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Protein–Protein Interaction Hotspots Carved into Sequences

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  • Yanay Ofran
  • Burkhard Rost

Abstract

Protein–protein interactions, a key to almost any biological process, are mediated by molecular mechanisms that are not entirely clear. The study of these mechanisms often focuses on all residues at protein–protein interfaces. However, only a small subset of all interface residues is actually essential for recognition or binding. Commonly referred to as “hotspots,” these essential residues are defined as residues that impede protein–protein interactions if mutated. While no in silico tool identifies hotspots in unbound chains, numerous prediction methods were designed to identify all the residues in a protein that are likely to be a part of protein–protein interfaces. These methods typically identify successfully only a small fraction of all interface residues. Here, we analyzed the hypothesis that the two subsets correspond (i.e., that in silico methods may predict few residues because they preferentially predict hotspots). We demonstrate that this is indeed the case and that we can therefore predict directly from the sequence of a single protein which residues are interaction hotspots (without knowledge of the interaction partner). Our results suggested that most protein complexes are stabilized by similar basic principles. The ability to accurately and efficiently identify hotspots from sequence enables the annotation and analysis of protein–protein interaction hotspots in entire organisms and thus may benefit function prediction and drug development. The server for prediction is available at http://www.rostlab.org/services/isis.: Interactions between proteins underlie all biological processes. Hence, to fully understand or to control biological processes we need to unravel the principles of protein interactions. The quest for these principles has focused predominantly on the entire interfaces between two interacting proteins. However, it has been shown that only few of the interface residues are essential for the recognition and binding to other proteins. The identification of these residues, commonly referred to as binding “hotspots,” is a first step toward understanding the function of proteins and studying their interactions. Experimentally, hotspots could be identified by mutating single residues—an expensive and laborious procedure that is not applicable on a large scale. Here, we show that it is possible to identify protein interaction hotspots computationally on a large scale based on the amino acid sequence of a single protein, without requiring the knowledge of its interaction partner. Our results suggest that most protein complexes are stabilized by similar basic principles. The ability to accurately and efficiently identify hotspots from sequence enables the annotation and analysis of protein–protein interaction hotspots in an entire organism and thus may benefit function prediction and drug development.

Suggested Citation

  • Yanay Ofran & Burkhard Rost, 2007. "Protein–Protein Interaction Hotspots Carved into Sequences," PLOS Computational Biology, Public Library of Science, vol. 3(7), pages 1-8, July.
  • Handle: RePEc:plo:pcbi00:0030119
    DOI: 10.1371/journal.pcbi.0030119
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    1. Peter D. Kwong & Richard Wyatt & James Robinson & Raymond W. Sweet & Joseph Sodroski & Wayne A. Hendrickson, 1998. "Structure of an HIV gp120 envelope glycoprotein in complex with the CD4 receptor and a neutralizing human antibody," Nature, Nature, vol. 393(6686), pages 648-659, June.
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    Cited by:

    1. John A Capra & Roman A Laskowski & Janet M Thornton & Mona Singh & Thomas A Funkhouser, 2009. "Predicting Protein Ligand Binding Sites by Combining Evolutionary Sequence Conservation and 3D Structure," PLOS Computational Biology, Public Library of Science, vol. 5(12), pages 1-18, December.
    2. Chittibabu Guda & Brian R King & Lipika R Pal & Purnima Guda, 2009. "A Top-Down Approach to Infer and Compare Domain-Domain Interactions across Eight Model Organisms," PLOS ONE, Public Library of Science, vol. 4(3), pages 1-15, March.
    3. Mahdi Sarmady & William Dampier & Aydin Tozeren, 2011. "Sequence- and Interactome-Based Prediction of Viral Protein Hotspots Targeting Host Proteins: A Case Study for HIV Nef," PLOS ONE, Public Library of Science, vol. 6(6), pages 1-11, June.
    4. Rudi Agius & Mieczyslaw Torchala & Iain H Moal & Juan Fernández-Recio & Paul A Bates, 2013. "Characterizing Changes in the Rate of Protein-Protein Dissociation upon Interface Mutation Using Hotspot Energy and Organization," PLOS Computational Biology, Public Library of Science, vol. 9(9), pages 1-27, September.
    5. Jiangning Song & Hao Tan & Andrew J Perry & Tatsuya Akutsu & Geoffrey I Webb & James C Whisstock & Robert N Pike, 2012. "PROSPER: An Integrated Feature-Based Tool for Predicting Protease Substrate Cleavage Sites," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-23, November.
    6. Jiangning Song & Hao Tan & Mingjun Wang & Geoffrey I Webb & Tatsuya Akutsu, 2012. "TANGLE: Two-Level Support Vector Regression Approach for Protein Backbone Torsion Angle Prediction from Primary Sequences," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-16, February.
    7. Sanjana Sudarshan & Sasi B Kodathala & Amruta C Mahadik & Isha Mehta & Brian W Beck, 2014. "Protein-Protein Interface Detection Using the Energy Centrality Relationship (ECR) Characteristic of Proteins," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-13, May.

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