurring patterns in virus-host dynamics. Results Our analyses show that human proteins interacting with HIV form a densely connected and central sub-network within the total human protein interaction network. The evaluation of this sub-network for connectivity and centrality resulted in a set of proteins essential for the HIV life-cycle. Remarkably, we were able to associate proteins involved in RNA polymerase II transcription with hubs and proteasome formation with bottlenecks. Inferred network motifs show significant over-representation of positive and negative feedback patterns between virus and host. Strikingly, such patterns have never been reported in combined virus-host systems. Conclusions HIV infection results in a reprioritization of cellular processes reflected by an increase in the relative importance of transcriptional machinery and proteasome formation. We conclude that during the evolution of HIV, some patterns of interaction have been selected for resulting in a system where virus proteins preferably interact with central human proteins for direct control and with proteasomal proteins for indirect control over the cellular processes. Finally, the patterns described by network motifs illustrate how virus and host interact with one another.

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

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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.

Protein-material interactions: From micro-to-nano scale

International Nuclear Information System (INIS)

Tsapikouni, Theodora S.; Missirlis, Yannis F.

2008-01-01

The article presents a survey on the significance of protein-material interactions, the mechanisms which control them and the techniques used for their study. Protein-surface interactions play a key role in regenerative medicine, drug delivery, biosensor technology and chromatography, while it is related to various undesired effects such as biofouling and bio-prosthetic malfunction. Although the effects of protein-surface interaction concern the micro-scale, being sometimes obvious even with bare eyes, they derive from biophysical events at the nano-scale. The sequential steps for protein adsorption involve events at the single biomolecule level and the forces driving or inhibiting protein adsorption act at the molecular level too. Following the scaling of protein-surface interactions, various techniques have been developed for their study both in the micro- and nano-scale. Protein labelling with radioisotopes or fluorescent probes, colorimetric assays and the quartz crystal microbalance were the first techniques used to monitor protein adsorption isotherms, while the surface force apparatus was used to measure the interaction forces between protein layers at the micro-scale. Recently, more elaborate techniques like total internal reflection fluorescence (TIRF), Fourier transform infrared spectroscopy (FTIR), surface plasmon resonance, Raman spectroscopy, ellipsometry and time of flight secondary ion mass spectrometry (ToF-SIMS) have been applied for the investigation of protein density, structure or orientation at the interfaces. However, a turning point in the study of protein interactions with the surfaces was the invention and the wide-spread use of atomic force microscopy (AFM) which can both image single protein molecules on surfaces and directly measure the interaction force

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

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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.

RAIN: RNA-protein Association and Interaction Networks

DEFF Research Database (Denmark)

Junge, Alexander; Refsgaard, Jan Christian; Garde, Christian

2017-01-01

is challenging due to data heterogeneity. Here, we present a database of ncRNA-RNA and ncRNA-protein interactions and its integration with the STRING database of protein-protein interactions. These ncRNA associations cover four organisms and have been established from curated examples, experimental data...

SCOWLP classification: Structural comparison and analysis of protein binding regions

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Anders Gerd

2008-01-01

Full Text Available Abstract Background Detailed information about protein interactions is critical for our understanding of the principles governing protein recognition mechanisms. The structures of many proteins have been experimentally determined in complex with different ligands bound either in the same or different binding regions. Thus, the structural interactome requires the development of tools to classify protein binding regions. A proper classification may provide a general view of the regions that a protein uses to bind others and also facilitate a detailed comparative analysis of the interacting information for specific protein binding regions at atomic level. Such classification might be of potential use for deciphering protein interaction networks, understanding protein function, rational engineering and design. Description Protein binding regions (PBRs might be ideally described as well-defined separated regions that share no interacting residues one another. However, PBRs are often irregular, discontinuous and can share a wide range of interacting residues among them. The criteria to define an individual binding region can be often arbitrary and may differ from other binding regions within a protein family. Therefore, the rational behind protein interface classification should aim to fulfil the requirements of the analysis to be performed. We extract detailed interaction information of protein domains, peptides and interfacial solvent from the SCOWLP database and we classify the PBRs of each domain family. For this purpose, we define a similarity index based on the overlapping of interacting residues mapped in pair-wise structural alignments. We perform our classification with agglomerative hierarchical clustering using the complete-linkage method. Our classification is calculated at different similarity cut-offs to allow flexibility in the analysis of PBRs, feature especially interesting for those protein families with conflictive binding regions

On the role of electrostatics on protein-protein interactions

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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

Time-resolved analysis of DNA-protein interactions in living cells by UV laser pulses.

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Nebbioso, Angela; Benedetti, Rosaria; Conte, Mariarosaria; Carafa, Vincenzo; De Bellis, Floriana; Shaik, Jani; Matarese, Filomena; Della Ventura, Bartolomeo; Gesuele, Felice; Velotta, Raffaele; Martens, Joost H A; Stunnenberg, Hendrik G; Altucci, Carlo; Altucci, Lucia

2017-09-15

Interactions between DNA and proteins are mainly studied through chemical procedures involving bi-functional reagents, mostly formaldehyde. Chromatin immunoprecipitation is used to identify the binding between transcription factors (TFs) and chromatin, and to evaluate the occurrence and impact of histone/DNA modifications. The current bottleneck in probing DNA-protein interactions using these approaches is caused by the fact that chemical crosslinkers do not discriminate direct and indirect bindings or short-lived chromatin occupancy. Here, we describe a novel application of UV laser-induced (L-) crosslinking and demonstrate that a combination of chemical and L-crosslinking is able to distinguish between direct and indirect DNA-protein interactions in a small number of living cells. The spatial and temporal dynamics of TF bindings to chromatin and their role in gene expression regulation may thus be assessed. The combination of chemical and L-crosslinking offers an exciting and unprecedented tool for biomedical applications.

The simulation approach to lipid-protein interactions.

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Paramo, Teresa; Garzón, Diana; Holdbrook, Daniel A; Khalid, Syma; Bond, Peter J

2013-01-01

The interactions between lipids and proteins are crucial for a range of biological processes, from the folding and stability of membrane proteins to signaling and metabolism facilitated by lipid-binding proteins. However, high-resolution structural details concerning functional lipid/protein interactions are scarce due to barriers in both experimental isolation of native lipid-bound complexes and subsequent biophysical characterization. The molecular dynamics (MD) simulation approach provides a means to complement available structural data, yielding dynamic, structural, and thermodynamic data for a protein embedded within a physiologically realistic, modelled lipid environment. In this chapter, we provide a guide to current methods for setting up and running simulations of membrane proteins and soluble, lipid-binding proteins, using standard atomistically detailed representations, as well as simplified, coarse-grained models. In addition, we outline recent studies that illustrate the power of the simulation approach in the context of biologically relevant lipid/protein interactions.

PARPs database: A LIMS systems for protein-protein interaction data mining or laboratory information management system

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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.

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

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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

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.

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.

Prediction of thermodynamic instabilities of protein solutions from simple protein–protein interactions

International Nuclear Information System (INIS)

D’Agostino, Tommaso; Solana, José Ramón; Emanuele, Antonio

2013-01-01

Highlights: ► We propose a model of effective protein–protein interaction embedding solvent effects. ► A previous square-well model is enhanced by giving to the interaction a free energy character. ► The temperature dependence of the interaction is due to entropic effects of the solvent. ► The validity of the original SW model is extended to entropy driven phase transitions. ► We get good fits for lysozyme and haemoglobin spinodal data taken from literature. - Abstract: Statistical thermodynamics of protein solutions is often studied in terms of simple, microscopic models of particles interacting via pairwise potentials. Such modelling can reproduce the short range structure of protein solutions at equilibrium and predict thermodynamics instabilities of these systems. We introduce a square well model of effective protein–protein interaction that embeds the solvent’s action. We modify an existing model [45] by considering a well depth having an explicit dependence on temperature, i.e. an explicit free energy character, thus encompassing the statistically relevant configurations of solvent molecules around proteins. We choose protein solutions exhibiting demixing upon temperature decrease (lysozyme, enthalpy driven) and upon temperature increase (haemoglobin, entropy driven). We obtain satisfactory fits of spinodal curves for both the two proteins without adding any mean field term, thus extending the validity of the original model. Our results underline the solvent role in modulating or stretching the interaction potential

Integral UBL domain proteins: a family of proteasome interacting proteins

DEFF Research Database (Denmark)

Hartmann-Petersen, Rasmus; Gordon, Colin

2004-01-01

The family of ubiquitin-like (UBL) domain proteins (UDPs) comprises a conserved group of proteins involved in a multitude of different cellular activities. However, recent studies on UBL-domain proteins indicate that these proteins appear to share a common property in their ability to interact...

Yeast Interacting Proteins Database: YOR302W, YOR047C [Yeast Interacting Proteins Database

Lifescience Database Archive (English)

Full Text Available rol of glucose-regulated gene expression; interacts with protein kinase Snf1p, glucose sensors Snf3p and Rgt...tein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding protein Spt1

Interactions of glycine betaine with proteins: insights from volume and compressibility measurements.

Science.gov (United States)

Shek, Yuen Lai; Chalikian, Tigran V

2013-01-29

We report the first application of volume and compressibility measurements to characterization of interactions between cosolvents (osmolytes) and globular proteins. Specifically, we measure the partial molar volumes and adiabatic compressibilities of cytochrome c, ribonuclease A, lysozyme, and ovalbumin in aqueous solutions of the stabilizing osmolyte glycine betaine (GB) at concentrations between 0 and 4 M. The fact that globular proteins do not undergo any conformational transitions in the presence of GB provides an opportunity to study the interactions of GB with proteins in their native states within the entire range of experimentally accessible GB concentrations. We analyze our resulting volumetric data within the framework of a statistical thermodynamic model in which each instance of GB interaction with a protein is viewed as a binding reaction that is accompanied by release of four water molecules. From this analysis, we calculate the association constants, k, as well as changes in volume, ΔV(0), and adiabatic compressibility, ΔK(S0), accompanying each GB-protein association event in an ideal solution. By comparing these parameters with similar characteristics determined for low-molecular weight analogues of proteins, we conclude that there are no significant cooperative effects involved in interactions of GB with any of the proteins studied in this work. We also evaluate the free energies of direct GB-protein interactions. The energetic properties of GB-protein association appear to scale with the size of the protein. For all proteins, the highly favorable change in free energy associated with direct protein-cosolvent interactions is nearly compensated by an unfavorable free energy of cavity formation (excluded volume effect), yielding a modestly unfavorable free energy for the transfer of a protein from water to a GB/water mixture.

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

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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.

Potato leafroll virus structural proteins manipulate overlapping, yet distinct protein interaction networks during infection.

Science.gov (United States)

DeBlasio, Stacy L; Johnson, Richard; Sweeney, Michelle M; Karasev, Alexander; Gray, Stewart M; MacCoss, Michael J; Cilia, Michelle

2015-06-01

Potato leafroll virus (PLRV) produces a readthrough protein (RTP) via translational readthrough of the coat protein amber stop codon. The RTP functions as a structural component of the virion and as a nonincorporated protein in concert with numerous insect and plant proteins to regulate virus movement/transmission and tissue tropism. Affinity purification coupled to quantitative MS was used to generate protein interaction networks for a PLRV mutant that is unable to produce the read through domain (RTD) and compared to the known wild-type PLRV protein interaction network. By quantifying differences in the protein interaction networks, we identified four distinct classes of PLRV-plant interactions: those plant and nonstructural viral proteins interacting with assembled coat protein (category I); plant proteins in complex with both coat protein and RTD (category II); plant proteins in complex with the RTD (category III); and plant proteins that had higher affinity for virions lacking the RTD (category IV). Proteins identified as interacting with the RTD are potential candidates for regulating viral processes that are mediated by the RTP such as phloem retention and systemic movement and can potentially be useful targets for the development of strategies to prevent infection and/or viral transmission of Luteoviridae species that infect important crop species. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Analysis of A549 cell proteome alteration in response to recombinant influenza A virus nucleoprotein and its interaction with cellular proteins, a preliminary study.

Science.gov (United States)

Kumar, D; Tiwari, K; Rajala, M S

Influenza A virus undergoes frequent changes of antigenicity and contributes to seasonal epidemics or unpredictable pandemics. Nucleoprotein, encoded by gene segment 5, is an internal protein of the virus and is conserved among strains of different host origins. In the current study, we analyzed the differentially expressed proteins in A549 cells transiently transfected with the recombinant nucleoprotein of influenza A virus by 2D gel electrophoresis. The resolved protein spots on gel were identified by MALDI-TOF/Mass spectrometry analysis. The majority of the host proteins detected to be differentially abundant in recombinant nucleoprotein-expressing cells as compared to vector-transfected cells are the proteins of metabolic pathways, glycolytic enzymes, molecular chaperones and cytoskeletal proteins. We further demonstrated the interaction of virus nucleoprotein with some of the identified host cellular proteins. In vitro binding assay carried out using the purified recombinant nucleoprotein (pET29a+NP-His) and A549 cell lysate confirmed the interaction between nucleoprotein and host proteins, such as alpha enolase 1, pyruvate kinase and β-actin. The preliminary data of our study provides the information on virus nucleoprotein interaction with proteins involved in glycolysis. However, studies are ongoing to understand the significance of these interactions in modulating the host factors during virus replication.

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.

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/.

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

Deciphering peculiar protein-protein interacting modules in Deinococcus radiodurans

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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.

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

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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.

Using a Specific RNA-Protein Interaction To Quench the Fluorescent RNA Spinach.

Science.gov (United States)

Roszyk, Laura; Kollenda, Sebastian; Hennig, Sven

2017-12-15

RNAs are involved in interaction networks with other biomolecules and are crucial for proper cell function. Yet their biochemical analysis remains challenging. For Förster Resonance Energy Transfer (FRET), a common tool to study such interaction networks, two interacting molecules have to be fluorescently labeled. "Spinach" is a genetically encodable RNA aptamer that starts to fluoresce upon binding of an organic molecule. Therefore, it is a biological fluorophore tag for RNAs. However, spinach has never been used in a FRET assembly before. Here, we describe how spinach is quenched when close to acceptors. We used RNA-DNA hybridization to bring quenchers or red organic dyes in close proximity to spinach. Furthermore, we investigate RNA-protein interactions quantitatively on the example of Pseudomonas aeruginosa phage coat protein 7 (PP7) and its interacting pp7-RNA. We utilize spinach quenching as a fully genetically encodable system even under lysate conditions. Therefore, this work represents a direct method to analyze RNA-protein interactions by quenching the spinach aptamer.

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.

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

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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.

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

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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.

A force-based, parallel assay for the quantification of protein-DNA interactions.

Science.gov (United States)

Limmer, Katja; Pippig, Diana A; Aschenbrenner, Daniela; Gaub, Hermann E

2014-01-01

Analysis of transcription factor binding to DNA sequences is of utmost importance to understand the intricate regulatory mechanisms that underlie gene expression. Several techniques exist that quantify DNA-protein affinity, but they are either very time-consuming or suffer from possible misinterpretation due to complicated algorithms or approximations like many high-throughput techniques. We present a more direct method to quantify DNA-protein interaction in a force-based assay. In contrast to single-molecule force spectroscopy, our technique, the Molecular Force Assay (MFA), parallelizes force measurements so that it can test one or multiple proteins against several DNA sequences in a single experiment. The interaction strength is quantified by comparison to the well-defined rupture stability of different DNA duplexes. As a proof-of-principle, we measured the interaction of the zinc finger construct Zif268/NRE against six different DNA constructs. We could show the specificity of our approach and quantify the strength of the protein-DNA interaction.

A force-based, parallel assay for the quantification of protein-DNA interactions.

Directory of Open Access Journals (Sweden)

Katja Limmer

Full Text Available Analysis of transcription factor binding to DNA sequences is of utmost importance to understand the intricate regulatory mechanisms that underlie gene expression. Several techniques exist that quantify DNA-protein affinity, but they are either very time-consuming or suffer from possible misinterpretation due to complicated algorithms or approximations like many high-throughput techniques. We present a more direct method to quantify DNA-protein interaction in a force-based assay. In contrast to single-molecule force spectroscopy, our technique, the Molecular Force Assay (MFA, parallelizes force measurements so that it can test one or multiple proteins against several DNA sequences in a single experiment. The interaction strength is quantified by comparison to the well-defined rupture stability of different DNA duplexes. As a proof-of-principle, we measured the interaction of the zinc finger construct Zif268/NRE against six different DNA constructs. We could show the specificity of our approach and quantify the strength of the protein-DNA interaction.

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

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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.

Instrumental biosensors: new perspectives for the analysis of biomolecular interactions.

Science.gov (United States)

Nice, E C; Catimel, B

1999-04-01

The use of instrumental biosensors in basic research to measure biomolecular interactions in real time is increasing exponentially. Applications include protein-protein, protein-peptide, DNA-protein, DNA-DNA, and lipid-protein interactions. Such techniques have been applied to, for example, antibody-antigen, receptor-ligand, signal transduction, and nuclear receptor studies. This review outlines the principles of two of the most commonly used instruments and highlights specific operating parameters that will assist in optimising experimental design, data generation, and analysis.

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

A protein domain interaction interface database: InterPare

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Lee Jungsul

2005-08-01

Full Text Available Abstract Background Most proteins function by interacting with other molecules. Their interaction interfaces are highly conserved throughout evolution to avoid undesirable interactions that lead to fatal disorders in cells. Rational drug discovery includes computational methods to identify the interaction sites of lead compounds to the target molecules. Identifying and classifying protein interaction interfaces on a large scale can help researchers discover drug targets more efficiently. Description We introduce a large-scale protein domain interaction interface database called InterPare http://interpare.net. It contains both inter-chain (between chains interfaces and intra-chain (within chain interfaces. InterPare uses three methods to detect interfaces: 1 the geometric distance method for checking the distance between atoms that belong to different domains, 2 Accessible Surface Area (ASA, a method for detecting the buried region of a protein that is detached from a solvent when forming multimers or complexes, and 3 the Voronoi diagram, a computational geometry method that uses a mathematical definition of interface regions. InterPare includes visualization tools to display protein interior, surface, and interaction interfaces. It also provides statistics such as the amino acid propensities of queried protein according to its interior, surface, and interface region. The atom coordinates that belong to interface, surface, and interior regions can be downloaded from the website. Conclusion InterPare is an open and public database server for protein interaction interface information. It contains the large-scale interface data for proteins whose 3D-structures are known. As of November 2004, there were 10,583 (Geometric distance, 10,431 (ASA, and 11,010 (Voronoi diagram entries in the Protein Data Bank (PDB containing interfaces, according to the above three methods. In the case of the geometric distance method, there are 31,620 inter-chain domain

(S)Pinning down protein interactions by NMR

DEFF Research Database (Denmark)

Teilum, Kaare; Kunze, Micha Ben Achim; Erlendsson, Simon

2017-01-01

Protein molecules are highly diverse communication platforms and their interaction repertoire stretches from atoms over small molecules such as sugars and lipids to macromolecules. An important route to understanding molecular communication is to quantitatively describe their interactions...... all types of protein reactions, which can span orders of magnitudes in affinities, reaction rates and lifetimes of states. As the more versatile technique, solution NMR spectroscopy offers a remarkable catalogue of methods that can be successfully applied to the quantitative as well as qualitative...... descriptions of protein interactions. In this review we provide an easy-access approach to NMR for the non-NMR specialist and describe how and when solution state NMR spectroscopy is the method of choice for addressing protein ligand interaction. We describe very briefly the theoretical background...

Protein Annotation from Protein Interaction Networks and Gene Ontology

OpenAIRE

Nguyen, Cao D.; Gardiner, Katheleen J.; Cios, Krzysztof J.

2011-01-01

We introduce a novel method for annotating protein function that combines Naïve Bayes and association rules, and takes advantage of the underlying topology in protein interaction networks and the structure of graphs in the Gene Ontology. We apply our method to proteins from the Human Protein Reference Database (HPRD) and show that, in comparison with other approaches, it predicts protein functions with significantly higher recall with no loss of precision. Specifically, it achieves 51% precis...

Defining the protein interaction network of human malaria parasite Plasmodium falciparum

KAUST Repository

Ramaprasad, Abhinay

2012-02-01

Malaria, caused by the protozoan parasite Plasmodium falciparum, affects around 225. million people yearly and a huge international effort is directed towards combating this grave threat to world health and economic development. Considerable advances have been made in malaria research triggered by the sequencing of its genome in 2002, followed by several high-throughput studies defining the malaria transcriptome and proteome. A protein-protein interaction (PPI) network seeks to trace the dynamic interactions between proteins, thereby elucidating their local and global functional relationships. Experimentally derived PPI network from high-throughput methods such as yeast two hybrid (Y2H) screens are inherently noisy, but combining these independent datasets by computational methods tends to give a greater accuracy and coverage. This review aims to discuss the computational approaches used till date to construct a malaria protein interaction network and to catalog the functional predictions and biological inferences made from analysis of the PPI network. © 2011 Elsevier Inc.