Dimerization in the Grb7 Protein

OpenAIRE

Peterson, Tabitha A.; Benallie, Renee L.; Bradford, Andrew M.; Pias, Sally C.; Yazzie, Jaron.; Lor, Siamee N.; Haulsee, Zachary M.; Park, Chad K.; Johnson, Dennis L.; Rohrschneider, Larry R.; Spuches, Anne.; Lyons, Barbara A.

2012-01-01

In previous studies, we showed that the tyrosine phosphorylation state of growth factor receptor–bound protein 7 (Grb7) affects its ability to bind to the transcription regulator FHL2 and the cortactin-interacting protein, human HS-1-associated protein-1. Here, we present results describing the importance of dimerization in the Grb7–Src homology 2 (SH2) domain in terms of its structural integrity and the ability to bind phosphorylated tyrosine peptide ligands. A tyrosine phosphorylation-mimic...

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

Science.gov (United States)

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

2006-11-15

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

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

Science.gov (United States)

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

2006-04-15

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

Interaction between IGFBP7 and insulin: a theoretical and experimental study

Science.gov (United States)

Ruan, Wenjing; Kang, Zhengzhong; Li, Youzhao; Sun, Tianyang; Wang, Lipei; Liang, Lijun; Lai, Maode; Wu, Tao

2016-04-01

Insulin-like growth factor binding protein 7 (IGFBP7) can bind to insulin with high affinity which inhibits the early steps of insulin action. Lack of recognition mechanism impairs our understanding of insulin regulation before it binds to insulin receptor. Here we combine computational simulations with experimental methods to investigate the interaction between IGFBP7 and insulin. Molecular dynamics simulations indicated that His200 and Arg198 in IGFBP7 were key residues. Verified by experimental data, the interaction remained strong in single mutation systems R198E and H200F but became weak in double mutation system R198E-H200F relative to that in wild-type IGFBP7. The results and methods in present study could be adopted in future research of discovery of drugs by disrupting protein-protein interactions in insulin signaling. Nevertheless, the accuracy, reproducibility, and costs of free-energy calculation are still problems that need to be addressed before computational methods can become standard binding prediction tools in discovery pipelines.

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

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Greenblatt Jack

2006-07-01

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

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

Science.gov (United States)

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

2015-01-01

suggesting that the proteins are natively folded and functional. This screen also identified two novel protein-protein interactions, between P12 and PVX_110945, and between MSP3.10 and MSP7.1, the latter of which was confirmed by surface plasmon resonance. Conclusions/Significance We produced a new library of recombinant full-length P. vivax ectodomains, established that the majority of them contain tertiary structure, and used them to identify predicted and novel protein-protein interactions. As well as identifying new interactions for further biological studies, this library will be useful in identifying P. vivax proteins with vaccine potential, and studying P. vivax malaria pathogenesis and immunity. Trial Registration ClinicalTrials.gov NCT00663546 PMID:26701602

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...

Molecular characterization of the 30-AA N-terminal mineral interaction domain of the biomineralization protein AP7.

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Kim, Il Won; Morse, Daniel E; Evans, John Spencer

2004-12-21

The AP7 protein is one of several mollusk shell proteins which are responsible for aragonite polymorph formation and stabilization within the nacre layer of the Pacific red abalone, H. rufescens. Previously, we demonstrated that the 30-AA N-terminal domain of AP7, denoted as AP7-1, exists as an unfolded sequence and possesses the capability of inhibiting calcium carbonate crystal growth in vitro via growth step frustration or interruption. However, very little is known with regard to the interactive capabilities of this sequence with Ca(II) and with calcium carbonates. Using multidisciplinary techniques, we determine that the AP7-1 polypeptide interacts with Ca(II) ions at the -DD- sequence clusters, yet retains its unfolded, conformationally labile structure in the presence of Ca(II) ions. Further, NMR experiments reveal that the extended structured sequence blocks, -GNGM-, -SVRTQG-, and -ISYL, exhibit motional, chemical exchange, and/or backbone geometry perturbations in response to Ca(II) interactions with AP7-1. Solid-state NMR magic angle spinning studies verify that during the course of in vitro calcium carbonate crystal growth, AP7-1 becomes bound to calcite fragments and cannot be entirely displaced from the mineral fragments using competitive Ca(II) washing. Finally, using a scrambled sequence version of the AP7-1 polypeptide, we observe that sequence scrambling does not adversely affect the crystal growth inhibitory activity of AP7-1, suggesting that the amino acid composition of AP7-1 may be more critical to growth step inhibition than the linear ordering of amino acids.

Interactions between whey proteins and kaolinite surfaces

Energy Technology Data Exchange (ETDEWEB)

Barral, S. [Department of Chemical Engineering and Environmental Technology, University of Oviedo, Julian Claveria 8, 33006 Oviedo (Spain); Villa-Garcia, M.A. [Department of Organic and Inorganic Chemistry, University of Oviedo, Julian Claveria 8, 33006 Oviedo (Spain)], E-mail: mavg@uniovi.es; Rendueles, M. [Project Management Area, University of Oviedo, Independencia 13, 33004 Oviedo (Spain); Diaz, M. [Department of Chemical Engineering and Environmental Technology, University of Oviedo, Julian Claveria 8, 33006 Oviedo (Spain)

2008-07-15

The nature of the interactions between whey proteins and kaolinite surfaces was investigated by adsorption-desorption experiments at room temperature, performed at the isoelectric point (IEP) of the proteins and at pH 7. It was found that kaolinite is a strong adsorbent for proteins, reaching the maximum adsorption capacity at the IEP of each protein. At pH 7.0, the retention capacity decreased considerably. The adsorption isotherms showed typical Langmuir characteristics. X-ray diffraction data for the protein-kaolinite complexes showed that protein molecules were not intercalated in the mineral structure, but immobilized at the external surfaces and the edges of the kaolinite. Fourier transform IR results indicate the absence of hydrogen bonding between kaolinite surfaces and the polypeptide chain. The adsorption patterns appear to be related to electrostatic interactions, although steric effects should be also considered.

Interactions between whey proteins and kaolinite surfaces

International Nuclear Information System (INIS)

Barral, S.; Villa-Garcia, M.A.; Rendueles, M.; Diaz, M.

2008-01-01

The nature of the interactions between whey proteins and kaolinite surfaces was investigated by adsorption-desorption experiments at room temperature, performed at the isoelectric point (IEP) of the proteins and at pH 7. It was found that kaolinite is a strong adsorbent for proteins, reaching the maximum adsorption capacity at the IEP of each protein. At pH 7.0, the retention capacity decreased considerably. The adsorption isotherms showed typical Langmuir characteristics. X-ray diffraction data for the protein-kaolinite complexes showed that protein molecules were not intercalated in the mineral structure, but immobilized at the external surfaces and the edges of the kaolinite. Fourier transform IR results indicate the absence of hydrogen bonding between kaolinite surfaces and the polypeptide chain. The adsorption patterns appear to be related to electrostatic interactions, although steric effects should be also considered

Detecting mutually exclusive interactions in protein-protein interaction maps.

KAUST Repository

Sánchez Claros, Carmen

2012-06-08

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

Detecting mutually exclusive interactions in protein-protein interaction maps.

KAUST Repository

Sá nchez Claros, Carmen; Tramontano, Anna

2012-01-01

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

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

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Albrecht von Brunn

2007-05-01

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

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

Science.gov (United States)

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

2015-12-01

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

Rice black streaked dwarf virus P7-2 forms a SCF complex through binding to Oryza sativa SKP1-like proteins, and interacts with GID2 involved in the gibberellin pathway.

Science.gov (United States)

Tao, Tao; Zhou, Cui-Ji; Wang, Qian; Chen, Xiang-Ru; Sun, Qian; Zhao, Tian-Yu; Ye, Jian-Chun; Wang, Ying; Zhang, Zong-Ying; Zhang, Yong-Liang; Guo, Ze-Jian; Wang, Xian-Bing; Li, Da-Wei; Yu, Jia-Lin; Han, Cheng-Gui

2017-01-01

As a core subunit of the SCF complex that promotes protein degradation through the 26S proteasome, S-phase kinase-associated protein 1 (SKP1) plays important roles in multiple cellular processes in eukaryotes, including gibberellin (GA), jasmonate, ethylene, auxin and light responses. P7-2 encoded by Rice black streaked dwarf virus (RBSDV), a devastating viral pathogen that causes severe symptoms in infected plants, interacts with SKP1 from different plants. However, whether RBSDV P7-2 forms a SCF complex and targets host proteins is poorly understood. In this study, we conducted yeast two-hybrid assays to further explore the interactions between P7-2 and 25 type I Oryza sativa SKP1-like (OSK) proteins, and found that P7-2 interacted with eight OSK members with different binding affinity. Co-immunoprecipitation assay further confirmed the interaction of P7-2 with OSK1, OSK5 and OSK20. It was also shown that P7-2, together with OSK1 and O. sativa Cullin-1, was able to form the SCF complex. Moreover, yeast two-hybrid assays revealed that P7-2 interacted with gibberellin insensitive dwarf2 (GID2) from rice and maize plants, which is essential for regulating the GA signaling pathway. It was further demonstrated that the N-terminal region of P7-2 was necessary for the interaction with GID2. Overall, these results indicated that P7-2 functioned as a component of the SCF complex in rice, and interaction of P7-2 with GID2 implied possible roles of the GA signaling pathway during RBSDV infection.

Rice black streaked dwarf virus P7-2 forms a SCF complex through binding to Oryza sativa SKP1-like proteins, and interacts with GID2 involved in the gibberellin pathway.

Directory of Open Access Journals (Sweden)

Tao Tao

Full Text Available As a core subunit of the SCF complex that promotes protein degradation through the 26S proteasome, S-phase kinase-associated protein 1 (SKP1 plays important roles in multiple cellular processes in eukaryotes, including gibberellin (GA, jasmonate, ethylene, auxin and light responses. P7-2 encoded by Rice black streaked dwarf virus (RBSDV, a devastating viral pathogen that causes severe symptoms in infected plants, interacts with SKP1 from different plants. However, whether RBSDV P7-2 forms a SCF complex and targets host proteins is poorly understood. In this study, we conducted yeast two-hybrid assays to further explore the interactions between P7-2 and 25 type I Oryza sativa SKP1-like (OSK proteins, and found that P7-2 interacted with eight OSK members with different binding affinity. Co-immunoprecipitation assay further confirmed the interaction of P7-2 with OSK1, OSK5 and OSK20. It was also shown that P7-2, together with OSK1 and O. sativa Cullin-1, was able to form the SCF complex. Moreover, yeast two-hybrid assays revealed that P7-2 interacted with gibberellin insensitive dwarf2 (GID2 from rice and maize plants, which is essential for regulating the GA signaling pathway. It was further demonstrated that the N-terminal region of P7-2 was necessary for the interaction with GID2. Overall, these results indicated that P7-2 functioned as a component of the SCF complex in rice, and interaction of P7-2 with GID2 implied possible roles of the GA signaling pathway during RBSDV infection.

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.

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.

Binding of MCM-interacting proteins to ATP-binding site in MCM6

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Hosoi A

2016-03-01

Full Text Available Atsutoshi Hosoi, Taku Sakairi, Yukio Ishimi Graduate School of Science and Engineering, Ibaraki University, Mito, Ibaraki, Japan Abstract: The function of MCM2–7 complex that is a DNA helicase in DNA replication may be regulated by various MCM-interacting proteins, including CDC45, RPA, TIM, TIPIN, Claspin, MCM10, and MCM-BP. It has been shown by immunoprecipitation that human MCM6 interacts with all these proteins in coexpressed insect cells. To determine the region in MCM6 to interact with these proteins, we prepared various truncated forms of MCM6 and examined the interaction of these MCM6 fragments with the MCM-interacting proteins. All these proteins bound to C-terminal half of MCM6, and CDC45, RPA2, TIM, TIPIN, MCM-BP, and MCM10 bound to the fragments containing ATP-binding motifs. CDC45 and RPA2 bound to the smallest fragment containing Walker motif A. Only MCM-BP is bound to the N-terminal half of MCM6. Site-directed mutagenesis study suggests that hydrophobic interaction is involved in the interaction of MCM6 with CDC45 and TIM. These results suggest a possibility that MCM-interacting proteins regulate MCM2–7 function by modulating the ATP-binding ability of the MCM2–7. Keywords: DNA helicase, DNA replication, checkpoint, MCM2–7 proteins

Molecular Docking Explains Atomic Interaction between Plant-originated Ligands and Oncogenic E7 Protein of High Risk Human Papillomavirus Type 16

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Satish Kumar

2014-12-01

Full Text Available Cervical cancer caused by Human papillomavirus (HPV is one of the leading causes of cancer mortality in women worldwide, particularly in the developing countries. In the last few decades, various compounds from plant origin such as Curcumin, Epigallocatechin gallate (EGCG, Jaceosidin, Resveratrol etc. have been used as anti cancer therapeutic agents. Different studies have shown these plant-originated compounds are able to suppress HPV infection. The E6 and E7 oncoproteins of high-risk HPV play a key role in HPV related cancers. In this study, we explored these ligands from plants origin against E7 oncoprotein of high risk HPV 16, which is known to inactivate tumor suppressor pRb protein. A robust homology model of HPV 16 E7 was built to foresee the interaction mechanism of E7 oncoprotein with these ligands using structure-based drug designing approach. Docking studies demonstrate the interaction of these ligands with pRb binding site of E7 protein by residues Tyr52, Asn53, Val55, Phe57, Cys59, Ser63, Thr64, Thr72, Arg77, Glu80 and Asp81 and help restoration of pRb functioning. This in silico based atomic interaction between these ligands and E7 protein may assist in validating the plant-originated ligands as effective drugs against HPV.

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

Energy Technology Data Exchange (ETDEWEB)

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

2015-07-31

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

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

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Susan Khor

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

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

Science.gov (United States)

Khor, Susan

2014-01-01

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

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.

Genetic interaction network of the Saccharomyces cerevisiae type 1 phosphatase Glc7

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Neszt Michael

2008-07-01

Full Text Available Abstract Background Protein kinases and phosphatases regulate protein phosphorylation, a critical means of modulating protein function, stability and localization. The identification of functional networks for protein phosphatases has been slow due to their redundant nature and the lack of large-scale analyses. We hypothesized that a genome-scale analysis of genetic interactions using the Synthetic Genetic Array could reveal protein phosphatase functional networks. We apply this approach to the conserved type 1 protein phosphatase Glc7, which regulates numerous cellular processes in budding yeast. Results We created a novel glc7 catalytic mutant (glc7-E101Q. Phenotypic analysis indicates that this novel allele exhibits slow growth and defects in glucose metabolism but normal cell cycle progression and chromosome segregation. This suggests that glc7-E101Q is a hypomorphic glc7 mutant. Synthetic Genetic Array analysis of glc7-E101Q revealed a broad network of 245 synthetic sick/lethal interactions reflecting that many processes are required when Glc7 function is compromised such as histone modification, chromosome segregation and cytokinesis, nutrient sensing and DNA damage. In addition, mitochondrial activity and inheritance and lipid metabolism were identified as new processes involved in buffering Glc7 function. An interaction network among 95 genes genetically interacting with GLC7 was constructed by integration of genetic and physical interaction data. The obtained network has a modular architecture, and the interconnection among the modules reflects the cooperation of the processes buffering Glc7 function. Conclusion We found 245 genes required for the normal growth of the glc7-E101Q mutant. Functional grouping of these genes and analysis of their physical and genetic interaction patterns bring new information on Glc7-regulated processes.

Interactions of cullin3/KCTD5 complexes with both cytoplasmic and nuclear proteins: Evidence for a role in protein stabilization

Energy Technology Data Exchange (ETDEWEB)

Rutz, Natalja; Heilbronn, Regine; Weger, Stefan, E-mail: stefan.weger@charite.de

2015-08-28

Based on its specific interaction with cullin3 mediated by an N-terminal BTB/POZ homologous domain, KCTD5 has been proposed to function as substrate adapter for cullin3 based ubiquitin E3 ligases. In the present study we tried to validate this hypothesis through identification and characterization of additional KCTD5 interaction partners. For the replication protein MCM7, the zinc finger protein ZNF711 and FAM193B, a yet poorly characterized cytoplasmic protein, we could demonstrate specific interaction with KCTD5 both in yeast two-hybrid and co-precipitation studies in mammalian cells. Whereas trimeric complexes of cullin3 and KCTD5 with the respective KCTD5 binding partner were formed, KCTD5/cullin3 induced polyubiquitylation and/or proteasome-dependent degradation of these binding partners could not be demonstrated. On the contrary, KCTD5 or Cullin3 overexpression increased ZNF711 protein stability. - Highlights: • KCTD5 nuclear translocation depends upon M phase and protein oligomerization. • Identification of MCM7, ZNF711 and FAM193 as KCTD5 interaction partners. • Formation of trimeric complexes of KCTD5/cullin3 with MCM7, ZNF711 and FAM193B. • KCTD5 is not involved in polyubiquitylation of MCM7 replication factor. • The KCTD5/cullin3 complex stabilizes ZNF711 transcription factor.

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

The alpha-fetoprotein (AFP) third domain: a search for AFP interaction sites of cell cycle proteins.

Science.gov (United States)

Mizejewski, G J

2016-09-01

The carboxy-terminal third domain of alpha-fetoprotein (AFP-3D) is known to harbor binding and/or interaction sites for hydrophobic ligands, receptors, and binding proteins. Such reports have established that AFP-3D consists of amino acid (AA) sequence stretches on the AFP polypeptide that engages in protein-to-protein interactions with various ligands and receptors. Using a computer software program specifically designed for such interactions, the present report identified AA sequence fragments on AFP-3D that could potentially interact with a variety of cell cycle proteins. The cell cycle proteins identified were (1) cyclins, (2) cyclin-dependent kinases, (3) cell cycle-associated proteins (inhibitors, checkpoints, initiators), and (4) ubiquitin ligases. Following detection of the AFP-3D to cell cycle protein interaction sites, the computer-derived AFP localization AA sequences were compared and aligned with previously reported hydrophobic ligand and receptor interaction sites on AFP-3D. A literature survey of the association of cell cycle proteins with AFP showed both positive relationships and correlations. Previous reports of experimental AFP-derived peptides effects on various cell cycle proteins served to confirm and verify the present computer cell cycle protein identifications. Cell cycle protein interactions with AFP-CD peptides have been reported in cultured MCF-7 breast cancer cells subjected to mRNA microarray analysis. After 7 days in culture with MCF-7 cells, the AFP-derived peptides were shown to downregulate cyclin E, SKP2, checkpoint suppressors, cyclin-dependent kinases, and ubiquitin ligases that modulate cyclin E/CdK2 transition from the G1 to the S-phase of the cell cycle. Thus, the experimental data on AFP-CD interaction with cell cycle proteins were consistent with the "in silico" findings.

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.

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

Aedes aegypti D7 Saliva Protein Inhibits Dengue Virus Infection.

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Michael J Conway

2016-09-01

Full Text Available Aedes aegypti is the primary vector of several medically relevant arboviruses including dengue virus (DENV types 1-4. Ae. aegypti transmits DENV by inoculating virus-infected saliva into host skin during probing and feeding. Ae. aegypti saliva contains over one hundred unique proteins and these proteins have diverse functions, including facilitating blood feeding. Previously, we showed that Ae. aegypti salivary gland extracts (SGEs enhanced dissemination of DENV to draining lymph nodes. In contrast, HPLC-fractionation revealed that some SGE components inhibited infection. Here, we show that D7 proteins are enriched in HPLC fractions that are inhibitory to DENV infection, and that recombinant D7 protein can inhibit DENV infection in vitro and in vivo. Further, binding assays indicate that D7 protein can directly interact with DENV virions and recombinant DENV envelope protein. These data reveal a novel role for D7 proteins, which inhibits arbovirus transmission to vertebrates through a direct interaction with virions.

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.

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.

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

Science.gov (United States)

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

2017-01-01

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

The nonstructural protein 8 (nsp8) of the SARS coronavirus interacts with its ORF6 accessory protein

International Nuclear Information System (INIS)

Kumar, Purnima; Gunalan, Vithiagaran; Liu Boping; Chow, Vincent T.K.; Druce, Julian; Birch, Chris; Catton, Mike; Fielding, Burtram C.; Tan, Yee-Joo; Lal, Sunil K.

2007-01-01

Severe acute respiratory syndrome (SARS) coronavirus (SARS-CoV) caused a severe outbreak in several regions of the world in 2003. The SARS-CoV genome is predicted to contain 14 functional open reading frames (ORFs). The first ORF (1a and 1b) encodes a large polyprotein that is cleaved into nonstructural proteins (nsp). The other ORFs encode for four structural proteins (spike, membrane, nucleocapsid and envelope) as well as eight SARS-CoV-specific accessory proteins (3a, 3b, 6, 7a, 7b, 8a, 8b and 9b). In this report we have cloned the predicted nsp8 gene and the ORF6 gene of the SARS-CoV and studied their abilities to interact with each other. We expressed the two proteins as fusion proteins in the yeast two-hybrid system to demonstrate protein-protein interactions and tested the same using a yeast genetic cross. Further the strength of the interaction was measured by challenging growth of the positive interaction clones on increasing gradients of 2-amino trizole. The interaction was then verified by expressing both proteins separately in-vitro in a coupled-transcription translation system and by coimmunoprecipitation in mammalian cells. Finally, colocalization experiments were performed in SARS-CoV infected Vero E6 mammalian cells to confirm the nsp8-ORF6 interaction. To the best of our knowledge, this is the first report of the interaction between a SARS-CoV accessory protein and nsp8 and our findings suggest that ORF6 protein may play a role in virus replication

Specificity and affinity quantification of protein-protein interactions.

Science.gov (United States)

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

2013-05-01

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

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.

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

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...

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

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...

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

Science.gov (United States)

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

2018-04-01

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

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

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. .

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

Science.gov (United States)

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

2013-10-04

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

Protein- protein interaction detection system using fluorescent protein microdomains

Science.gov (United States)

Waldo, Geoffrey S.; Cabantous, Stephanie

2010-02-23

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

Evolutionary reprograming of protein-protein interaction specificity.

Science.gov (United States)

Akiva, Eyal; Babbitt, Patricia C

2015-10-22

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

Whirlin and PDZ domain-containing 7 (PDZD7) proteins are both required to form the quaternary protein complex associated with Usher syndrome type 2.

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Chen, Qian; Zou, Junhuang; Shen, Zuolian; Zhang, Weiping; Yang, Jun

2014-12-26

Usher syndrome (USH) is the leading genetic cause of combined hearing and vision loss. Among the three USH clinical types, type 2 (USH2) occurs most commonly. USH2A, GPR98, and WHRN are three known causative genes of USH2, whereas PDZD7 is a modifier gene found in USH2 patients. The proteins encoded by these four USH genes have been proposed to form a multiprotein complex, the USH2 complex, due to interactions found among some of these proteins in vitro, their colocalization in vivo, and mutual dependence of some of these proteins for their normal in vivo localizations. However, evidence showing the formation of the USH2 complex is missing, and details on how this complex is formed remain elusive. Here, we systematically investigated interactions among the intracellular regions of the four USH proteins using colocalization, yeast two-hybrid, and pull-down assays. We show that multiple domains of the four USH proteins interact among one another. Importantly, both WHRN and PDZD7 are required for the complex formation with USH2A and GPR98. In this USH2 quaternary complex, WHRN prefers to bind to USH2A, whereas PDZD7 prefers to bind to GPR98. Interaction between WHRN and PDZD7 is the bridge between USH2A and GPR98. Additionally, the USH2 quaternary complex has a variable stoichiometry. These findings suggest that a non-obligate, short term, and dynamic USH2 quaternary protein complex may exist in vivo. Our work provides valuable insight into the physiological role of the USH2 complex in vivo and informs possible reconstruction of the USH2 complex for future therapy. © 2014 by The American Society for Biochemistry and Molecular Biology, Inc.

Bioinformatic Prediction of WSSV-Host Protein-Protein Interaction

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Zheng Sun

2014-01-01

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

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.

A Mesoscopic Model for Protein-Protein Interactions in Solution

OpenAIRE

Lund, Mikael; Jönsson, Bo

2003-01-01

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

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

Science.gov (United States)

Park, Byungkyu; Han, Kyungsook

2010-01-18

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

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.

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...

Oligomeric protein structure networks: insights into protein-protein interactions

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Brinda KV

2005-12-01

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

The Protein Interaction Network of Bacteriophage Lambda with Its Host, Escherichia coli

Science.gov (United States)

Blasche, Sonja; Wuchty, Stefan; Rajagopala, Seesandra V.

2013-01-01

Although most of the 73 open reading frames (ORFs) in bacteriophage λ have been investigated intensively, the function of many genes in host-phage interactions remains poorly understood. Using yeast two-hybrid screens of all lambda ORFs for interactions with its host Escherichia coli, we determined a raw data set of 631 host-phage interactions resulting in a set of 62 high-confidence interactions after multiple rounds of retesting. These links suggest novel regulatory interactions between the E. coli transcriptional network and lambda proteins. Targeted host proteins and genes required for lambda infection are enriched among highly connected proteins, suggesting that bacteriophages resemble interaction patterns of human viruses. Lambda tail proteins interact with both bacterial fimbrial proteins and E. coli proteins homologous to other phage proteins. Lambda appears to dramatically differ from other phages, such as T7, because of its unusually large number of modified and processed proteins, which reduces the number of host-virus interactions detectable by yeast two-hybrid screens. PMID:24049175

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

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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/.

Lipid droplet-binding protein TIP47 regulates hepatitis C Virus RNA replication through interaction with the viral NS5A protein.

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Dorothee A Vogt

Full Text Available The nonstructural protein NS5A has emerged as a new drug target in antiviral therapies for Hepatitis C Virus (HCV infection. NS5A is critically involved in viral RNA replication that takes place at newly formed membranes within the endoplasmic reticulum (membranous web and assists viral assembly in the close vicinity of lipid droplets (LDs. To identify host proteins that interact with NS5A, we performed a yeast two-hybrid screen with the N-terminus of NS5A (amino acids 1-31, a well-studied α-helical domain important for the membrane tethering of NS5A. Our studies identified the LD-associated host protein, Tail-Interacting Protein 47 (TIP47 as a novel NS5A interaction partner. Coimmunoprecipitation experiments in Huh7 hepatoma cells confirmed the interaction of TIP47 with full-length NS5A. shRNA-mediated knockdown of TIP47 caused a more than 10-fold decrease in the propagation of full-length infectious HCV in Huh7.5 hepatoma cells. A similar reduction was observed when TIP47 was knocked down in cells harboring an autonomously replicating HCV RNA (subgenomic replicon, indicating that TIP47 is required for efficient HCV RNA replication. A single point mutation (W9A in NS5A that disrupts the interaction with TIP47 but preserves proper subcellular localization severely decreased HCV RNA replication. In biochemical membrane flotation assays, TIP47 cofractionated with HCV NS3, NS5A, NS5B proteins, and viral RNA, and together with nonstructural viral proteins was uniquely distributed to lower-density LD-rich membrane fractions in cells actively replicating HCV RNA. Collectively, our data support a model where TIP47--via its interaction with NS5A--serves as a novel cofactor for HCV infection possibly by integrating LD membranes into the membranous web.

Multiplex single-molecule interaction profiling of DNA barcoded proteins

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Gu, Liangcai; Li, Chao; Aach, John; Hill, David E.; Vidal, Marc; Church, George M.

2014-01-01

In contrast with advances in massively parallel DNA sequencing1, high-throughput protein analyses2-4 are often limited by ensemble measurements, individual analyte purification and hence compromised quality and cost-effectiveness. Single-molecule (SM) protein detection achieved using optical methods5 is limited by the number of spectrally nonoverlapping chromophores. Here, we introduce a single molecular interaction-sequencing (SMI-Seq) technology for parallel protein interaction profiling leveraging SM advantages. DNA barcodes are attached to proteins collectively via ribosome display6 or individually via enzymatic conjugation. Barcoded proteins are assayed en masse in aqueous solution and subsequently immobilized in a polyacrylamide (PAA) thin film to construct a random SM array, where barcoding DNAs are amplified into in situ polymerase colonies (polonies)7 and analyzed by DNA sequencing. This method allows precise quantification of various proteins with a theoretical maximum array density of over one million polonies per square millimeter. Furthermore, protein interactions can be measured based on the statistics of colocalized polonies arising from barcoding DNAs of interacting proteins. Two demanding applications, G-protein coupled receptor (GPCR) and antibody binding profiling, were demonstrated. SMI-Seq enables “library vs. library” screening in a one-pot assay, simultaneously interrogating molecular binding affinity and specificity. PMID:25252978

Molecular screening of compounds to the predicted Protein-Protein Interaction site of Rb1-E7 with p53- E6 in HPV

Science.gov (United States)

Shaikh, Faraz; Sanehi, Parvish; Rawal, Rakesh

2012-01-01

Cervical cancer is malignant neoplasm of the cervix uteri or cervical area. Human Papillomaviruses (HPVs) which are heterogeneous groups of small double stranded DNA viruses are considered as the primary cause of cervical cancer, involved in 90% of all Cervical Cancers. Two early HPV genes, E6 and E7, are known to play crucial role in tumor formation. E6 binds with p53 and prevents its translocation and thereby inhibit the ability of p53 to activate or repress target genes. E7 binds to hypophosphorylated Rb and thereby induces cells to enter into premature S-phase by disrupting Rb-E2F complexes. The strategy of the research work was to target the site of interaction of Rb1 -E7 & p53-E6. A total of 88 compounds were selected for molecular screening, based on comprehensive literature survey for natural compounds with anti-cancer activity. Molecular docking analysis was carried out with Molegro Virtual Docker, to screen the 88 chosen compounds and rank them according to their binding affinity towards the site of interaction of the viral oncoproteins and human tumor suppressor proteins. The docking result revealed that Nicandrenone a member of Withanolides family of chemical compounds as the most likely molecule that can be used as a candidate drug against HPV induced cervical cancer. Abbreviations HPV - Human Papiloma Virus, HTSP - Human Tumor Suppressor Proteins, VOP - Viral oncoproteins. PMID:22829740

The Sso7d protein of Sulfolobus solfataricus: in vitro relationship among different activities

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Annamaria Guagliardi

2002-01-01

Full Text Available The physiological role of the nonspecific DNA-binding protein Sso7d from the crenarchaeon Sulfolobus solfataricus is unknown. In vitro studies have shown that Sso7d promotes annealing of complementary DNA strands (Guagliardi et al. 1997, induces negative supercoiling (Lopez-Garcia et al. 1998, and chaperones the disassembly and renaturation of protein aggregates in an ATP hydrolysis-dependent manner (Guagliardi et al. 2000. In this study, we examined the relationships among the binding of Sso7d to double-stranded DNA, its interaction with protein aggregates, and its ATPase activity. Experiments with 1-anilinonaphthalene-8-sulfonic acid as probe demonstrated that exposed hydrophobic surfaces in Sso7d are responsible for interactions with protein aggregates and double-stranded DNA, whereas the site of ATPase activity has a non-hydrophobic character. The interactions of Sso7d with double-stranded DNA and with protein aggregates are mutually exclusive events, suggesting that the disassembly activity and the DNA-related activities of Sso7d may be competitive in vivo. In contrast, the hydrolysis of ATP by Sso7d is independent of the binding of Sso7d to double-stranded DNA or protein aggregates.

Protein structure modelling and evaluation based on a 4-distance description of side-chain interactions

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Inbar Yuval

2010-07-01

Full Text Available Abstract Background Accurate evaluation and modelling of residue-residue interactions within and between proteins is a key aspect of computational structure prediction including homology modelling, protein-protein docking, refinement of low-resolution structures, and computational protein design. Results Here we introduce a method for accurate protein structure modelling and evaluation based on a novel 4-distance description of residue-residue interaction geometry. Statistical 4-distance preferences were extracted from high-resolution protein structures and were used as a basis for a knowledge-based potential, called Hunter. We demonstrate that 4-distance description of side chain interactions can be used reliably to discriminate the native structure from a set of decoys. Hunter ranked the native structure as the top one in 217 out of 220 high-resolution decoy sets, in 25 out of 28 "Decoys 'R' Us" decoy sets and in 24 out of 27 high-resolution CASP7/8 decoy sets. The same concept was applied to side chain modelling in protein structures. On a set of very high-resolution protein structures the average RMSD was 1.47 Å for all residues and 0.73 Å for buried residues, which is in the range of attainable accuracy for a model. Finally, we show that Hunter performs as good or better than other top methods in homology modelling based on results from the CASP7 experiment. The supporting web site http://bioinfo.weizmann.ac.il/hunter/ was developed to enable the use of Hunter and for visualization and interactive exploration of 4-distance distributions. Conclusions Our results suggest that Hunter can be used as a tool for evaluation and for accurate modelling of residue-residue interactions in protein structures. The same methodology is applicable to other areas involving high-resolution modelling of biomolecules.

A lock-and-key model for protein–protein interactions

OpenAIRE

Morrison, Julie L.; Breitling, Rainer; Higham, Desmond J.; Gilbert, David R.

2006-01-01

Motivation: Protein–protein interaction networks are one of the major post-genomic data sources available to molecular biologists. They provide a comprehensive view of the global interaction structure of an organism’s proteome, as well as detailed information on specific interactions. Here we suggest a physical model of protein interactions that can be used to extract additional information at an intermediate level: It enables us to identify proteins which share biological interaction motifs,...

Can infrared spectroscopy provide information on protein-protein interactions?

Science.gov (United States)

Haris, Parvez I

2010-08-01

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

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.

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.

Force spectroscopy studies on protein-ligand interactions: a single protein mechanics perspective.

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Hu, Xiaotang; Li, Hongbin

2014-10-01

Protein-ligand interactions are ubiquitous and play important roles in almost every biological process. The direct elucidation of the thermodynamic, structural and functional consequences of protein-ligand interactions is thus of critical importance to decipher the mechanism underlying these biological processes. A toolbox containing a variety of powerful techniques has been developed to quantitatively study protein-ligand interactions in vitro as well as in living systems. The development of atomic force microscopy-based single molecule force spectroscopy techniques has expanded this toolbox and made it possible to directly probe the mechanical consequence of ligand binding on proteins. Many recent experiments have revealed how ligand binding affects the mechanical stability and mechanical unfolding dynamics of proteins, and provided mechanistic understanding on these effects. The enhancement effect of mechanical stability by ligand binding has been used to help tune the mechanical stability of proteins in a rational manner and develop novel functional binding assays for protein-ligand interactions. Single molecule force spectroscopy studies have started to shed new lights on the structural and functional consequence of ligand binding on proteins that bear force under their biological settings. Copyright © 2014 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

Biospecific protein immobilization for rapid analysis of weak protein interactions using self-interaction nanoparticle spectroscopy.

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Bengali, Aditya N; Tessier, Peter M

2009-10-01

"Reversible" protein interactions govern diverse biological behavior ranging from intracellular transport and toxic protein aggregation to protein crystallization and inactivation of protein therapeutics. Much less is known about weak protein interactions than their stronger counterparts since they are difficult to characterize, especially in a parallel format (in contrast to a sequential format) necessary for high-throughput screening. We have recently introduced a highly efficient approach of characterizing protein self-association, namely self-interaction nanoparticle spectroscopy (SINS; Tessier et al., 2008; J Am Chem Soc 130:3106-3112). This approach exploits the separation-dependent optical properties of gold nanoparticles to detect weak self-interactions between proteins immobilized on nanoparticles. A limitation of our previous work is that differences in the sequence and structure of proteins can lead to significant differences in their affinity to adsorb to nanoparticle surfaces, which complicates analysis of the corresponding protein self-association behavior. In this work we demonstrate a highly specific approach for coating nanoparticles with proteins using biotin-avidin interactions to generate protein-nanoparticle conjugates that report protein self-interactions through changes in their optical properties. Using lysozyme as a model protein that is refractory to characterization by conventional SINS, we demonstrate that surface Plasmon wavelengths for gold-avidin-lysozyme conjugates over a range of solution conditions (i.e., pH and ionic strength) are well correlated with lysozyme osmotic second virial coefficient measurements. Since SINS requires orders of magnitude less protein and time than conventional methods (e.g., static light scattering), we envision this approach will find application in large screens of protein self-association aimed at either preventing (e.g., protein aggregation) or promoting (e.g., protein crystallization) these

Predicting the binding patterns of hub proteins: a study using yeast protein interaction networks.

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Carson M Andorf

Full Text Available Protein-protein interactions are critical to elucidating the role played by individual proteins in important biological pathways. Of particular interest are hub proteins that can interact with large numbers of partners and often play essential roles in cellular control. Depending on the number of binding sites, protein hubs can be classified at a structural level as singlish-interface hubs (SIH with one or two binding sites, or multiple-interface hubs (MIH with three or more binding sites. In terms of kinetics, hub proteins can be classified as date hubs (i.e., interact with different partners at different times or locations or party hubs (i.e., simultaneously interact with multiple partners.Our approach works in 3 phases: Phase I classifies if a protein is likely to bind with another protein. Phase II determines if a protein-binding (PB protein is a hub. Phase III classifies PB proteins as singlish-interface versus multiple-interface hubs and date versus party hubs. At each stage, we use sequence-based predictors trained using several standard machine learning techniques.Our method is able to predict whether a protein is a protein-binding protein with an accuracy of 94% and a correlation coefficient of 0.87; identify hubs from non-hubs with 100% accuracy for 30% of the data; distinguish date hubs/party hubs with 69% accuracy and area under ROC curve of 0.68; and SIH/MIH with 89% accuracy and area under ROC curve of 0.84. Because our method is based on sequence information alone, it can be used even in settings where reliable protein-protein interaction data or structures of protein-protein complexes are unavailable to obtain useful insights into the functional and evolutionary characteristics of proteins and their interactions.We provide a web server for our three-phase approach: http://hybsvm.gdcb.iastate.edu.

Two modes of interaction of the single-stranded DNA-binding protein of bacteriophage T7 with the DNA polymerase-thioredoxin complex

KAUST Repository

Ghosh, Sharmistha; Hamdan, Samir; Richardson, Charles C.

2010-01-01

The DNA polymerase encoded by bacteriophage T7 has low processivity. Escherichia coli thioredoxin binds to a segment of 76 residues in the thumb subdomain of the polymerase and increases the processivity. The binding of thioredoxin leads to the formation of two basic loops, loops A and B, located within the thioredoxin-binding domain (TBD). Both loops interact with the acidic C terminus of the T7 helicase. A relatively weak electrostatic mode involves the C-terminal tail of the helicase and the TBD, whereas a high affinity interaction that does not involve the C-terminal tail occurs when the polymerase is in a polymerization mode. T7 gene 2.5 single-stranded DNA-binding protein (gp2.5) also has an acidic C-terminal tail. gp2.5 also has two modes of interaction with the polymerase, but both involve the C-terminal tail of gp2.5. An electrostatic interaction requires the basic residues in loops A and B, and gp2.5 binds to both loops with similar affinity as measured by surface plasmon resonance. When the polymerase is in a polymerization mode, the C terminus of gene 2.5 protein interacts with the polymerase in regions outside the TBD.gp2.5 increases the processivity of the polymerase-helicase complex during leading strand synthesis. When loop B of the TBD is altered, abortive DNA products are observed during leading strand synthesis. Loop B appears to play an important role in communication with the helicase and gp2.5, whereas loop A plays a stabilizing role in these interactions. © 2010 by The American Society for Biochemistry and Molecular Biology, Inc.

Two modes of interaction of the single-stranded DNA-binding protein of bacteriophage T7 with the DNA polymerase-thioredoxin complex

KAUST Repository

Ghosh, Sharmistha

2010-04-06

The DNA polymerase encoded by bacteriophage T7 has low processivity. Escherichia coli thioredoxin binds to a segment of 76 residues in the thumb subdomain of the polymerase and increases the processivity. The binding of thioredoxin leads to the formation of two basic loops, loops A and B, located within the thioredoxin-binding domain (TBD). Both loops interact with the acidic C terminus of the T7 helicase. A relatively weak electrostatic mode involves the C-terminal tail of the helicase and the TBD, whereas a high affinity interaction that does not involve the C-terminal tail occurs when the polymerase is in a polymerization mode. T7 gene 2.5 single-stranded DNA-binding protein (gp2.5) also has an acidic C-terminal tail. gp2.5 also has two modes of interaction with the polymerase, but both involve the C-terminal tail of gp2.5. An electrostatic interaction requires the basic residues in loops A and B, and gp2.5 binds to both loops with similar affinity as measured by surface plasmon resonance. When the polymerase is in a polymerization mode, the C terminus of gene 2.5 protein interacts with the polymerase in regions outside the TBD.gp2.5 increases the processivity of the polymerase-helicase complex during leading strand synthesis. When loop B of the TBD is altered, abortive DNA products are observed during leading strand synthesis. Loop B appears to play an important role in communication with the helicase and gp2.5, whereas loop A plays a stabilizing role in these interactions. © 2010 by The American Society for Biochemistry and Molecular Biology, Inc.

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.

A Survey of Aspartate Phenylalanine and Glutamate Phenylalanine Interactions in the Protein Data Bank: Searching for Anion Pairs

Energy Technology Data Exchange (ETDEWEB)

Philip, Vivek M [ORNL; Harris, Jason B [ORNL; Adams, Rachel M [ORNL; Nguyen, Don [University of Tennessee, Knoxville (UTK); Spires, Jeremy [University of Tennessee, Knoxville (UTK); Howell, Elizabeth E. [University of Tennessee, Knoxville (UTK); Hinde, Robert J [ORNL

2011-01-01

Protein structures are stabilized using noncovalent interactions. In addition to the traditional noncovalent interactions, newer types of interactions are thought to be present in proteins. One such interaction, an anion pair, in which the positively charged edge of an aromatic ring interacts with an anion, forming a favorable anion quadrupole interaction, has been previously proposed [Jackson, M. R., et al. (2007) J. Phys. Chem. B111, 8242 8249]. To study the role of anion interactions in stabilizing protein structure, we analyzed pairwise interactions between phenylalanine (Phe) and the anionic amino acids, aspartate (Asp) and glutamate (Glu). Particular emphasis was focused on identification of Phe Asp or Glu pairs separated by less than 7 in the high-resolution, nonredundant Protein Data Bank. Simplifying Phe to benzene and Asp or Glu to formate molecules facilitated in silico analysis of the pairs. Kitaura Morokuma energy calculations were performed on roughly 19000 benzene formate pairs and the resulting energies analyzed as a function of distance and angle. Edgewise interactions typically produced strongly stabilizing interaction energies (2 to 7.3 kcal/mol), while interactions involving the ring face resulted in weakly stabilizing to repulsive interaction energies. The strongest, most stabilizing interactions were identified as preferentially occurring in buried residues. Anion pairs are found throughout protein structures, in helices as well as strands. Numerous pairs also had nearby cation interactions as well as potential stacking. While more than 1000 structures did not contain an anion pair, the 3134 remaining structures contained approximately 2.6 anion pairs per protein, suggesting it is a reasonably common motif that could contribute to the overall structural stability of a protein.

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

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.

PIMiner: A web tool for extraction of protein interactions from biomedical literature

KAUST Repository

Chowdhary, Rajesh

2013-01-01

Information on Protein Interactions (PIs) is valuable for biomedical research, but often lies buried in the scientific literature and cannot be readily retrieved. While much progress has been made over the years in extracting PIs from the literature using computational methods, there is a lack of free, public, user-friendly tools for the discovery of PIs. We developed an online tool for the extraction of PI relationships from PubMed-abstracts, which we name PIMiner. Protein pairs and the words that describe their interactions are reported by PIMiner so that new interactions can be easily detected within text. The interaction likelihood levels are reported too. The option to extract only specific types of interactions is also provided. The PIMiner server can be accessed through a web browser or remotely through a client\\'s command line. PIMiner can process 50,000 PubMed abstracts in approximately 7 min and thus appears suitable for large-scale processing of biological/biomedical literature. Copyright © 2013 Inderscience Enterprises Ltd.

Role of bone morphogenetic protein-7 in renal fibrosis

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Rui Xi eLi

2015-04-01

Full Text Available Renal fibrosis is final common pathway of end stage renal disease. Irrespective of the primary cause, renal fibrogenesis is a dynamic process which involves a large network of cellular and molecular interaction, including pro-inflammatory cell infiltration and activation, matrix-producing cell accumulation and activation, and secretion of profibrogenic factors that modulate extracellular matrix (ECM formation and cell-cell interaction. Bone morphogenetic protein-7 is a protein of the TGF-β super family and increasingly regarded as a counteracting molecule against TGF-β. A large variety of evidence shows an anti-fibrotic role of BMP-7 in chronic kidney disease, and this effect is largely mediated via counterbalancing the profibrotic effect of TGF-β. Besides, BMP-7 reduced ECM formation by inactivating matrix-producing cells and promoting mesenchymal-to-epithelial transition (MET. BMP-7 also increased ECM degradation. Despite these observations, the anti-fibrotic effect of BMP-7 is still controversial such that fine regulation of BMP-7 expression in vivo might be a great challenge for its ultimate clinical application.

Role of bone morphogenetic protein-7 in renal fibrosis

Science.gov (United States)

Li, Rui Xi; Yiu, Wai Han; Tang, Sydney C. W.

2015-01-01

Renal fibrosis is final common pathway of end stage renal disease. Irrespective of the primary cause, renal fibrogenesis is a dynamic process which involves a large network of cellular and molecular interaction, including pro-inflammatory cell infiltration and activation, matrix-producing cell accumulation and activation, and secretion of profibrogenic factors that modulate extracellular matrix (ECM) formation and cell-cell interaction. Bone morphogenetic protein-7 is a protein of the TGF-β super family and increasingly regarded as a counteracting molecule against TGF-β. A large variety of evidence shows an anti-fibrotic role of BMP-7 in chronic kidney disease, and this effect is largely mediated via counterbalancing the profibrotic effect of TGF-β. Besides, BMP-7 reduced ECM formation by inactivating matrix-producing cells and promoting mesenchymal-to-epithelial transition (MET). BMP-7 also increased ECM degradation. Despite these observations, the anti-fibrotic effect of BMP-7 is still controversial such that fine regulation of BMP-7 expression in vivo might be a great challenge for its ultimate clinical application. PMID:25954203

Brain transcriptome-wide screen for HIV-1 Nef protein interaction partners reveals various membrane-associated proteins.

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Ellen C Kammula

Full Text Available HIV-1 Nef protein contributes essentially to the pathology of AIDS by a variety of protein-protein-interactions within the host cell. The versatile functionality of Nef is partially attributed to different conformational states and posttranslational modifications, such as myristoylation. Up to now, many interaction partners of Nef have been identified using classical yeast two-hybrid screens. Such screens rely on transcriptional activation of reporter genes in the nucleus to detect interactions. Thus, the identification of Nef interaction partners that are integral membrane proteins, membrane-associated proteins or other proteins that do not translocate into the nucleus is hampered. In the present study, a split-ubiquitin based yeast two-hybrid screen was used to identify novel membrane-localized interaction partners of Nef. More than 80% of the hereby identified interaction partners of Nef are transmembrane proteins. The identified hits are GPM6B, GPM6A, BAP31, TSPAN7, CYB5B, CD320/TCblR, VSIG4, PMEPA1, OCIAD1, ITGB1, CHN1, PH4, CLDN10, HSPA9, APR-3, PEBP1 and B3GNT, which are involved in diverse cellular processes like signaling, apoptosis, neurogenesis, cell adhesion and protein trafficking or quality control. For a subfraction of the hereby identified proteins we present data supporting their direct interaction with HIV-1 Nef. We discuss the results with respect to many phenotypes observed in HIV infected cells and patients. The identified Nef interaction partners may help to further elucidate the molecular basis of HIV-related diseases.

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

Science.gov (United States)

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

2013-03-01

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

Identification of proteins that may directly interact with human RPA.

Science.gov (United States)

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.

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

Science.gov (United States)

Rosell, Mireia; Fernández-Recio, Juan

2018-04-01

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

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.

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.

Characterization of interactions between inclusion membrane proteins from Chlamydia trachomatis

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Emilie eGauliard

2015-02-01

Full Text Available Chlamydiae are obligate intracellular pathogens of eukaryotes. The bacteria grow in an intracellular vesicle called an inclusion, the membrane of which is heavily modified by chlamydial proteins called Incs (Inclusion membrane proteins. Incs represent 7-10% of the genomes of Chlamydia and, given their localization at the interface between the host and the pathogen, likely play a key role in the development and pathogenesis of the bacterium. However, their functions remain largely unknown. Here, we characterized the interaction properties between various Inc proteins of C. trachomatis, using a bacterial two-hybrid (BACTH method suitable for detecting interactions between integral membrane proteins. To validate this approach, we first examined the oligomerization properties of the well-characterized IncA protein and showed that both the cytoplasmic domain and the transmembrane region independently contribute to IncA oligomerization. We then analyzed a set of Inc proteins and identified novel interactions between these components. Two small Incs, IncF and Ct222, were found here to interact with many other Inc proteins and may thus represent interaction nodes within the inclusion membrane. Our data suggest that the Inc proteins may assemble in the membrane of the inclusion to form specific multi-molecular complexes in an hierarchical and temporal manner. These studies will help to better define the putative functions of the Inc proteins in the infectious process of Chlamydia.

Structure of the TPR domain of AIP: lack of client protein interaction with the C-terminal α-7 helix of the TPR domain of AIP is sufficient for pituitary adenoma predisposition.

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Rhodri M L Morgan

Full Text Available Mutations of the aryl hydrocarbon receptor interacting protein (AIP have been associated with familial isolated pituitary adenomas predisposing to young-onset acromegaly and gigantism. The precise tumorigenic mechanism is not well understood as AIP interacts with a large number of independent proteins as well as three chaperone systems, HSP90, HSP70 and TOMM20. We have determined the structure of the TPR domain of AIP at high resolution, which has allowed a detailed analysis of how disease-associated mutations impact on the structural integrity of the TPR domain. A subset of C-terminal α-7 helix (Cα-7h mutations, R304* (nonsense mutation, R304Q, Q307* and R325Q, a known site for AhR and PDE4A5 client-protein interaction, occur beyond those that interact with the conserved MEEVD and EDDVE sequences of HSP90 and TOMM20. These C-terminal AIP mutations appear to only disrupt client-protein binding to the Cα-7h, while chaperone binding remains unaffected, suggesting that failure of client-protein interaction with the Cα-7h is sufficient to predispose to pituitary adenoma. We have also identified a molecular switch in the AIP TPR-domain that allows recognition of both the conserved HSP90 motif, MEEVD, and the equivalent sequence (EDDVE of TOMM20.

Identification of salivary mucin MUC7 binding proteins from Streptococcus gordonii

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Thornton David J

2009-08-01

Full Text Available Abstract Background The salivary mucin MUC7 (previously known as MG2 can adhere to various strains of streptococci that are primary colonizers and predominant microorganisms of the oral cavity. Although there is a growing interest in interaction between oral pathogens and salivary mucins, studies reporting the specific binding sites on the bacteria are rather limited. Identification and characterization of the specific interacting proteins on the bacterial cell surface, termed adhesins, are crucial to further understand host-pathogen interactions. Results We demonstrate here, using purified MUC7 to overlay blots of SDS-extracts of Streptococcus gordonii cell surface proteins, 4 MUC7-binding bands, with apparent molecular masses of 62, 78, 84 and 133 kDa from the Streptococcus gordonii strain, PK488. Putative adhesins were identified by in-gel digestion and subsequent nanoLC-tandem mass spectrometry analysis of resultant peptides. The 62 kDa and 84 kDa bands were identified as elongation factor (EF Tu and EF-G respectively. The 78 kDa band was a hppA gene product; the 74 kDa oligopeptide-binding lipoprotein. The 133 kDa band contained two proteins; alpha enolase and DNA-directed RNA polymerase, beta' subunit. Some of these proteins, for example alpha enolase are expected to be intracellular, however, flow cytometric analysis confirmed its location on the bacterial surface. Conclusion Our data demonstrated that S. gordonii expressed a number of putative MUC7 recognizing proteins and these contribute to MUC7 mucin binding of this streptococcal strain.

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

Science.gov (United States)

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

2011-05-20

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

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

Directory of Open Access Journals (Sweden)

Vincent Vagenende

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

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

Quantification of protein interaction kinetics in a micro droplet

Energy Technology Data Exchange (ETDEWEB)

Yin, L. L. [Center for Bioelectronics and Biosensors, Biodesign Institute, Arizona State University, Tempe, Arizona 85287 (United States); College of Chemistry and Chemical Engineering, Chongqing University, Chongqing 400044 (China); Wang, S. P., E-mail: shaopeng.wang@asu.edu, E-mail: njtao@asu.edu; Shan, X. N.; Tao, N. J., E-mail: shaopeng.wang@asu.edu, E-mail: njtao@asu.edu [Center for Bioelectronics and Biosensors, Biodesign Institute, Arizona State University, Tempe, Arizona 85287 (United States); Zhang, S. T. [College of Chemistry and Chemical Engineering, Chongqing University, Chongqing 400044 (China)

2015-11-15

Characterization of protein interactions is essential to the discovery of disease biomarkers, the development of diagnostic assays, and the screening for therapeutic drugs. Conventional flow-through kinetic measurements need relative large amount of sample that is not feasible for precious protein samples. We report a novel method to measure protein interaction kinetics in a single droplet with sub microliter or less volume. A droplet in a humidity-controlled environmental chamber is replacing the microfluidic channels as the reactor for the protein interaction. The binding process is monitored by a surface plasmon resonance imaging (SPRi) system. Association curves are obtained from the average SPR image intensity in the center area of the droplet. The washing step required by conventional flow-through SPR method is eliminated in the droplet method. The association and dissociation rate constants and binding affinity of an antigen-antibody interaction are obtained by global fitting of association curves at different concentrations. The result obtained by this method is accurate as validated by conventional flow-through SPR system. This droplet-based method not only allows kinetic studies for proteins with limited supply but also opens the door for high-throughput protein interaction study in a droplet-based microarray format that enables measurement of many to many interactions on a single chip.

A Physical Interaction Network of Dengue Virus and Human Proteins*

Science.gov (United States)

Khadka, Sudip; Vangeloff, Abbey D.; Zhang, Chaoying; Siddavatam, Prasad; Heaton, Nicholas S.; Wang, Ling; Sengupta, Ranjan; Sahasrabudhe, Sudhir; Randall, Glenn; Gribskov, Michael; Kuhn, Richard J.; Perera, Rushika; LaCount, Douglas J.

2011-01-01

Dengue virus (DENV), an emerging mosquito-transmitted pathogen capable of causing severe disease in humans, interacts with host cell factors to create a more favorable environment for replication. However, few interactions between DENV and human proteins have been reported to date. To identify DENV-human protein interactions, we used high-throughput yeast two-hybrid assays to screen the 10 DENV proteins against a human liver activation domain library. From 45 DNA-binding domain clones containing either full-length viral genes or partially overlapping gene fragments, we identified 139 interactions between DENV and human proteins, the vast majority of which are novel. These interactions involved 105 human proteins, including six previously implicated in DENV infection and 45 linked to the replication of other viruses. Human proteins with functions related to the complement and coagulation cascade, the centrosome, and the cytoskeleton were enriched among the DENV interaction partners. To determine if the cellular proteins were required for DENV infection, we used small interfering RNAs to inhibit their expression. Six of 12 proteins targeted (CALR, DDX3X, ERC1, GOLGA2, TRIP11, and UBE2I) caused a significant decrease in the replication of a DENV replicon. We further showed that calreticulin colocalized with viral dsRNA and with the viral NS3 and NS5 proteins in DENV-infected cells, consistent with a direct role for calreticulin in DENV replication. Human proteins that interacted with DENV had significantly higher average degree and betweenness than expected by chance, which provides additional support for the hypothesis that viruses preferentially target cellular proteins that occupy central position in the human protein interaction network. This study provides a valuable starting point for additional investigations into the roles of human proteins in DENV infection. PMID:21911577

A physical interaction network of dengue virus and human proteins.

Science.gov (United States)

Khadka, Sudip; Vangeloff, Abbey D; Zhang, Chaoying; Siddavatam, Prasad; Heaton, Nicholas S; Wang, Ling; Sengupta, Ranjan; Sahasrabudhe, Sudhir; Randall, Glenn; Gribskov, Michael; Kuhn, Richard J; Perera, Rushika; LaCount, Douglas J

2011-12-01

Dengue virus (DENV), an emerging mosquito-transmitted pathogen capable of causing severe disease in humans, interacts with host cell factors to create a more favorable environment for replication. However, few interactions between DENV and human proteins have been reported to date. To identify DENV-human protein interactions, we used high-throughput yeast two-hybrid assays to screen the 10 DENV proteins against a human liver activation domain library. From 45 DNA-binding domain clones containing either full-length viral genes or partially overlapping gene fragments, we identified 139 interactions between DENV and human proteins, the vast majority of which are novel. These interactions involved 105 human proteins, including six previously implicated in DENV infection and 45 linked to the replication of other viruses. Human proteins with functions related to the complement and coagulation cascade, the centrosome, and the cytoskeleton were enriched among the DENV interaction partners. To determine if the cellular proteins were required for DENV infection, we used small interfering RNAs to inhibit their expression. Six of 12 proteins targeted (CALR, DDX3X, ERC1, GOLGA2, TRIP11, and UBE2I) caused a significant decrease in the replication of a DENV replicon. We further showed that calreticulin colocalized with viral dsRNA and with the viral NS3 and NS5 proteins in DENV-infected cells, consistent with a direct role for calreticulin in DENV replication. Human proteins that interacted with DENV had significantly higher average degree and betweenness than expected by chance, which provides additional support for the hypothesis that viruses preferentially target cellular proteins that occupy central position in the human protein interaction network. This study provides a valuable starting point for additional investigations into the roles of human proteins in DENV infection.

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.

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

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

KAUST Repository

Li, Chuanxi

2014-01-01

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

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

Science.gov (United States)

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

2013-01-01

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

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

KAUST Repository

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

2014-01-01

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

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

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

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

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

Science.gov (United States)

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

2017-10-03

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

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.

Impact of surface coating and food-mimicking media on nanosilver-protein interaction

Energy Technology Data Exchange (ETDEWEB)

Burcza, Anna, E-mail: anna.burcza@mri.bund.de; Gräf, Volker; Walz, Elke; Greiner, Ralf [Max Rubner-Institute, Department of Food Technology and Bioprocess Engineering (Germany)

2015-11-15

The application of silver nanoparticles (AgNPs) in food contact materials has recently become a subject of dispute due to the possible migration of silver in nanoform into foods and beverages. Therefore, the analysis of the interaction of AgNPs with food components, especially proteins, is of high importance in order to increase our knowledge of the behavior of nanoparticles in food matrices. AgPURE™ W10 (20 nm), an industrially applied nanomaterial, was compared with AgNPs of similar size frequently investigated for scientific purposes differing in the surface capping agent (spherical AgNP coated with either PVP or citrate). The interactions of the AgNPs with whey proteins (BSA, α-lactalbumin and β-lactoglobulin) at different pH values (4.2, 7 or 7.4) were investigated using surface plasmon resonance, SDS-PAGE, and asymmetric flow field-flow fractionation. The data obtained by the three different methods correlated well. Besides the nature of the protein and the nanoparticle coating, the environment was shown to affect the interaction significantly. The strongest interaction was obtained with BSA and AgNPs in an acidic environment. Neutral and slightly alkaline conditions however, seemed to prevent the AgNP-protein interaction almost completely. Furthermore, the interaction of whey proteins with AgPURE™ W10 was found to be weaker compared to the interaction with the other two AgNPs under all conditions investigated.

Impact of surface coating and food-mimicking media on nanosilver-protein interaction

International Nuclear Information System (INIS)

Burcza, Anna; Gräf, Volker; Walz, Elke; Greiner, Ralf

2015-01-01

The application of silver nanoparticles (AgNPs) in food contact materials has recently become a subject of dispute due to the possible migration of silver in nanoform into foods and beverages. Therefore, the analysis of the interaction of AgNPs with food components, especially proteins, is of high importance in order to increase our knowledge of the behavior of nanoparticles in food matrices. AgPURE™ W10 (20 nm), an industrially applied nanomaterial, was compared with AgNPs of similar size frequently investigated for scientific purposes differing in the surface capping agent (spherical AgNP coated with either PVP or citrate). The interactions of the AgNPs with whey proteins (BSA, α-lactalbumin and β-lactoglobulin) at different pH values (4.2, 7 or 7.4) were investigated using surface plasmon resonance, SDS-PAGE, and asymmetric flow field-flow fractionation. The data obtained by the three different methods correlated well. Besides the nature of the protein and the nanoparticle coating, the environment was shown to affect the interaction significantly. The strongest interaction was obtained with BSA and AgNPs in an acidic environment. Neutral and slightly alkaline conditions however, seemed to prevent the AgNP-protein interaction almost completely. Furthermore, the interaction of whey proteins with AgPURE™ W10 was found to be weaker compared to the interaction with the other two AgNPs under all conditions investigated

ProteinShop: A tool for interactive protein manipulation and steering

Energy Technology Data Exchange (ETDEWEB)

Crivelli, Silvia; Kreylos, Oliver; Max, Nelson; Hamann, Bernd; Bethel, Wes

2004-05-25

We describe ProteinShop, a new visualization tool that streamlines and simplifies the process of determining optimal protein folds. ProteinShop may be used at different stages of a protein structure prediction process. First, it can create protein configurations containing secondary structures specified by the user. Second, it can interactively manipulate protein fragments to achieve desired folds by adjusting the dihedral angles of selected coil regions using an Inverse Kinematics method. Last, it serves as a visual framework to monitor and steer a protein structure prediction process that may be running on a remote machine. ProteinShop was used to create initial configurations for a protein structure prediction method developed by a team that competed in CASP5. ProteinShop's use accelerated the process of generating initial configurations, reducing the time required from days to hours. This paper describes the structure of ProteinShop and discusses its main features.

A survey of aspartate-phenylalanine and glutamate-phenylalanine interactions in the protein data bank: searching for anion-π pairs.

Science.gov (United States)

Philip, Vivek; Harris, Jason; Adams, Rachel; Nguyen, Don; Spiers, Jeremy; Baudry, Jerome; Howell, Elizabeth E; Hinde, Robert J

2011-04-12

Protein structures are stabilized using noncovalent interactions. In addition to the traditional noncovalent interactions, newer types of interactions are thought to be present in proteins. One such interaction, an anion-π pair, in which the positively charged edge of an aromatic ring interacts with an anion, forming a favorable anion-quadrupole interaction, has been previously proposed [Jackson, M. R., et al. (2007) J. Phys. Chem. B111, 8242-8249]. To study the role of anion-π interactions in stabilizing protein structure, we analyzed pairwise interactions between phenylalanine (Phe) and the anionic amino acids, aspartate (Asp) and glutamate (Glu). Particular emphasis was focused on identification of Phe-Asp or -Glu pairs separated by less than 7 Å in the high-resolution, nonredundant Protein Data Bank. Simplifying Phe to benzene and Asp or Glu to formate molecules facilitated in silico analysis of the pairs. Kitaura-Morokuma energy calculations were performed on roughly 19000 benzene-formate pairs and the resulting energies analyzed as a function of distance and angle. Edgewise interactions typically produced strongly stabilizing interaction energies (-2 to -7.3 kcal/mol), while interactions involving the ring face resulted in weakly stabilizing to repulsive interaction energies. The strongest, most stabilizing interactions were identified as preferentially occurring in buried residues. Anion-π pairs are found throughout protein structures, in helices as well as β strands. Numerous pairs also had nearby cation-π interactions as well as potential π-π stacking. While more than 1000 structures did not contain an anion-π pair, the 3134 remaining structures contained approximately 2.6 anion-π pairs per protein, suggesting it is a reasonably common motif that could contribute to the overall structural stability of a protein.

A Survey of Aspartate-Phenylalanine and Glutamate-Phenylalanine Interactions in the Protein Data Bank: Searching for Anion-pi Pairs

Energy Technology Data Exchange (ETDEWEB)

Philip, Vivek M [ORNL; Harris, Jason B [ORNL; Adams, Rachel M [ORNL; Nguyen, Don [University of Tennessee; Spiers, Jeremy D [ORNL; Baudry, Jerome Y [ORNL; Howell, Elizabeth E [ORNL; Hinde, Robert J [ORNL

2011-01-01

Protein structures are stabilized using noncovalent interactions. In addition to the traditional noncovalent interactions, newer types of interactions are thought to be present in proteins. One such interaction, an anion-{pi} pair, in which the positively charged edge of an aromatic ring interacts with an anion, forming a favorable anion-quadrupole interaction, has been previously proposed [Jackson, M. R., et al. (2007) J. Phys. Chem. B111, 8242-8249]. To study the role of anion-{pi} interactions in stabilizing protein structure, we analyzed pairwise interactions between phenylalanine (Phe) and the anionic amino acids, aspartate (Asp) and glutamate (Glu). Particular emphasis was focused on identification of Phe-Asp or -Glu pairs separated by less than 7 {angstrom} in the high-resolution, nonredundant Protein Data Bank. Simplifying Phe to benzene and Asp or Glu to formate molecules facilitated in silico analysis of the pairs. Kitaura-Morokuma energy calculations were performed on roughly 19000 benzene-formate pairs and the resulting energies analyzed as a function of distance and angle. Edgewise interactions typically produced strongly stabilizing interaction energies (-2 to -7.3 kcal/mol), while interactions involving the ring face resulted in weakly stabilizing to repulsive interaction energies. The strongest, most stabilizing interactions were identified as preferentially occurring in buried residues. Anion-{pi} pairs are found throughout protein structures, in helices as well as {beta} strands. Numerous pairs also had nearby cation-{pi} interactions as well as potential {pi}-{pi} stacking. While more than 1000 structures did not contain an anion-{pi} pair, the 3134 remaining structures contained approximately 2.6 anion-{pi} pairs per protein, suggesting it is a reasonably common motif that could contribute to the overall structural stability of a protein.

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

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.

Molecular evolution of a-kinase anchoring protein (AKAP-7: implications in comparative PKA compartmentalization

Directory of Open Access Journals (Sweden)

Johnson Keven R

2012-07-01

Full Text Available Abstract Background A-Kinase Anchoring Proteins (AKAPs are molecular scaffolding proteins mediating the assembly of multi-protein complexes containing cAMP-dependent protein kinase A (PKA, directing the kinase in discrete subcellular locations. Splice variants from the AKAP7 gene (AKAP15/18 are vital components of neuronal and cardiac phosphatase complexes, ion channels, cardiac Ca2+ handling and renal water transport. Results Shown in evolutionary analyses, the formation of the AKAP7-RI/RII binding domain (required for AKAP/PKA-R interaction corresponds to vertebrate-specific gene duplication events in the PKA-RI/RII subunits. Species analyses of AKAP7 splice variants shows the ancestral AKAP7 splice variant is AKAP7α, while the ancestral long form AKAP7 splice variant is AKAP7γ. Multi-species AKAP7 gene alignments, show the recent formation of AKAP7δ occurs with the loss of native AKAP7γ in rats and basal primates. AKAP7 gene alignments and two dimensional Western analyses indicate that AKAP7γ is produced from an internal translation-start site that is present in the AKAP7δ cDNA of mice and humans but absent in rats. Immunofluorescence analysis of AKAP7 protein localization in both rat and mouse heart suggests AKAP7γ replaces AKAP7δ at the cardiac sarcoplasmic reticulum in species other than rat. DNA sequencing identified Human AKAP7δ insertion-deletions (indels that promote the production of AKAP7γ instead of AKAP7δ. Conclusions This AKAP7 molecular evolution study shows that these vital scaffolding proteins developed in ancestral vertebrates and that independent mutations in the AKAP7 genes of rodents and early primates has resulted in the recent formation of AKAP7δ, a splice variant of likely lesser importance in humans than currently described.

Identification of a novel higher molecular weight isoform of USP7/HAUSP that interacts with the Herpes simplex virus type-1 immediate early protein ICP0.

Science.gov (United States)

Antrobus, Robin; Boutell, Chris

2008-10-01

The Herpes simplex virus type-1 (HSV-1) regulatory protein ICP0, a RING-finger E3 ubiquitin ligase, stimulates the onset of viral lytic replication and the reactivation of quiescent viral genomes from latency. Like many ubiquitin ligases ICP0 induces its own ubiquitination, a process that can lead to its proteasome-dependent degradation. ICP0 counteracts this activity by recruiting the cellular ubiquitin-specific protease USP7/HAUSP. Here we show that ICP0 can also interact with a previously unidentified isoform of USP7 (termed here USP7(beta)). This isoform is not a predominantly ubiquitinated, SUMO-modified, or phosphorylated species of USP7 but is constitutively expressed in a number of different cell types. Like USP7, USP7(beta) binds specifically to an electrophilic ubiquitin probe, indicating that it contains an accessible catalytic core with potential ubiquitin-protease activity. The interaction formed between ICP0 and USP7(beta) requires ICP0 to have an intact USP7-binding domain and results in its susceptibility to ICP0-mediated degradation during HSV-1 infection.

Mass spectrometric analysis of protein interactions

DEFF Research Database (Denmark)

Borch, Jonas; Jørgensen, Thomas J. D.; Roepstorff, Peter

2005-01-01

Mass spectrometry is a powerful tool for identification of interaction partners and structural characterization of protein interactions because of its high sensitivity, mass accuracy and tolerance towards sample heterogeneity. Several tools that allow studies of protein interaction are now...... available and recent developments that increase the confidence of studies of protein interaction by mass spectrometry include quantification of affinity-purified proteins by stable isotope labeling and reagents for surface topology studies that can be identified by mass-contributing reporters (e.g. isotope...... labels, cleavable cross-linkers or fragment ions. The use of mass spectrometers to study protein interactions using deuterium exchange and for analysis of intact protein complexes recently has progressed considerably....

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

DEFF Research Database (Denmark)

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

2003-01-01

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

A systematic evaluation of protein kinase a-a-kinase anchoring protein interaction motifs

NARCIS (Netherlands)

Burgers, Pepijn P|info:eu-repo/dai/nl/341566551; van der Heyden, Marcel A G; Kok, Bart; Heck, Albert J R|info:eu-repo/dai/nl/105189332; Scholten, Arjen|info:eu-repo/dai/nl/313939780

2015-01-01

Protein kinase A (PKA) in vertebrates is localized to specific locations in the cell via A-kinase anchoring proteins (AKAPs). The regulatory subunits of the four PKA isoforms (RIα, RIβ, RIIα, and RIIβ) each form a homodimer, and their dimerization domain interacts with a small helical region present

A systematic evaluation of protein kinase A-A-kinase anchoring protein interaction motifs

NARCIS (Netherlands)

Burgers, Pepijn P; van der Heyden, MAG; Kok, Bart; Heck, Albert J R; Scholten, Arjen

2015-01-01

Protein kinase A (PKA) in vertebrates is localized to specific locations in the cell via A-kinase anchoring proteins (AKAPs). The regulatory subunits of the four PKA isoforms (RIα, RIβ, RIIα, and RIIβ) each form a homodimer, and their dimerization domain interacts with a small helical region present

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

Directory of Open Access Journals (Sweden)

Meijing Li

2015-01-01

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

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.

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

Science.gov (United States)

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

2017-03-24

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

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.

Interaction of milk whey protein with common phenolic acids

Science.gov (United States)

Zhang, Hao; Yu, Dandan; Sun, Jing; Guo, Huiyuan; Ding, Qingbo; Liu, Ruihai; Ren, Fazheng

2014-01-01

Phenolics-rich foods such as fruit juices and coffee are often consumed with milk. In this study, the interactions of α-lactalbumin and β-lactoglobulin with the phenolic acids (chlorogenic acid, caffeic acid, ferulic acid, and coumalic acid) were examined. Fluorescence, CD, and FTIR spectroscopies were used to analyze the binding modes, binding constants, and the effects of complexation on the conformation of whey protein. The results showed that binding constants of each whey protein-phenolic acid interaction ranged from 4 × 105 to 7 × 106 M-n and the number of binding sites n ranged from 1.28 ± 0.13 to 1.54 ± 0.34. Because of these interactions, the conformation of whey protein was altered, with a significant reduction in the amount of α-helix and an increase in the amounts of β-sheet and turn structures.

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

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

Biochemical and structural characterization of Cren7, a novel chromatin protein conserved among Crenarchaea.

Science.gov (United States)

Guo, Li; Feng, Yingang; Zhang, Zhenfeng; Yao, Hongwei; Luo, Yuanming; Wang, Jinfeng; Huang, Li

2008-03-01

Archaea contain a variety of chromatin proteins consistent with the evolution of different genome packaging mechanisms. Among the two main kingdoms in the Archaea, Euryarchaeota synthesize histone homologs, whereas Crenarchaeota have not been shown to possess a chromatin protein conserved at the kingdom level. We report the identification of Cren7, a novel family of chromatin proteins highly conserved in the Crenarchaeota. A small, basic, methylated and abundant protein, Cren7 displays a higher affinity for double-stranded DNA than for single-stranded DNA, constrains negative DNA supercoils and is associated with genomic DNA in vivo. The solution structure and DNA-binding surface of Cren7 from the hyperthermophilic crenarchaeon Sulfolobus solfataricus were determined by NMR. The protein adopts an SH3-like fold. It interacts with duplex DNA through a beta-sheet and a long flexible loop, presumably resulting in DNA distortions through intercalation of conserved hydrophobic residues into the DNA structure. These data suggest that the crenarchaeal kingdom in the Archaea shares a common strategy in chromatin organization.

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

Interactions of the human MCM-BP protein with MCM complex components and Dbf4.

Directory of Open Access Journals (Sweden)

Tin Nguyen

Full Text Available MCM-BP was discovered as a protein that co-purified from human cells with MCM proteins 3 through 7; results which were recapitulated in frogs, yeast and plants. Evidence in all of these organisms supports an important role for MCM-BP in DNA replication, including contributions to MCM complex unloading. However the mechanisms by which MCM-BP functions and associates with MCM complexes are not well understood. Here we show that human MCM-BP is capable of interacting with individual MCM proteins 2 through 7 when co-expressed in insect cells and can greatly increase the recovery of some recombinant MCM proteins. Glycerol gradient sedimentation analysis indicated that MCM-BP interacts most strongly with MCM4 and MCM7. Similar gradient analyses of human cell lysates showed that only a small amount of MCM-BP overlapped with the migration of MCM complexes and that MCM complexes were disrupted by exogenous MCM-BP. In addition, large complexes containing MCM-BP and MCM proteins were detected at mid to late S phase, suggesting that the formation of specific MCM-BP complexes is cell cycle regulated. We also identified an interaction between MCM-BP and the Dbf4 regulatory component of the DDK kinase in both yeast 2-hybrid and insect cell co-expression assays, and this interaction was verified by co-immunoprecipitation of endogenous proteins from human cells. In vitro kinase assays showed that MCM-BP was not a substrate for DDK but could inhibit DDK phosphorylation of MCM4,6,7 within MCM4,6,7 or MCM2-7 complexes, with little effect on DDK phosphorylation of MCM2. Since DDK is known to activate DNA replication through phosphorylation of these MCM proteins, our results suggest that MCM-BP may affect DNA replication in part by regulating MCM phosphorylation by DDK.

Interactions of the human MCM-BP protein with MCM complex components and Dbf4.

Science.gov (United States)

Nguyen, Tin; Jagannathan, Madhav; Shire, Kathy; Frappier, Lori

2012-01-01

MCM-BP was discovered as a protein that co-purified from human cells with MCM proteins 3 through 7; results which were recapitulated in frogs, yeast and plants. Evidence in all of these organisms supports an important role for MCM-BP in DNA replication, including contributions to MCM complex unloading. However the mechanisms by which MCM-BP functions and associates with MCM complexes are not well understood. Here we show that human MCM-BP is capable of interacting with individual MCM proteins 2 through 7 when co-expressed in insect cells and can greatly increase the recovery of some recombinant MCM proteins. Glycerol gradient sedimentation analysis indicated that MCM-BP interacts most strongly with MCM4 and MCM7. Similar gradient analyses of human cell lysates showed that only a small amount of MCM-BP overlapped with the migration of MCM complexes and that MCM complexes were disrupted by exogenous MCM-BP. In addition, large complexes containing MCM-BP and MCM proteins were detected at mid to late S phase, suggesting that the formation of specific MCM-BP complexes is cell cycle regulated. We also identified an interaction between MCM-BP and the Dbf4 regulatory component of the DDK kinase in both yeast 2-hybrid and insect cell co-expression assays, and this interaction was verified by co-immunoprecipitation of endogenous proteins from human cells. In vitro kinase assays showed that MCM-BP was not a substrate for DDK but could inhibit DDK phosphorylation of MCM4,6,7 within MCM4,6,7 or MCM2-7 complexes, with little effect on DDK phosphorylation of MCM2. Since DDK is known to activate DNA replication through phosphorylation of these MCM proteins, our results suggest that MCM-BP may affect DNA replication in part by regulating MCM phosphorylation by DDK.

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.

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...

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.

[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

Electrostatic Interactions between Elongated Monomers Drive Filamentation of Drosophila Shrub, a Metazoan ESCRT-III Protein

Directory of Open Access Journals (Sweden)

Brian J. McMillan

2016-08-01

Full Text Available The endosomal sorting complex required for transport (ESCRT is a conserved protein complex that facilitates budding and fission of membranes. It executes a key step in many cellular events, including cytokinesis and multi-vesicular body formation. The ESCRT-III protein Shrub in flies, or its homologs in yeast (Snf7 or humans (CHMP4B, is a critical polymerizing component of ESCRT-III needed to effect membrane fission. We report the structural basis for polymerization of Shrub and define a minimal region required for filament formation. The X-ray structure of the Shrub core shows that individual monomers in the lattice interact in a staggered arrangement using complementary electrostatic surfaces. Mutations that disrupt interface salt bridges interfere with Shrub polymerization and function. Despite substantial sequence divergence and differences in packing interactions, the arrangement of Shrub subunits in the polymer resembles that of Snf7 and other family homologs, suggesting that this intermolecular packing mechanism is shared among ESCRT-III proteins.

Identification of a novel receptor-like protein kinase that interacts with a geminivirus nuclear shuttle protein

International Nuclear Information System (INIS)

Mariano, Andrea C.; Andrade, Maxuel O.; Santos, Anesia A.; Carolino, Sonia M.B.; Oliveira, Marli L.; Baracat-Pereira, Maria Cristina; Brommonshenkel, Sergio H.; Fontes, Elizabeth P.B.

2004-01-01

Despite extensive studies in plant virus-host interactions, the molecular mechanisms of geminivirus movement and interactions with host components remain largely unknown. A tomato kinase protein and its soybean homolog were found to interact specifically with the nuclear shuttle protein (NSP) of Tomato golden mosaic virus (TGMV) and Tomato crinkle leaf yellows virus (TCrLYV) through yeast two-hybrid screening and in vitro protein binding assays. These proteins, designated LeNIK (Lycopersicon esculentum NSP-Interacting Kinase) and GmNIK (Glycine max NIK), belong to the LRR-RLK (leucine rich-repeat receptor-like kinase) family that is involved in plant developmental processes and/or resistance response. As such, NIK is structurally organized into characteristic domains, including a serine/threonine kinase domain with a nucleotide binding site at the C-terminal region, an internal transmembrane segment and leucine-rich repeats (LRR) at the N-terminal portion. The potential significance of the NSP-NIK interaction is discussed

PSAIA – Protein Structure and Interaction Analyzer

Directory of Open Access Journals (Sweden)

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.

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

Directory of Open Access Journals (Sweden)

Swapnil R Chhabra

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

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.

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)

Wiki-pi: a web-server of annotated human protein-protein interactions to aid in discovery of protein function.

Directory of Open Access Journals (Sweden)

Naoki Orii

Full Text Available Protein-protein interactions (PPIs are the basis of biological functions. Knowledge of the interactions of a protein can help understand its molecular function and its association with different biological processes and pathways. Several publicly available databases provide comprehensive information about individual proteins, such as their sequence, structure, and function. There also exist databases that are built exclusively to provide PPIs by curating them from published literature. The information provided in these web resources is protein-centric, and not PPI-centric. The PPIs are typically provided as lists of interactions of a given gene with links to interacting partners; they do not present a comprehensive view of the nature of both the proteins involved in the interactions. A web database that allows search and retrieval based on biomedical characteristics of PPIs is lacking, and is needed. We present Wiki-Pi (read Wiki-π, a web-based interface to a database of human PPIs, which allows users to retrieve interactions by their biomedical attributes such as their association to diseases, pathways, drugs and biological functions. Each retrieved PPI is shown with annotations of both of the participant proteins side-by-side, creating a basis to hypothesize the biological function facilitated by the interaction. Conceptually, it is a search engine for PPIs analogous to PubMed for scientific literature. Its usefulness in generating novel scientific hypotheses is demonstrated through the study of IGSF21, a little-known gene that was recently identified to be associated with diabetic retinopathy. Using Wiki-Pi, we infer that its association to diabetic retinopathy may be mediated through its interactions with the genes HSPB1, KRAS, TMSB4X and DGKD, and that it may be involved in cellular response to external stimuli, cytoskeletal organization and regulation of molecular activity. The website also provides a wiki-like capability allowing users

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.

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 ...

A discriminatory function for prediction of protein-DNA interactions based on alpha shape modeling.

Science.gov (United States)

Zhou, Weiqiang; Yan, Hong

2010-10-15

Protein-DNA interaction has significant importance in many biological processes. However, the underlying principle of the molecular recognition process is still largely unknown. As more high-resolution 3D structures of protein-DNA complex are becoming available, the surface characteristics of the complex become an important research topic. In our work, we apply an alpha shape model to represent the surface structure of the protein-DNA complex and developed an interface-atom curvature-dependent conditional probability discriminatory function for the prediction of protein-DNA interaction. The interface-atom curvature-dependent formalism captures atomic interaction details better than the atomic distance-based method. The proposed method provides good performance in discriminating the native structures from the docking decoy sets, and outperforms the distance-dependent formalism in terms of the z-score. Computer experiment results show that the curvature-dependent formalism with the optimal parameters can achieve a native z-score of -8.17 in discriminating the native structure from the highest surface-complementarity scored decoy set and a native z-score of -7.38 in discriminating the native structure from the lowest RMSD decoy set. The interface-atom curvature-dependent formalism can also be used to predict apo version of DNA-binding proteins. These results suggest that the interface-atom curvature-dependent formalism has a good prediction capability for protein-DNA interactions. The code and data sets are available for download on http://www.hy8.com/bioinformatics.htm kenandzhou@hotmail.com.

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

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

Targeting protein-protein interactions for parasite control.

Directory of Open Access Journals (Sweden)

Christina M Taylor

2011-04-01

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

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

Science.gov (United States)

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

2017-05-25

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

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

Science.gov (United States)

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

2007-08-01

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

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.

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

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Li Min

2012-03-01

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

Building blocks for protein interaction devices

Science.gov (United States)

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

Structural investigation of nucleophosmin interaction with the tumor suppressor Fbw7γ.

Science.gov (United States)

Di Matteo, A; Franceschini, M; Paiardini, A; Grottesi, A; Chiarella, S; Rocchio, S; Di Natale, C; Marasco, D; Vitagliano, L; Travaglini-Allocatelli, C; Federici, L

2017-09-18

Nucleophosmin (NPM1) is a multifunctional nucleolar protein implicated in ribogenesis, centrosome duplication, cell cycle control, regulation of DNA repair and apoptotic response to stress stimuli. The majority of these functions are played through the interactions with a variety of protein partners. NPM1 is frequently overexpressed in solid tumors of different histological origin. Furthermore NPM1 is the most frequently mutated protein in acute myeloid leukemia (AML) patients. Mutations map to the C-terminal domain and lead to the aberrant and stable localization of the protein in the cytoplasm of leukemic blasts. Among NPM1 protein partners, a pivotal role is played by the tumor suppressor Fbw7γ, an E3-ubiquitin ligase that degrades oncoproteins like c-MYC, cyclin E, Notch and c-jun. In AML with NPM1 mutations, Fbw7γ is degraded following its abnormal cytosolic delocalization by mutated NPM1. This mechanism also applies to other tumor suppressors and it has been suggested that it may play a key role in leukemogenesis. Here we analyse the interaction between NPM1 and Fbw7γ, by identifying the protein surfaces implicated in recognition and key aminoacids involved. Based on the results of computational methods, we propose a structural model for the interaction, which is substantiated by experimental findings on several site-directed mutants. We also extend the analysis to two other NPM1 partners (HIV Tat and CENP-W) and conclude that NPM1 uses the same molecular surface as a platform for recognizing different protein partners. We suggest that this region of NPM1 may be targeted for cancer treatment.

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

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Stefan M Ivanov

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

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

Science.gov (United States)

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

2017-01-01

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

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.

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

Science.gov (United States)

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

2018-01-05

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

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

OpenAIRE

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

2008-01-01

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

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

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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.

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)....

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

Science.gov (United States)

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

2008-09-01

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

A two-hybrid assay to study protein interactions within the secretory pathway.

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Danielle H Dube

Full Text Available Interactions of transcriptional activators are difficult to study using transcription-based two-hybrid assays due to potent activation resulting in false positives. Here we report the development of the Golgi two-hybrid (G2H, a method that interrogates protein interactions within the Golgi, where transcriptional activators can be assayed with negligible background. The G2H relies on cell surface glycosylation to report extracellularly on protein-protein interactions occurring within the secretory pathway. In the G2H, protein pairs are fused to modular domains of the reporter glycosyltransferase, Och1p, and proper cell wall formation due to Och1p activity is observed only when a pair of proteins interacts. Cells containing interacting protein pairs are identified by selectable phenotypes associated with Och1p activity and proper cell wall formation: cells that have interacting proteins grow under selective conditions and display weak wheat germ agglutinin (WGA binding by flow cytometry, whereas cells that lack interacting proteins display stunted growth and strong WGA binding. Using this assay, we detected the interaction between transcription factor MyoD and its binding partner Id2. Interfering mutations along the MyoD:Id2 interaction interface ablated signal in the G2H assay. Furthermore, we used the G2H to detect interactions of the activation domain of Gal4p with a variety of binding partners. Finally, selective conditions were used to enrich for cells encoding interacting partners. The G2H detects protein-protein interactions that cannot be identified via traditional two-hybrid methods and should be broadly useful for probing previously inaccessible subsets of the interactome, including transcriptional activators and proteins that traffic through the secretory pathway.

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

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

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

A protein interaction map of the kalimantacin biosynthesis assembly line

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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.

Interactions between whey proteins and salivary proteins as related to astringency of whey protein beverages at low pH.

Science.gov (United States)

Ye, A; Streicher, C; Singh, H

2011-12-01

Whey protein beverages have been shown to be astringent at low pH. In the present study, the interactions between model whey proteins (β-lactoglobulin and lactoferrin) and human saliva in the pH range from 7 to 2 were investigated using particle size, turbidity, and ζ-potential measurements and sodium dodecyl sulfate-PAGE. The correlation between the sensory results of astringency and the physicochemical data was discussed. Strong interactions between β-lactoglobulin and salivary proteins led to an increase in the particle size and turbidity of mixtures of both unheated and heated β-lactoglobulin and human saliva at pH ∼3.4. However, the large particle size and high turbidity that occurred at pH 2.0 were the result of aggregation of human salivary proteins. The intense astringency in whey protein beverages may result from these increases in particle size and turbidity at these pH values and from the aggregation and precipitation of human salivary proteins alone at pH salivary proteins in the interaction is a key factor in the perception of astringency in whey protein beverages. At any pH, the increases in particle size and turbidity were much smaller in mixtures of lactoferrin and saliva, which suggests that aggregation and precipitation may not be the only mechanism linked to the perception of astringency in whey protein. Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

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.

Induction of viral, 7-methyl-guanosine cap-independent translation and oncolysis by mitogen-activated protein kinase-interacting kinase-mediated effects on the serine/arginine-rich protein kinase.

Science.gov (United States)

Brown, Michael C; Bryant, Jeffrey D; Dobrikova, Elena Y; Shveygert, Mayya; Bradrick, Shelton S; Chandramohan, Vidyalakshmi; Bigner, Darell D; Gromeier, Matthias

2014-11-01

Protein synthesis, the most energy-consuming process in cells, responds to changing physiologic priorities, e.g., upon mitogen- or stress-induced adaptations signaled through the mitogen-activated protein kinases (MAPKs). The prevailing status of protein synthesis machinery is a viral pathogenesis factor, particularly for plus-strand RNA viruses, where immediate translation of incoming viral RNAs shapes host-virus interactions. In this study, we unraveled signaling pathways centered on the ERK1/2 and p38α MAPK-interacting kinases MNK1/2 and their role in controlling 7-methyl-guanosine (m(7)G) "cap"-independent translation at enterovirus type 1 internal ribosomal entry sites (IRESs). Activation of Raf-MEK-ERK1/2 signals induced viral IRES-mediated translation in a manner dependent on MNK1/2. This effect was not due to MNK's known functions as eukaryotic initiation factor (eIF) 4G binding partner or eIF4E(S209) kinase. Rather, MNK catalytic activity enabled viral IRES-mediated translation/host cell cytotoxicity through negative regulation of the Ser/Arg (SR)-rich protein kinase (SRPK). Our investigations suggest that SRPK activity is a major determinant of type 1 IRES competency, host cell cytotoxicity, and viral proliferation in infected cells. We are targeting unfettered enterovirus IRES activity in cancer with PVSRIPO, the type 1 live-attenuated poliovirus (PV) (Sabin) vaccine containing a human rhinovirus type 2 (HRV2) IRES. A phase I clinical trial of PVSRIPO with intratumoral inoculation in patients with recurrent glioblastoma (GBM) is showing early promise. Viral translation proficiency in infected GBM cells is a core requirement for the antineoplastic efficacy of PVSRIPO. Therefore, it is critically important to understand the mechanisms controlling viral cap-independent translation in infected host cells. Copyright © 2014, American Society for Microbiology. All Rights Reserved.

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

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

Directory of Open Access Journals (Sweden)

Jung Me Hwang

2011-12-01

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

Protein prenylation: a new mode of host-pathogen interaction.

Science.gov (United States)

Amaya, Moushimi; Baranova, Ancha; van Hoek, Monique L

2011-12-09

Post translational modifications are required for proteins to be fully functional. The three step process, prenylation, leads to farnesylation or geranylgeranylation, which increase the hydrophobicity of the prenylated protein for efficient anchoring into plasma membranes and/or organellar membranes. Prenylated proteins function in a number of signaling and regulatory pathways that are responsible for basic cell operations. Well characterized prenylated proteins include Ras, Rac and Rho. Recently, pathogenic prokaryotic proteins, such as SifA and AnkB, have been shown to be prenylated by eukaryotic host cell machinery, but their functions remain elusive. The identification of other bacterial proteins undergoing this type of host-directed post-translational modification shows promise in elucidating host-pathogen interactions to develop new therapeutics. This review incorporates new advances in the study of protein prenylation into a broader aspect of biology with a focus on host-pathogen interaction. Copyright © 2011 Elsevier Inc. All rights reserved.

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

Science.gov (United States)

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

2010-01-01

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

The Plasmodium falciparum exported protein PF3D7_0402000 binds to erythrocyte ankyrin and band 4.1

Energy Technology Data Exchange (ETDEWEB)

Shakya, Bikash; Penn, Wesley D.; Nakayasu, Ernesto S.; Lacount, Douglas J.

2017-09-01

Plasmodium falciparum extensively modifies the infected red blood cell (RBC), resulting in changes in deformability, shape and surface properties. These alterations suggest that the RBC cytoskeleton is a major target for modification during infection. However, the molecular mechanisms leading to these changes are largely unknown. To begin to address this question, we screened for exported P. falciparum proteins that bound to the erythrocyte cytoskeleton proteins ankyrin 1 (ANK1) and band 4.1 (4.1R), which form critical interactions with other cytoskeletal proteins that contribute to the deformability and stability of RBCs. Yeast two-hybrid screens with ANK1 and 4.1R identified eight interactions with P. falciparum exported proteins, including an interaction between 4.1R and PF3D7_0402000 (PFD0090c). This interaction was first identified in a large-scale screen (Vignali et al., Malaria J, 7:211, 2008), which also reported an interaction between PF3D7_0402000 and ANK1. We confirmed the interactions of PF3D7_0402000 with 4.1R and ANK1 in pair-wise yeast two-hybrid and co-precipitation assays. In both cases, an intact PHIST domain in PF3D7_0402000 was required for binding. Complex purification followed by mass spectrometry analysis provided additional support for the interaction of PF3D7_0402000 with ANK1 and 4.1R. RBC ghost cells loaded with maltose-binding protein (MBP)-PF3D7_0402000 passed through a metal microsphere column less efficiently than mock- or MBP-loaded controls, consistent with an effect of PF3D7_0402000 on RBC rigidity or membrane stability. This study confirmed the interaction of PF3D7_0402000 with 4.1R in multiple independent assays, provided the first evidence that PF3D7_0402000 also binds to ANK1, and suggested that PF3D7_0402000 affects deformability or membrane stability of uninfected RBC ghosts.

Interaction of Proteins Identified in Human Thyroid Cells

Science.gov (United States)

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.

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.

Phthalic Acid Chemical Probes Synthesized for Protein-Protein Interaction Analysis

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Chin-Jen Wu

2013-06-01

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

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

The interactions between CdTe quantum dots and proteins: understanding nano-bio interface

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Shreeram S. Joglekar

2017-01-01

Full Text Available Despite remarkable developments in the nanoscience, relatively little is known about the physical (electrostatic interactions of nanoparticles with bio macromolecules. These interactions can influence the properties of both nanoparticles and the bio-macromolecules. Understanding this bio-interface is a prerequisite to utilize both nanoparticles and biomolecules for bioengineering. In this study, luminescent, water soluble CdTe quantum dots (QDs capped with mercaptopropionic acid (MPA were synthesized by organometallic method and then interaction between nanoparticles (QDs and three different types of proteins (BSA, Lysozyme and Hemoglobin were investigated by fluorescence spectroscopy at pH= 7.4. Based on fluorescence quenching results, Stern-Volmer quenching constant (Ksv, binding constant (Kq and binding sites (n for proteins were calculated. The results show that protein structure (e.g.,globular, metalloprotein, etc. has a significant role in Protein-Quantum dots interactions and each type of protein influence physicochemical properties of Quantum dots differently.

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

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

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

2009-12-01

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

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

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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.

Scoring functions for protein-protein interactions.

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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.

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.

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.

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

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

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

2011-05-01

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

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.

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.

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

Lifescience Database Archive (English)

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

Expression and purification of recombinant polyomavirus VP2 protein and its interactions with polyomavirus proteins

Science.gov (United States)

Cai, X.; Chang, D.; Rottinghaus, S.; Consigli, R. A.; Spooner, B. S. (Principal Investigator)

1994-01-01

Recombinant polyomavirus VP2 protein was expressed in Escherichia coli (RK1448), using the recombinant expression system pFPYV2. Recombinant VP2 was purified to near homogeneity by sodium dodecyl sulfate-polyacrylamide gel electrophoresis, electroelution, and Extracti-Gel chromatography. Polyclonal serum to this protein which reacted specifically with recombinant VP2 as well as polyomavirus virion VP2 and VP3 on Western blots (immunoblots) was produced. Purified VP2 was used to establish an in vitro protein-protein interaction assay with polyomavirus structural proteins and purified recombinant VP1. Recombinant VP2 interacted with recombinant VP1, virion VP1, and the four virion histones. Recombinant VP1 coimmunoprecipitated with recombinant VP2 or truncated VP2 (delta C12VP2), which lacked the carboxy-terminal 12 amino acids. These experiments confirmed the interaction between VP1 and VP2 and revealed that the carboxyterminal 12 amino acids of VP2 and VP3 were not necessary for formation of this interaction. In vivo VP1-VP2 interaction study accomplished by cotransfection of COS-7 cells with VP2 and truncated VP1 (delta N11VP1) lacking the nuclear localization signal demonstrated that VP2 was capable of translocating delta N11VP1 into the nucleus. These studies suggest that complexes of VP1 and VP2 may be formed in the cytoplasm and cotransported to the nucleus for virion assembly to occur.

Identification of Essential Proteins Based on a New Combination of Local Interaction Density and Protein Complexes.

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Jiawei Luo

Full Text Available Computational approaches aided by computer science have been used to predict essential proteins and are faster than expensive, time-consuming, laborious experimental approaches. However, the performance of such approaches is still poor, making practical applications of computational approaches difficult in some fields. Hence, the development of more suitable and efficient computing methods is necessary for identification of essential proteins.In this paper, we propose a new method for predicting essential proteins in a protein interaction network, local interaction density combined with protein complexes (LIDC, based on statistical analyses of essential proteins and protein complexes. First, we introduce a new local topological centrality, local interaction density (LID, of the yeast PPI network; second, we discuss a new integration strategy for multiple bioinformatics. The LIDC method was then developed through a combination of LID and protein complex information based on our new integration strategy. The purpose of LIDC is discovery of important features of essential proteins with their neighbors in real protein complexes, thereby improving the efficiency of identification.Experimental results based on three different PPI(protein-protein interaction networks of Saccharomyces cerevisiae and Escherichia coli showed that LIDC outperformed classical topological centrality measures and some recent combinational methods. Moreover, when predicting MIPS datasets, the better improvement of performance obtained by LIDC is over all nine reference methods (i.e., DC, BC, NC, LID, PeC, CoEWC, WDC, ION, and UC.LIDC is more effective for the prediction of essential proteins than other recently developed methods.

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

Science.gov (United States)

Gong, Wei; He, Kun; Covington, Mike; Dinesh-Kumar, S. P.; Snyder, Michael; Harmer, Stacey L.; Zhu, Yu-Xian; Deng, Xing Wang

2009-01-01

We used our collection of Arabidopsis transcription factor (TF) ORFeome clones to construct protein microarrays containing as many as 802 TF proteins. These protein microarrays were used for both protein-DNA and protein-protein interaction analyses. For protein-DNA interaction studies, we examined AP2/ERF family TFs and their cognate cis-elements. By careful comparison of the DNA-binding specificity of 13 TFs on the protein microarray with previous non-microarray data, we showed that protein microarrays provide an efficient and high throughput tool for genome-wide analysis of TF-DNA interactions. This microarray protein-DNA interaction analysis allowed us to derive a comprehensive view of DNA-binding profiles of AP2/ERF family proteins in Arabidopsis. It also revealed four TFs that bound the EE (evening element) and had the expected phased gene expression under clock-regulation, thus providing a basis for further functional analysis of their roles in clock regulation of gene expression. We also developed procedures for detecting protein interactions using this TF protein microarray and discovered four novel partners that interact with HY5, which can be validated by yeast two-hybrid assays. Thus, plant TF protein microarrays offer an attractive high-throughput alternative to traditional techniques for TF functional characterization on a global scale. PMID:19802365

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

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Lei Yang

2014-01-01

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

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

Science.gov (United States)

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

2009-04-22

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

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

Science.gov (United States)

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

2004-03-15

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

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.

Protein-protein interactions as a strategy towards protein-specific drug design: the example of ataxin-1.

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Cesira de Chiara

Full Text Available A main challenge for structural biologists is to understand the mechanisms that discriminate between molecular interactions and determine function. Here, we show how partner recognition of the AXH domain of the transcriptional co-regulator ataxin-1 is fine-tuned by a subtle balance between self- and hetero-associations. Ataxin-1 is the protein responsible for the hereditary spinocerebellar ataxia type 1, a disease linked to protein aggregation and transcriptional dysregulation. Expansion of a polyglutamine tract is essential for ataxin-1 aggregation, but the sequence-wise distant AXH domain plays an important aggravating role in the process. The AXH domain is also a key element for non-aberrant function as it intervenes in interactions with multiple protein partners. Previous data have shown that AXH is dimeric in solution and forms a dimer of dimers when crystallized. By solving the structure of a complex of AXH with a peptide from the interacting transcriptional repressor CIC, we show that the dimer interface of AXH is displaced by the new interaction and that, when blocked by the CIC peptide AXH aggregation and misfolding are impaired. This is a unique example in which palindromic self- and hetero-interactions within a sequence with chameleon properties discriminate the partner. We propose a drug design strategy for the treatment of SCA1 that is based on the information gained from the AXH/CIC complex.

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

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

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

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Moschopoulos Charalampos

2011-06-01

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

Influenza A Virus-Host Protein Interactions Control Viral Pathogenesis.

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Zhao, Mengmeng; Wang, Lingyan; Li, Shitao

2017-08-01

The influenza A virus (IAV), a member of the Orthomyxoviridae family, is a highly transmissible respiratory pathogen and represents a continued threat to global health with considerable economic and social impact. IAV is a zoonotic virus that comprises a plethora of strains with different pathogenic profiles. The different outcomes of viral pathogenesis are dependent on the engagement between the virus and the host cellular protein interaction network. The interactions may facilitate virus hijacking of host molecular machinery to fulfill the viral life cycle or trigger host immune defense to eliminate the virus. In recent years, much effort has been made to discover the virus-host protein interactions and understand the underlying mechanisms. In this paper, we review the recent advances in our understanding of IAV-host interactions and how these interactions contribute to host defense and viral pathogenesis.

Identification of the interaction and interaction domains of chicken anemia virus VP2 and VP3 proteins.

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Sun, Fenfen; Pan, Wei; Gao, Honglei; Qi, Xiaole; Qin, Liting; Wang, Yongqiang; Gao, Yulong; Wang, Xiaomei

2018-01-01

Chicken anemia virus (CAV) is a small, single-stranded DNA virus of Anelloviridae family. Its genome segments encode three proteins, VP1, VP2, and VP3. This study identified an interaction between VP2 and VP3 and mapped the interaction domains. Through the yeast two-hybrid (Y2H) system, VP2 was found to interact with VP3. The presence of the VP2-VP3 complex in CAV-infected chicken cells was confirmed by co-immunoprecipitation. Confocal microscopy showed that VP2 and VP3 were expressed in the cytoplasm in cotransfected Vero cells. In the Y2H system, the interaction domains were identified as being within the N-terminal aa 1-30 and C-terminal aa 17-60 for VP2 and the N-terminal aa 46-60 and C-terminal aa 1-7 for VP3. This study showed the interaction between VP2 and VP3 of CAV and identified multiple independent interactive domains within the two proteins. This provides novel information for investigating the biological functions of these proteins. Copyright © 2017. Published by Elsevier Inc.

The prion protein has RNA binding and chaperoning properties characteristic of nucleocapsid protein NCP7 of HIV-1.

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Gabus, C; Derrington, E; Leblanc, P; Chnaiderman, J; Dormont, D; Swietnicki, W; Morillas, M; Surewicz, W K; Marc, D; Nandi, P; Darlix, J L

2001-06-01

Transmissible spongiform encephalopathies are fatal neurodegenerative diseases associated with the accumulation of a protease-resistant form of the prion protein (PrP). Although PrP is conserved in vertebrates, its function remains to be identified. In vitro PrP binds large nucleic acids causing the formation of nucleoprotein complexes resembling human immunodeficiency virus type 1 (HIV-1) nucleocapsid-RNA complexes and in vivo MuLV replication accelerates the scrapie infectious process, suggesting possible interactions between retroviruses and PrP. Retroviruses, including HIV-1 encode a major nucleic acid binding protein (NC protein) found within the virus where 2000 NC protein molecules coat the dimeric genome. NC is required in virus assembly and infection to chaperone RNA dimerization and packaging and in proviral DNA synthesis by reverse transcriptase (RT). In HIV-1, 5'-leader RNA/NC interactions appear to control these viral processes. This prompted us to compare and contrast the interactions of human and ovine PrP and HIV-1 NCp7 with HIV-1 5'-leader RNA. Results show that PrP has properties characteristic of NCp7 with respect to viral RNA dimerization and proviral DNA synthesis by RT. The NC-like properties of huPrP map to the N-terminal region of huPrP. Interestingly, PrP localizes in the membrane and cytoplasm of PrP-expressing cells. These findings suggest that PrP is a multifunctional protein possibly participating in nucleic acid metabolism.

Fibulin-1C, C1 Esterase Inhibitor and Glucose Regulated Protein 75 Interact with the CREC Proteins, Calumenin and Reticulocalbin.

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Gry Aune Westergaard Hansen

Full Text Available Affinity purification, immunoprecipitation, gel electrophoresis and mass spectrometry were used to identify fibulin-1C, C1 esterase inhibitor and glucose regulated protein 75, grp75, as binding partners of the CREC proteins, calumenin and reticulocalbin. Surface plasmon resonance was used to verify the interaction of all three proteins with each of the CREC proteins. Fibulin-1C interacts with calumenin and reticulocalbin with an estimated dissociation constant around 50-60 nM. The interaction, at least for reticulocalbin, was not dependent upon the presence of Ca2+. C1 esterase inhibitor interacted with both proteins with an estimated dissociation constant at 1 μM for reticulocalbin and 150 nM for calumenin. The interaction, at least for calumenin, was dependent upon the presence of Ca2+ with strong interaction at 3.5 mM while no detectable interaction could be found at 0.1 mM. Grp75 binds with an affinity of approximately 3-7 nM with reticulocalbin as well as with calumenin. These interactions suggest functional participation of the CREC proteins in chaperone activity, cell proliferation and transformation, cellular aging, haemostasis and thrombosis as well as modulation of the complement system in fighting bacterial infection.

Interaction of Myosin Phosphatase Target Subunit (MYPT1) with Myosin Phosphatase-RhoA Interacting Protein (MRIP): A Role of Glutamic Acids in the Interaction.

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Lee, Eunhee; Stafford, Walter F

2015-01-01

Scaffold proteins bind to and functionally link protein members of signaling pathways. Interaction of the scaffold proteins, myosin phosphatase target subunit (MYPT1) and myosin phosphatase-RhoA interacting protein (MRIP), causes co-localization of myosin phosphatase and RhoA to actomyosin. To examine biophysical properties of interaction of MYPT1 with MRIP, we employed analytical ultracentrifugation and surface plasmon resonance. In regard to MRIP, its residues 724-837 are sufficient for the MYPT1/MRIP interaction. Moreover, MRIP binds to MYPT1 as either a monomer or a dimer. With respect to MYPT1, its leucine repeat region, LR (residues 991-1030) is sufficient to account for the MYPT1/MRIP interaction. Furthermore, point mutations that replace glutamic acids 998-1000 within LR reduced the binding affinity toward MRIP. This suggests that the glutamic acids of MYPT1 play an important role in the interaction.

A lock-and-key model for protein–protein interactions

NARCIS (Netherlands)

Morrison, Julie L.; Breitling, Rainer; Higham, Desmond J.; Gilbert, David R.

2006-01-01

Motivation: Protein–protein interaction networks are one of the major post-genomic data sources available to molecular biologists. They provide a comprehensive view of the global interaction structure of an organism’s proteome, as well as detailed information on specific interactions. Here we

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

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Lei Li

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

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.

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.

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

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

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

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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.

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

Analysis of Protein-Membrane Interactions

DEFF Research Database (Denmark)

Kemmer, Gerdi Christine

Cellular membranes are complex structures, consisting of hundreds of different lipids and proteins. These membranes act as barriers between distinct environments, constituting hot spots for many essential functions of the cell, including signaling, energy conversion, and transport. These functions....... Discovered interactions were then probed on the level of the membrane using liposome-based assays. In the second part, a transmembrane protein was investigated. Assays to probe activity of the plasma membrane ATPase (Arabidopsis thaliana H+ -ATPase isoform 2 (AHA2)) in single liposomes using both giant...... are implemented by soluble proteins reversibly binding to, as well as by integral membrane proteins embedded in, cellular membranes. The activity and interaction of these proteins is furthermore modulated by the lipids of the membrane. Here, liposomes were used as model membrane systems to investigate...

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

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

2011-06-01

Physics approaches focus on uncovering, modeling and quantitating the general principles governing the micro and macro universe. This has always been an important component of biological research, however recent advances in experimental techniques and the accumulation of unprecedented genome-scale experimental data produced by these novel technologies now allow for addressing fundamental questions on a large scale. These relate to molecular interactions, principles of bimolecular recognition, and mechanisms of signal propagation. The functioning of a cell requires a variety of intermolecular interactions including protein-protein, protein-DNA, protein-RNA, hormones, peptides, small molecules, lipids and more. Biomolecules work together to provide specific functions and perturbations in intermolecular communication channels often lead to cellular malfunction and disease. A full understanding of the interactome requires an in-depth grasp of the biophysical principles underlying individual interactions as well as their organization in cellular networks. Phenomena can be described at different levels of abstraction. Computational and systems biology strive to model cellular processes by integrating and analyzing complex data from multiple experimental sources using interdisciplinary tools. As a result, both the causal relationships between the variables and the general features of the system can be discovered, which even without knowing the details of the underlying mechanisms allow for putting forth hypotheses and predicting the behavior of the systems in response to perturbation. And here lies the strength of in silico models which provide control and predictive power. At the same time, the complexity of individual elements and molecules can be addressed by the fields of molecular biophysics, physical biology and structural biology, which focus on the underlying physico-chemical principles and may explain the molecular mechanisms of cellular function. In this issue

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.

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.

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

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Kathrin Roderer

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

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

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

2014-01-01

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

Protein and Drug Interactions in the Minor Groove of DNA

Czech Academy of Sciences Publication Activity Database

Morávek, Z.; Neidle, S.; Schneider, Bohdan

2002-01-01

Roč. 30, č. 5 (2002), s. 1182-1191 ISSN 0305-1048 R&D Projects: GA MŠk LN00A032 Institutional research plan: CEZ:AV0Z4040901 Keywords : protein * DNA * interactions Subject RIV: CF - Physical ; Theoretical Chemistry Impact factor: 7.051, year: 2002

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.

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.

Expressed proteins of Herbaspirillum seropedicae in maize (DKB240) roots-bacteria interaction revealed using proteomics.

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Ferrari, Cibele Santos; Amaral, Fernanda Plucani; Bueno, Jessica Cavalheiro Ferreira; Scariot, Mirella Christine; Valentim-Neto, Pedro Alexandre; Arisi, Ana Carolina Maisonnave

2014-11-01

Several molecular tools have been used to clarify the basis of plant-bacteria interaction; however, the mechanism behind the association is still unclear. In this study, we used a proteomic approach to investigate the root proteome of Zea mays (cv. DKB240) inoculated with Herbaspirillum seropedicae strain SmR1 grown in vitro and harvested 7 days after inoculation. Eighteen differentially accumulated proteins were observed in root samples, ten of which were identified by MALDI-TOF mass spectrometry peptide mass fingerprint. Among the identified proteins, we observed three proteins present exclusively in inoculated root samples and six upregulated proteins and one downregulated protein relative to control. Differentially expressed maize proteins were identified as hypothetical protein ZEAMMB73_483204, hypothetical protein ZEAMMB73_269466, and tubulin beta-7 chain. The following were identified as H. seropedicae proteins: peroxiredoxin protein, EF-Tu elongation factor protein, cation transport ATPase, NADPH:quinone oxidoreductase, dinitrogenase reductase, and type III secretion ATP synthase. Our results presented the first evidence of type III secretion ATP synthase expression during H. seropedicae-maize root interaction.

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.

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

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 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

Computational design, construction, and characterization of a set of specificity determining residues in protein-protein interactions.

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Nagao, Chioko; Izako, Nozomi; Soga, Shinji; Khan, Samia Haseeb; Kawabata, Shigeki; Shirai, Hiroki; Mizuguchi, Kenji

2012-10-01

Proteins interact with different partners to perform different functions and it is important to elucidate the determinants of partner specificity in protein complex formation. Although methods for detecting specificity determining positions have been developed previously, direct experimental evidence for these amino acid residues is scarce, and the lack of information has prevented further computational studies. In this article, we constructed a dataset that is likely to exhibit specificity in protein complex formation, based on available crystal structures and several intuitive ideas about interaction profiles and functional subclasses. We then defined a "structure-based specificity determining position (sbSDP)" as a set of equivalent residues in a protein family showing a large variation in their interaction energy with different partners. We investigated sequence and structural features of sbSDPs and demonstrated that their amino acid propensities significantly differed from those of other interacting residues and that the importance of many of these residues for determining specificity had been verified experimentally. Copyright © 2012 Wiley Periodicals, Inc.

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...

BMRF-Net: a software tool for identification of protein interaction subnetworks by a bagging Markov random field-based method.

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Shi, Xu; Barnes, Robert O; Chen, Li; Shajahan-Haq, Ayesha N; Hilakivi-Clarke, Leena; Clarke, Robert; Wang, Yue; Xuan, Jianhua

2015-07-15

Identification of protein interaction subnetworks is an important step to help us understand complex molecular mechanisms in cancer. In this paper, we develop a BMRF-Net package, implemented in Java and C++, to identify protein interaction subnetworks based on a bagging Markov random field (BMRF) framework. By integrating gene expression data and protein-protein interaction data, this software tool can be used to identify biologically meaningful subnetworks. A user friendly graphic user interface is developed as a Cytoscape plugin for the BMRF-Net software to deal with the input/output interface. The detailed structure of the identified networks can be visualized in Cytoscape conveniently. The BMRF-Net package has been applied to breast cancer data to identify significant subnetworks related to breast cancer recurrence. The BMRF-Net package is available at http://sourceforge.net/projects/bmrfcjava/. The package is tested under Ubuntu 12.04 (64-bit), Java 7, glibc 2.15 and Cytoscape 3.1.0. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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

Directory of Open Access Journals (Sweden)

Lin Yen-Han

2007-07-01

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

Human HOX Proteins Use Diverse and Context-Dependent Motifs to Interact with TALE Class Cofactors.

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Dard, Amélie; Reboulet, Jonathan; Jia, Yunlong; Bleicher, Françoise; Duffraisse, Marilyne; Vanaker, Jean-Marc; Forcet, Christelle; Merabet, Samir

2018-03-13

HOX proteins achieve numerous functions by interacting with the TALE class PBX and MEIS cofactors. In contrast to this established partnership in development and disease, how HOX proteins could interact with PBX and MEIS remains unclear. Here, we present a systematic analysis of HOX/PBX/MEIS interaction properties, scanning all paralog groups with human and mouse HOX proteins in vitro and in live cells. We demonstrate that a previously characterized HOX protein motif known to be critical for HOX-PBX interactions becomes dispensable in the presence of MEIS in all except the two most anterior paralog groups. We further identify paralog-specific TALE-binding sites that are used in a highly context-dependent manner. One of these binding sites is involved in the proliferative activity of HOXA7 in breast cancer cells. Together these findings reveal an extraordinary level of interaction flexibility between HOX proteins and their major class of developmental cofactors. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

Interaction between plate make and protein in protein crystallisation screening.

Directory of Open Access Journals (Sweden)

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.

RAID: a comprehensive resource for human RNA-associated (RNA–RNA/RNA–protein) interaction

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