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      Contacts-based prediction of binding affinity in protein–protein complexes

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          Abstract

          Almost all critical functions in cells rely on specific protein–protein interactions. Understanding these is therefore crucial in the investigation of biological systems. Despite all past efforts, we still lack a thorough understanding of the energetics of association of proteins. Here, we introduce a new and simple approach to predict binding affinity based on functional and structural features of the biological system, namely the network of interfacial contacts. We assess its performance against a protein–protein binding affinity benchmark and show that both experimental methods used for affinity measurements and conformational changes have a strong impact on prediction accuracy. Using a subset of complexes with reliable experimental binding affinities and combining our contacts and contact-types-based model with recent observations on the role of the non -interacting surface in protein–protein interactions, we reach a high prediction accuracy for such a diverse dataset outperforming all other tested methods.

          DOI: http://dx.doi.org/10.7554/eLife.07454.001

          eLife digest

          Proteins help to copy DNA, transport nutrients and perform many other important roles in cells. To perform these tasks, proteins often interact with other proteins and work together. These interactions can be very complex because each protein has a three-dimensional shape that may change when it binds to other proteins. Also, two proteins may form several connections with each other.

          It is possible to carry out experiments to calculate how likely it is that two proteins will physically interact with each other and how strong their connections will be. However, these measurements are time consuming and costly to do. Some researchers have developed computer models to help predict the interactions between proteins, but these models are often incorrect because they leave out some of the chemical or physical properties that influence the ability of proteins to interact.

          With the aim of making a better model, Vangone and Bonvin examined 122 different combinations of proteins whose abilities to interact had previously been experimentally measured. Vangone and Bonvin found that the number of connections between each pair of proteins was a strong predictor of how tightly the proteins connect to each other. Particular features of the surface of the proteins—specifically, the region defined as the non-interacting surface—can also influence how strong the interaction is.

          Vangone and Bonvin used this information to develop a new model that predicts how tightly proteins interact with each other based on the number of connections between the two proteins and the characteristics of the non-interacting surface. The model is simple, and Vangone and Bonvin show that it is more accurate than previous models. Defects in the interactions between proteins can lead to many diseases in humans, so this model may be useful for the development of new drugs to treat these conditions.

          DOI: http://dx.doi.org/10.7554/eLife.07454.002

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          Most cited references50

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          The cell as a collection of protein machines: preparing the next generation of molecular biologists.

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            FireDock: fast interaction refinement in molecular docking.

            Here, we present FireDock, an efficient method for the refinement and rescoring of rigid-body docking solutions. The refinement process consists of two main steps: (1) rearrangement of the interface side-chains and (2) adjustment of the relative orientation of the molecules. Our method accounts for the observation that most interface residues that are important in recognition and binding do not change their conformation significantly upon complexation. Allowing full side-chain flexibility, a common procedure in refinement methods, often causes excessive conformational changes. These changes may distort preformed structural signatures, which have been shown to be important for binding recognition. Here, we restrict side-chain movements, and thus manage to reduce the false-positive rate noticeably. In the later stages of our procedure (orientation adjustments and scoring), we smooth the atomic radii. This allows for the minor backbone and side-chain movements and increases the sensitivity of our algorithm. FireDock succeeds in ranking a near-native structure within the top 15 predictions for 83% of the 30 enzyme-inhibitor test cases, and for 78% of the 18 semiunbound antibody-antigen complexes. Our refinement procedure significantly improves the ranking of the rigid-body PatchDock algorithm for these cases. The FireDock program is fully automated. In particular, to our knowledge, FireDock's prediction results are comparable to current state-of-the-art refinement methods while its running time is significantly lower. The method is available at http://bioinfo3d.cs.tau.ac.il/FireDock/. 2007 Wiley-Liss, Inc.
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              Principles of protein-protein recognition.

              The formation of the protein-protein interface by the insulin dimer, the trypsin-PTI complex and the alphabeta oxyhaemoglobin dimer removes 1,130-1,720 A2 of accessible surface from contact with water. The residues forming the interface are close packed: each occupies the same volume as it does in crystals of amino acids. These results indicate that hydrophobicity is the major factor stabilising protein-protein association, while complementarily plays a selective role in deciding which proteins may associate.
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                Author and article information

                Contributors
                Role: Reviewing editor
                Journal
                eLife
                eLife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                2050-084X
                20 July 2015
                2015
                : 4
                : e07454
                Affiliations
                [1 ]deptComputational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science—Chemistry , Utrecht University , Utrecht, Netherlands
                Stanford University , United States
                Stanford University , United States
                Author notes
                [* ]For correspondence: a.m.j.j.bonvin@ 123456uu.nl
                Article
                07454
                10.7554/eLife.07454
                4523921
                26193119
                79e4d8c2-f6e7-4ce9-81ab-ed79581fcfea
                © 2015, Vangone and Bonvin

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 12 March 2015
                : 08 July 2015
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004963, Seventh Framework Programme;
                Award ID: WeNMR e-Infrastrure project, grant 261572
                Award Recipient :
                The funder had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Biophysics and Structural Biology
                Custom metadata
                2.3
                The number of contacts at the interface of a protein–protein complex, together with the properties of the surface, provides a simple, but well-performing predictor of binding affinity.

                Life sciences
                protein–protein complexes,binding affinity,protein contacts,buried surface area,protein–protein interactions,non-interacting surface,none

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