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Volzhenin K, Bittner L, Carbone A. SENSE-PPI reconstructs interactomes within, across, and between species at the genome scale. iScience 2024; 27:110371. [PMID: 39055916 PMCID: PMC11269938 DOI: 10.1016/j.isci.2024.110371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 05/04/2024] [Accepted: 06/21/2024] [Indexed: 07/28/2024] Open
Abstract
Ab initio computational reconstructions of protein-protein interaction (PPI) networks will provide invaluable insights into cellular systems, enabling the discovery of novel molecular interactions and elucidating biological mechanisms within and between organisms. Leveraging the latest generation protein language models and recurrent neural networks, we present SENSE-PPI, a sequence-based deep learning model that efficiently reconstructs ab initio PPIs, distinguishing partners among tens of thousands of proteins and identifying specific interactions within functionally similar proteins. SENSE-PPI demonstrates high accuracy, limited training requirements, and versatility in cross-species predictions, even with non-model organisms and human-virus interactions. Its performance decreases for phylogenetically more distant model and non-model organisms, but signal alteration is very slow. In this regard, it demonstrates the important role of parameters in protein language models. SENSE-PPI is very fast and can test 10,000 proteins against themselves in a matter of hours, enabling the reconstruction of genome-wide proteomes.
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Affiliation(s)
- Konstantin Volzhenin
- Sorbonne Université, CNRS, IBPS, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), 75005 Paris, France
| | - Lucie Bittner
- Institut de Systématique, Evolution, Biodiversité (ISYEB), Muséum national d’Histoire naturelle, CNRS, Sorbonne Université, EPHE, Université des Antilles, Paris, France
- Institut Universitaire de France, Paris, France
| | - Alessandra Carbone
- Sorbonne Université, CNRS, IBPS, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), 75005 Paris, France
- Institut Universitaire de France, Paris, France
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2
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Nandigrami P, Fiser A. Assessing the functional impact of protein binding site definition. Protein Sci 2024; 33:e5026. [PMID: 38757384 PMCID: PMC11099757 DOI: 10.1002/pro.5026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 05/01/2024] [Accepted: 05/03/2024] [Indexed: 05/18/2024]
Abstract
Many biomedical applications, such as classification of binding specificities or bioengineering, depend on the accurate definition of protein binding interfaces. Depending on the choice of method used, substantially different sets of residues can be classified as belonging to the interface of a protein. A typical approach used to verify these definitions is to mutate residues and measure the impact of these changes on binding. Besides the lack of exhaustive data, this approach also suffers from the fundamental problem that a mutation introduces an unknown amount of alteration into an interface, which potentially alters the binding characteristics of the interface. In this study we explore the impact of alternative binding site definitions on the ability of a protein to recognize its cognate ligand using a pharmacophore approach, which does not affect the interface. The study also shows that methods for protein binding interface predictions should perform above approximately F-score = 0.7 accuracy level to capture the biological function of a protein.
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Affiliation(s)
- Prithviraj Nandigrami
- Departments of Systems and Computational Biology, and BiochemistryAlbert Einstein College of MedicineBronxNew YorkUSA
| | - Andras Fiser
- Departments of Systems and Computational Biology, and BiochemistryAlbert Einstein College of MedicineBronxNew YorkUSA
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3
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Mohseni Behbahani Y, Crouzet S, Laine E, Carbone A. Deep Local Analysis evaluates protein docking conformations with locally oriented cubes. Bioinformatics 2022; 38:4505-4512. [PMID: 35962985 PMCID: PMC9525006 DOI: 10.1093/bioinformatics/btac551] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 07/04/2022] [Accepted: 08/08/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION With the recent advances in protein 3D structure prediction, protein interactions are becoming more central than ever before. Here, we address the problem of determining how proteins interact with one another. More specifically, we investigate the possibility of discriminating near-native protein complex conformations from incorrect ones by exploiting local environments around interfacial residues. RESULTS Deep Local Analysis (DLA)-Ranker is a deep learning framework applying 3D convolutions to a set of locally oriented cubes representing the protein interface. It explicitly considers the local geometry of the interfacial residues along with their neighboring atoms and the regions of the interface with different solvent accessibility. We assessed its performance on three docking benchmarks made of half a million acceptable and incorrect conformations. We show that DLA-Ranker successfully identifies near-native conformations from ensembles generated by molecular docking. It surpasses or competes with other deep learning-based scoring functions. We also showcase its usefulness to discover alternative interfaces. AVAILABILITY AND IMPLEMENTATION http://gitlab.lcqb.upmc.fr/dla-ranker/DLA-Ranker.git. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yasser Mohseni Behbahani
- Sorbonne Université, CNRS, IBPS, Laboratory of Computational and Quantitative Biology (LCQB), UMR 7238, Paris 75005, France
| | - Simon Crouzet
- Sorbonne Université, CNRS, IBPS, Laboratory of Computational and Quantitative Biology (LCQB), UMR 7238, Paris 75005, France
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4
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Krzemińska A, Kwiatos N, Arenhart Soares F, Steinbüchel A. Theoretical Studies of Cyanophycin Dipeptides as Inhibitors of Tyrosinases. Int J Mol Sci 2022; 23:ijms23063335. [PMID: 35328756 PMCID: PMC8950311 DOI: 10.3390/ijms23063335] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 03/13/2022] [Accepted: 03/17/2022] [Indexed: 02/06/2023] Open
Abstract
The three-dimensional structure of tyrosinase has been crystallized from many species but not from Homo sapiens. Tyrosinase is a key enzyme in melanin biosynthesis, being an important target for melanoma and skin-whitening cosmetics. Several studies employed the structure of tyrosinase from Agaricus bisporus as a model enzyme. Recently, 98% of human genome proteins were elucidated by AlphaFold. Herein, the AlphaFold structure of human tyrosinase and the previous model were compared. Moreover, tyrosinase-related proteins 1 and 2 were included, along with inhibition studies employing kojic and cinnamic acids. Peptides are widely studied for their inhibitory activity of skin-related enzymes. Cyanophycin is an amino acid polymer produced by cyanobacteria and is built of aspartic acid and arginine; arginine can be also replaced by other amino acids. A new set of cyanophycin-derived dipeptides was evaluated as potential inhibitors. Aspartate–glutamate showed the strongest interaction and was chosen as a leading compound for future studies.
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5
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From complete cross-docking to partners identification and binding sites predictions. PLoS Comput Biol 2022; 18:e1009825. [PMID: 35089918 PMCID: PMC8827487 DOI: 10.1371/journal.pcbi.1009825] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Revised: 02/09/2022] [Accepted: 01/11/2022] [Indexed: 11/19/2022] Open
Abstract
Proteins ensure their biological functions by interacting with each other. Hence, characterising protein interactions is fundamental for our understanding of the cellular machinery, and for improving medicine and bioengineering. Over the past years, a large body of experimental data has been accumulated on who interacts with whom and in what manner. However, these data are highly heterogeneous and sometimes contradictory, noisy, and biased. Ab initio methods provide a means to a "blind" protein-protein interaction network reconstruction. Here, we report on a molecular cross-docking-based approach for the identification of protein partners. The docking algorithm uses a coarse-grained representation of the protein structures and treats them as rigid bodies. We applied the approach to a few hundred of proteins, in the unbound conformations, and we systematically investigated the influence of several key ingredients, such as the size and quality of the interfaces, and the scoring function. We achieved some significant improvement compared to previous works, and a very high discriminative power on some specific functional classes. We provide a readout of the contributions of shape and physico-chemical complementarity, interface matching, and specificity, in the predictions. In addition, we assessed the ability of the approach to account for protein surface multiple usages, and we compared it with a sequence-based deep learning method. This work may contribute to guiding the exploitation of the large amounts of protein structural models now available toward the discovery of unexpected partners and their complex structure characterisation.
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6
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Roel-Touris J, Bonvin AM. Coarse-grained (hybrid) integrative modeling of biomolecular interactions. Comput Struct Biotechnol J 2020; 18:1182-1190. [PMID: 32514329 PMCID: PMC7264466 DOI: 10.1016/j.csbj.2020.05.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 04/23/2020] [Accepted: 05/06/2020] [Indexed: 12/23/2022] Open
Abstract
The computational modeling field has vastly evolved over the past decades. The early developments of simplified protein systems represented a stepping stone towards establishing more efficient approaches to sample intricated conformational landscapes. Downscaling the level of resolution of biomolecules to coarser representations allows for studying protein structure, dynamics and interactions that are not accessible by classical atomistic approaches. The combination of different resolutions, namely hybrid modeling, has also been proved as an alternative when mixed levels of details are required. In this review, we provide an overview of coarse-grained/hybrid models focusing on their applicability in the modeling of biomolecular interactions. We give a detailed list of ready-to-use modeling software for studying biomolecular interactions allowing various levels of coarse-graining and provide examples of complexes determined by integrative coarse-grained/hybrid approaches in combination with experimental information.
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7
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Schweke H, Mucchielli MH, Sacquin-Mora S, Bei W, Lopes A. Protein Interaction Energy Landscapes are Shaped by Functional and also Non-functional Partners. J Mol Biol 2020; 432:1183-1198. [DOI: 10.1016/j.jmb.2019.12.047] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 12/19/2019] [Accepted: 12/30/2019] [Indexed: 10/25/2022]
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8
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Reille S, Garnier M, Robert X, Gouet P, Martin J, Launay G. Identification and visualization of protein binding regions with the ArDock server. Nucleic Acids Res 2019; 46:W417-W422. [PMID: 29905873 PMCID: PMC6031020 DOI: 10.1093/nar/gky472] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 05/28/2018] [Indexed: 12/21/2022] Open
Abstract
ArDock (ardock.ibcp.fr) is a structural bioinformatics web server for the prediction and the visualization of potential interaction regions at protein surfaces. ArDock ranks the surface residues of a protein according to their tendency to form interfaces in a set of predefined docking experiments between the query protein and a set of arbitrary protein probes. The ArDock methodology is derived from large scale cross-docking studies where it was observed that randomly chosen proteins tend to dock in a non-random way at protein surfaces. The method predicts interaction site of the protein, or alternate interfaces in the case of proteins with multiple interaction modes. The server takes a protein structure as input and computes a score for each surface residue. Its output focuses on the interactive visualization of results and on interoperability with other services.
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Affiliation(s)
- Sébastien Reille
- Molecular Microbiology and Structural Biochemistry, Unité Mixte de Recherche, Université Claude Bernard Lyon 1, Centre National de la Recherche Scientifique, 69367 Lyon Cedex 07, France
| | - Mélanie Garnier
- Molecular Microbiology and Structural Biochemistry, Unité Mixte de Recherche, Université Claude Bernard Lyon 1, Centre National de la Recherche Scientifique, 69367 Lyon Cedex 07, France
| | - Xavier Robert
- Molecular Microbiology and Structural Biochemistry, Unité Mixte de Recherche, Université Claude Bernard Lyon 1, Centre National de la Recherche Scientifique, 69367 Lyon Cedex 07, France
| | - Patrice Gouet
- Molecular Microbiology and Structural Biochemistry, Unité Mixte de Recherche, Université Claude Bernard Lyon 1, Centre National de la Recherche Scientifique, 69367 Lyon Cedex 07, France
| | - Juliette Martin
- Molecular Microbiology and Structural Biochemistry, Unité Mixte de Recherche, Université Claude Bernard Lyon 1, Centre National de la Recherche Scientifique, 69367 Lyon Cedex 07, France
| | - Guillaume Launay
- Molecular Microbiology and Structural Biochemistry, Unité Mixte de Recherche, Université Claude Bernard Lyon 1, Centre National de la Recherche Scientifique, 69367 Lyon Cedex 07, France
- To whom correspondence should be addressed. Tel: +33 437 652 936; Fax: +33 472 722 601;
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9
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Dequeker C, Laine E, Carbone A. Decrypting protein surfaces by combining evolution, geometry, and molecular docking. Proteins 2019; 87:952-965. [PMID: 31199528 PMCID: PMC6852240 DOI: 10.1002/prot.25757] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Revised: 05/09/2019] [Accepted: 06/07/2019] [Indexed: 01/30/2023]
Abstract
The growing body of experimental and computational data describing how proteins interact with each other has emphasized the multiplicity of protein interactions and the complexity underlying protein surface usage and deformability. In this work, we propose new concepts and methods toward deciphering such complexity. We introduce the notion of interacting region to account for the multiple usage of a protein's surface residues by several partners and for the variability of protein interfaces coming from molecular flexibility. We predict interacting patches by crossing evolutionary, physicochemical and geometrical properties of the protein surface with information coming from complete cross-docking (CC-D) simulations. We show that our predictions match well interacting regions and that the different sources of information are complementary. We further propose an indicator of whether a protein has a few or many partners. Our prediction strategies are implemented in the dynJET2 algorithm and assessed on a new dataset of 262 protein on which we performed CC-D. The code and the data are available at: http://www.lcqb.upmc.fr/dynJET2/.
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Affiliation(s)
- Chloé Dequeker
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), Paris, France
| | - Elodie Laine
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), Paris, France
| | - Alessandra Carbone
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), Paris, France.,Institut Universitaire de France (IUF), Paris, France
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10
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Viswanathan R, Fajardo E, Steinberg G, Haller M, Fiser A. Protein-protein binding supersites. PLoS Comput Biol 2019; 15:e1006704. [PMID: 30615604 PMCID: PMC6336348 DOI: 10.1371/journal.pcbi.1006704] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Revised: 01/17/2019] [Accepted: 12/05/2018] [Indexed: 11/19/2022] Open
Abstract
The lack of a deep understanding of how proteins interact remains an important roadblock in advancing efforts to identify binding partners and uncover the corresponding regulatory mechanisms of the functions they mediate. Understanding protein-protein interactions is also essential for designing specific chemical modifications to develop new reagents and therapeutics. We explored the hypothesis of whether protein interaction sites serve as generic biding sites for non-cognate protein ligands, just as it has been observed for small-molecule-binding sites in the past. Using extensive computational docking experiments on a test set of 241 protein complexes, we found that indeed there is a strong preference for non-cognate ligands to bind to the cognate binding site of a receptor. This observation appears to be robust to variations in docking programs, types of non-cognate protein probes, sizes of binding patches, relative sizes of binding patches and full-length proteins, and the exploration of obligate and non-obligate complexes. The accuracy of the docking scoring function appears to play a role in defining the correct site. The frequency of interaction of unrelated probes recognizing the binding interface was utilized in a simple prediction algorithm that showed accuracy competitive with other state of the art methods.
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Affiliation(s)
- Raji Viswanathan
- Department of Chemistry, Yeshiva University, New York, NY, United States of America
| | - Eduardo Fajardo
- Departments of Systems & Computational Biology, and Biochemistry, Albert Einstein College of Medicine, Bronx, NY, United States of America
| | - Gabriel Steinberg
- Department of Chemistry, Yeshiva University, New York, NY, United States of America
| | - Matthew Haller
- Department of Chemistry, Yeshiva University, New York, NY, United States of America
| | - Andras Fiser
- Departments of Systems & Computational Biology, and Biochemistry, Albert Einstein College of Medicine, Bronx, NY, United States of America
- * E-mail:
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11
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Nadalin F, Carbone A. Protein-protein interaction specificity is captured by contact preferences and interface composition. Bioinformatics 2018; 34:459-468. [PMID: 29028884 PMCID: PMC5860360 DOI: 10.1093/bioinformatics/btx584] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Accepted: 09/18/2017] [Indexed: 12/24/2022] Open
Abstract
Motivation Large-scale computational docking will be increasingly used in future years to discriminate protein–protein interactions at the residue resolution. Complete cross-docking experiments make in silico reconstruction of protein–protein interaction networks a feasible goal. They ask for efficient and accurate screening of the millions structural conformations issued by the calculations. Results We propose CIPS (Combined Interface Propensity for decoy Scoring), a new pair potential combining interface composition with residue–residue contact preference. CIPS outperforms several other methods on screening docking solutions obtained either with all-atom or with coarse-grain rigid docking. Further testing on 28 CAPRI targets corroborates CIPS predictive power over existing methods. By combining CIPS with atomic potentials, discrimination of correct conformations in all-atom structures reaches optimal accuracy. The drastic reduction of candidate solutions produced by thousands of proteins docked against each other makes large-scale docking accessible to analysis. Availability and implementation CIPS source code is freely available at http://www.lcqb.upmc.fr/CIPS. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Francesca Nadalin
- Sorbonne Universités, UPMC-Univ P6, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative-UMR 7238, 75005 Paris, France
| | - Alessandra Carbone
- Sorbonne Universités, UPMC-Univ P6, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative-UMR 7238, 75005 Paris, France.,Institut Universitaire de France, 75005 Paris, France
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12
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Raucci R, Laine E, Carbone A. Local Interaction Signal Analysis Predicts Protein-Protein Binding Affinity. Structure 2018; 26:905-915.e4. [PMID: 29779789 DOI: 10.1016/j.str.2018.04.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Revised: 02/06/2018] [Accepted: 04/10/2018] [Indexed: 12/27/2022]
Abstract
Several models estimating the strength of the interaction between proteins in a complex have been proposed. By exploring the geometry of contact distribution at protein-protein interfaces, we provide an improved model of binding energy. Local interaction signal analysis (LISA) is a radial function based on terms describing favorable and non-favorable contacts obtained by density functional theory, the support-core-rim interface residue distribution, non-interacting charged residues and secondary structures contribution. The three-dimensional organization of the contacts and their contribution on localized hot-sites over the entire interaction surface were numerically evaluated. LISA achieves a correlation of 0.81 (and a root-mean-square error of 2.35 ± 0.38 kcal/mol) when tested on 125 complexes for which experimental measurements were realized. LISA's performance is stable for subsets defined by functional composition and extent of conformational changes upon complex formation. A large-scale comparison with 17 other functions demonstrated the power of the geometrical model in the understanding of complex binding.
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Affiliation(s)
- Raffaele Raucci
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), 4 Place Jussieu, 75005 Paris, France; Sorbonne Université, Institut des Sciences du Calcul et des Données (ISCD), 75005 Paris, France
| | - Elodie Laine
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), 4 Place Jussieu, 75005 Paris, France
| | - Alessandra Carbone
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), 4 Place Jussieu, 75005 Paris, France; Institut Universitaire de France, 75005 Paris, France.
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13
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Lagarde N, Carbone A, Sacquin-Mora S. Hidden partners: Using cross-docking calculations to predict binding sites for proteins with multiple interactions. Proteins 2018; 86:723-737. [DOI: 10.1002/prot.25506] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Revised: 03/23/2018] [Accepted: 04/07/2018] [Indexed: 02/06/2023]
Affiliation(s)
- Nathalie Lagarde
- Laboratoire de Biochimie Théorique, CNRS UPR9080, Institut de Biologie Physico-Chimique, University Paris Diderot, Sorbonne Paris Cité, 13 rue Pierre et Marie Curie; Paris 75005 France
| | - Alessandra Carbone
- Laboratoire de Biologie Computationnelle et Quantitative, CNRS UMR7238, UPMC Univ-Paris 6, Sorbonne Université, 4 place Jussieu; Paris 75005 France
- Institut Universitaire de France; Paris 75005 France
| | - Sophie Sacquin-Mora
- Laboratoire de Biochimie Théorique, CNRS UPR9080, Institut de Biologie Physico-Chimique, University Paris Diderot, Sorbonne Paris Cité, 13 rue Pierre et Marie Curie; Paris 75005 France
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14
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Abdollahi N, Albani A, Anthony E, Baud A, Cardon M, Clerc R, Czernecki D, Conte R, David L, Delaune A, Djerroud S, Fourgoux P, Guiglielmoni N, Laurentie J, Lehmann N, Lochard C, Montagne R, Myrodia V, Opuu V, Parey E, Polit L, Privé S, Quignot C, Ruiz-Cuevas M, Sissoko M, Sompairac N, Vallerix A, Verrecchia V, Delarue M, Guérois R, Ponty Y, Sacquin-Mora S, Carbone A, Froidevaux C, Le Crom S, Lespinet O, Weigt M, Abboud S, Bernardes J, Bouvier G, Dequeker C, Ferré A, Fuchs P, Lelandais G, Poulain P, Richard H, Schweke H, Laine E, Lopes A. Meet-U: Educating through research immersion. PLoS Comput Biol 2018; 14:e1005992. [PMID: 29543809 PMCID: PMC5854232 DOI: 10.1371/journal.pcbi.1005992] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
We present a new educational initiative called Meet-U that aims to train students for collaborative work in computational biology and to bridge the gap between education and research. Meet-U mimics the setup of collaborative research projects and takes advantage of the most popular tools for collaborative work and of cloud computing. Students are grouped in teams of 4–5 people and have to realize a project from A to Z that answers a challenging question in biology. Meet-U promotes "coopetition," as the students collaborate within and across the teams and are also in competition with each other to develop the best final product. Meet-U fosters interactions between different actors of education and research through the organization of a meeting day, open to everyone, where the students present their work to a jury of researchers and jury members give research seminars. This very unique combination of education and research is strongly motivating for the students and provides a formidable opportunity for a scientific community to unite and increase its visibility. We report on our experience with Meet-U in two French universities with master’s students in bioinformatics and modeling, with protein–protein docking as the subject of the course. Meet-U is easy to implement and can be straightforwardly transferred to other fields and/or universities. All the information and data are available at www.meet-u.org.
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Affiliation(s)
- Nika Abdollahi
- Departments of Computer Science and of Life Sciences, Sorbonne Université (SU) / UPMC, Paris, France
| | - Alexandre Albani
- Departments of Computer Science and of Life Sciences, Sorbonne Université (SU) / UPMC, Paris, France
| | - Eric Anthony
- Department of Biology and of Computer Science, Univ. Paris-Sud, Université Paris-Saclay (UPSay), Orsay, France
| | - Agnes Baud
- Departments of Computer Science and of Life Sciences, Sorbonne Université (SU) / UPMC, Paris, France
| | - Mélissa Cardon
- Departments of Computer Science and of Life Sciences, Sorbonne Université (SU) / UPMC, Paris, France
| | - Robert Clerc
- Departments of Computer Science and of Life Sciences, Sorbonne Université (SU) / UPMC, Paris, France
| | - Dariusz Czernecki
- Departments of Computer Science and of Life Sciences, Sorbonne Université (SU) / UPMC, Paris, France
| | - Romain Conte
- Departments of Computer Science and of Life Sciences, Sorbonne Université (SU) / UPMC, Paris, France
| | - Laurent David
- Departments of Computer Science and of Life Sciences, Sorbonne Université (SU) / UPMC, Paris, France
| | - Agathe Delaune
- Departments of Computer Science and of Life Sciences, Sorbonne Université (SU) / UPMC, Paris, France
| | - Samia Djerroud
- Departments of Computer Science and of Life Sciences, Sorbonne Université (SU) / UPMC, Paris, France
| | - Pauline Fourgoux
- Department of Biology and of Computer Science, Univ. Paris-Sud, Université Paris-Saclay (UPSay), Orsay, France
| | - Nadège Guiglielmoni
- Department of Biology and of Computer Science, Univ. Paris-Sud, Université Paris-Saclay (UPSay), Orsay, France
| | - Jeanne Laurentie
- Departments of Computer Science and of Life Sciences, Sorbonne Université (SU) / UPMC, Paris, France
| | - Nathalie Lehmann
- Departments of Computer Science and of Life Sciences, Sorbonne Université (SU) / UPMC, Paris, France
| | - Camille Lochard
- Department of Biology and of Computer Science, Univ. Paris-Sud, Université Paris-Saclay (UPSay), Orsay, France
| | - Rémi Montagne
- Department of Biology and of Computer Science, Univ. Paris-Sud, Université Paris-Saclay (UPSay), Orsay, France
| | - Vasiliki Myrodia
- Departments of Computer Science and of Life Sciences, Sorbonne Université (SU) / UPMC, Paris, France
| | - Vaitea Opuu
- Department of Biology and of Computer Science, Univ. Paris-Sud, Université Paris-Saclay (UPSay), Orsay, France
| | - Elise Parey
- Departments of Computer Science and of Life Sciences, Sorbonne Université (SU) / UPMC, Paris, France
| | - Lélia Polit
- Departments of Computer Science and of Life Sciences, Sorbonne Université (SU) / UPMC, Paris, France
| | - Sylvain Privé
- Department of Biology and of Computer Science, Univ. Paris-Sud, Université Paris-Saclay (UPSay), Orsay, France
| | - Chloé Quignot
- Departments of Computer Science and of Life Sciences, Sorbonne Université (SU) / UPMC, Paris, France
| | - Maria Ruiz-Cuevas
- Departments of Computer Science and of Life Sciences, Sorbonne Université (SU) / UPMC, Paris, France
| | - Mariam Sissoko
- Departments of Computer Science and of Life Sciences, Sorbonne Université (SU) / UPMC, Paris, France
| | - Nicolas Sompairac
- Departments of Computer Science and of Life Sciences, Sorbonne Université (SU) / UPMC, Paris, France
| | - Audrey Vallerix
- Department of Biology and of Computer Science, Univ. Paris-Sud, Université Paris-Saclay (UPSay), Orsay, France
| | - Violaine Verrecchia
- Department of Biology and of Computer Science, Univ. Paris-Sud, Université Paris-Saclay (UPSay), Orsay, France
| | - Marc Delarue
- Unit of Structural Dynamics of Macromolecules, CNRS, Institut Pasteur, Paris, France
| | - Raphael Guérois
- Institute for Integrative Biology of the Cell (I2BC), IBITECS, CEA, CNRS, Univ. Paris-Sud, UPSay, Gif-sur-Yvette cedex, France
| | - Yann Ponty
- AMIBio team, Laboratoire d’informatique de l’École polytechnique (LIX, UMR 7161) / Inria Saclay, UPSay, Palaiseau, France
| | - Sophie Sacquin-Mora
- Laboratoire de Biochimie Théorique, UPR 9080 CNRS Institut de Biologie Physico-Chimique, Paris, France
| | - Alessandra Carbone
- Sorbonne Université / UPMC, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), UMR 7238, Paris, France
- Institut Universitaire de France
| | | | - Stéphane Le Crom
- Sorbonne Université / UPMC, Univ. Antilles, Univ. Nice Sophia Antipolis, CNRS, Evolution Paris Seine - Institut de Biologie Paris Seine (EPS - IBPS), Paris, France
| | - Olivier Lespinet
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, UPSay, Gif-sur-Yvette cedex, France
| | - Martin Weigt
- Sorbonne Université / UPMC, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), UMR 7238, Paris, France
| | - Samer Abboud
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, UPSay, Gif-sur-Yvette cedex, France
| | - Juliana Bernardes
- Sorbonne Université / UPMC, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), UMR 7238, Paris, France
| | - Guillaume Bouvier
- Department of Structural Biology and CheImistry (CNRS UMR3528) - Center of Bioinformatics, Biostatistics and Integrative Biology (CNRS USR3756) - Structural Bioinformatics Unit, Institut Pasteur, Paris, France
| | - Chloé Dequeker
- Sorbonne Université / UPMC, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), UMR 7238, Paris, France
| | - Arnaud Ferré
- MaIAGE, INRA, UPSay, Jouy-en-Josas, France and LIMSI, CNRS, UPSay, Orsay, France
| | - Patrick Fuchs
- Sorbonne Université / UPMC, Ecole Normale Supérieure - PLS Research University, Département de Chimie, CNRS, Laboratoire des Biomolécules, UMR 7203 - Univ. Paris Diderot, Sorbonne Paris Cité, Paris, France
| | - Gaëlle Lelandais
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, UPSay, Gif-sur-Yvette cedex, France
| | - Pierre Poulain
- Mitochondria, Metals and Oxidative Stress Group, Institut Jacques Monod, UMR 7592, Univ. Paris Diderot, CNRS, Sorbonne Paris Cité, Paris, France
| | - Hugues Richard
- Sorbonne Université / UPMC, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), UMR 7238, Paris, France
| | - Hugo Schweke
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, UPSay, Gif-sur-Yvette cedex, France
| | - Elodie Laine
- Sorbonne Université / UPMC, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), UMR 7238, Paris, France
- * E-mail: (EL); (AL)
| | - Anne Lopes
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, UPSay, Gif-sur-Yvette cedex, France
- * E-mail: (EL); (AL)
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15
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Škrbić T, Zamuner S, Hong R, Seno F, Laio A, Trovato A. Vibrational entropy estimation can improve binding affinity prediction for non-obligatory protein complexes. Proteins 2018; 86:393-404. [DOI: 10.1002/prot.25454] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Revised: 12/22/2017] [Accepted: 01/05/2018] [Indexed: 01/10/2023]
Affiliation(s)
- Tatjana Škrbić
- Faculty of Physics; International School for Advanced Studies (SISSA/ISAS); Trieste Italy
- Department of Physics and Astronomy “Galileo Galilei”; University of Padova; Padova Italy
| | - Stefano Zamuner
- Department of Physics and Astronomy “Galileo Galilei”; University of Padova; Padova Italy
| | - Rolando Hong
- Faculty of Physics; International School for Advanced Studies (SISSA/ISAS); Trieste Italy
| | - Flavio Seno
- Department of Physics and Astronomy “Galileo Galilei”; University of Padova; Padova Italy
- Padova Section, National Institute of Nuclear Physics (INFN); Padova Italy
| | - Alessandro Laio
- Faculty of Physics; International School for Advanced Studies (SISSA/ISAS); Trieste Italy
| | - Antonio Trovato
- Department of Physics and Astronomy “Galileo Galilei”; University of Padova; Padova Italy
- Padova Section, National Institute of Nuclear Physics (INFN); Padova Italy
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16
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Dequeker C, Laine E, Carbone A. INTerface Builder: A Fast Protein-Protein Interface Reconstruction Tool. J Chem Inf Model 2017; 57:2613-2617. [PMID: 28991473 DOI: 10.1021/acs.jcim.7b00360] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
INTerface Builder (INTBuilder) is a fast, easy-to-use program to compute protein-protein interfaces. It is designed to retrieve interfaces from molecular docking software outputs in an empirically determined linear complexity. INTBuilder directly reads the output formats of popular docking programs like ATTRACT, HEX, MAXDo, and ZDOCK, as well as a more generic format and Protein Data Bank (PDB) files. It identifies interacting surfaces at both residue and atom resolutions. INTerface Builder is an open source software written in C and freely available for noncommercial use (CeCILL license) at https://www.lcqb.upmc.fr/INTBuilder .
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Affiliation(s)
- Chloé Dequeker
- UPMC-Univ P6, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative-UMR 7238, Sorbonne Universités , 4 place Jussieu, 75005 Paris, France
| | - Elodie Laine
- UPMC-Univ P6, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative-UMR 7238, Sorbonne Universités , 4 place Jussieu, 75005 Paris, France
| | - Alessandra Carbone
- UPMC-Univ P6, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative-UMR 7238, Sorbonne Universités , 4 place Jussieu, 75005 Paris, France.,Institut Universitaire de France , Paris 75005, France
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17
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Launay G, Ceres N, Martin J. Non-interacting proteins may resemble interacting proteins: prevalence and implications. Sci Rep 2017; 7:40419. [PMID: 28084410 PMCID: PMC5289270 DOI: 10.1038/srep40419] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2016] [Accepted: 12/07/2016] [Indexed: 12/13/2022] Open
Abstract
The vast majority of proteins do not form functional interactions in physiological conditions. We have considered several sets of protein pairs from S. cerevisiae with no functional interaction reported, denoted as non-interacting pairs, and compared their 3D structures to available experimental complexes. We identified some non-interacting pairs with significant structural similarity with experimental complexes, indicating that, even though they do not form functional interactions, they have compatible structures. We estimate that up to 8.7% of non-interacting protein pairs could have compatible structures. This number of interactions exceeds the number of functional interactions (around 0.2% of the total interactions) by a factor 40. Network analysis suggests that the interactions formed by non-interacting pairs with compatible structures could be particularly hazardous to the protein-protein interaction network. From a structural point of view, these interactions display no aberrant structural characteristics, and are even predicted as relatively stable and enriched in potential physical interactors, suggesting a major role of regulation to prevent them.
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Affiliation(s)
- Guillaume Launay
- Univ Lyon, CNRS, UMR 5086 MMSB, 7 passage du Vercors F-69367, Lyon, France
| | - Nicoletta Ceres
- Univ Lyon, CNRS, UMR 5086 MMSB, 7 passage du Vercors F-69367, Lyon, France
| | - Juliette Martin
- Univ Lyon, CNRS, UMR 5086 MMSB, 7 passage du Vercors F-69367, Lyon, France
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18
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Laine E, Carbone A. Protein social behavior makes a stronger signal for partner identification than surface geometry. Proteins 2016; 85:137-154. [PMID: 27802579 PMCID: PMC5242317 DOI: 10.1002/prot.25206] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2016] [Revised: 10/10/2016] [Accepted: 10/20/2016] [Indexed: 01/26/2023]
Abstract
Cells are interactive living systems where proteins movements, interactions and regulation are substantially free from centralized management. How protein physico‐chemical and geometrical properties determine who interact with whom remains far from fully understood. We show that characterizing how a protein behaves with many potential interactors in a complete cross‐docking study leads to a sharp identification of its cellular/true/native partner(s). We define a sociability index, or S‐index, reflecting whether a protein likes or not to pair with other proteins. Formally, we propose a suitable normalization function that accounts for protein sociability and we combine it with a simple interface‐based (ranking) score to discriminate partners from non‐interactors. We show that sociability is an important factor and that the normalization permits to reach a much higher discriminative power than shape complementarity docking scores. The social effect is also observed with more sophisticated docking algorithms. Docking conformations are evaluated using experimental binding sites. These latter approximate in the best possible way binding sites predictions, which have reached high accuracy in recent years. This makes our analysis helpful for a global understanding of partner identification and for suggesting discriminating strategies. These results contradict previous findings claiming the partner identification problem being solvable solely with geometrical docking. Proteins 2016; 85:137–154. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Elodie Laine
- Sorbonne Universités, UPMC-Univ P6, CNRS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, Paris, 75005, France
| | - Alessandra Carbone
- Sorbonne Universités, UPMC-Univ P6, CNRS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, Paris, 75005, France.,Institut Universitaire de France, Paris, 75005, France
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19
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Vamparys L, Laurent B, Carbone A, Sacquin-Mora S. Great interactions: How binding incorrect partners can teach us about protein recognition and function. Proteins 2016; 84:1408-21. [PMID: 27287388 PMCID: PMC5516155 DOI: 10.1002/prot.25086] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2016] [Revised: 06/01/2016] [Accepted: 06/02/2016] [Indexed: 12/29/2022]
Abstract
Protein–protein interactions play a key part in most biological processes and understanding their mechanism is a fundamental problem leading to numerous practical applications. The prediction of protein binding sites in particular is of paramount importance since proteins now represent a major class of therapeutic targets. Amongst others methods, docking simulations between two proteins known to interact can be a useful tool for the prediction of likely binding patches on a protein surface. From the analysis of the protein interfaces generated by a massive cross‐docking experiment using the 168 proteins of the Docking Benchmark 2.0, where all possible protein pairs, and not only experimental ones, have been docked together, we show that it is also possible to predict a protein's binding residues without having any prior knowledge regarding its potential interaction partners. Evaluating the performance of cross‐docking predictions using the area under the specificity‐sensitivity ROC curve (AUC) leads to an AUC value of 0.77 for the complete benchmark (compared to the 0.5 AUC value obtained for random predictions). Furthermore, a new clustering analysis performed on the binding patches that are scattered on the protein surface show that their distribution and growth will depend on the protein's functional group. Finally, in several cases, the binding‐site predictions resulting from the cross‐docking simulations will lead to the identification of an alternate interface, which corresponds to the interaction with a biomolecular partner that is not included in the original benchmark. Proteins 2016; 84:1408–1421. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.
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Affiliation(s)
- Lydie Vamparys
- Laboratoire De Biochimie Théorique, CNRS UPR 9080, Institut De Biologie Physico-Chimique, 13 Rue Pierre Et Marie Curie, Paris, 75005, France
| | - Benoist Laurent
- Laboratoire De Biochimie Théorique, CNRS UPR 9080, Institut De Biologie Physico-Chimique, 13 Rue Pierre Et Marie Curie, Paris, 75005, France
| | - Alessandra Carbone
- Sorbonne Universités, UPMC Univ-Paris 6, CNRS UMR7238, Laboratoire De Biologie Computationnelle Et Quantitative, 15 Rue De L'Ecole De Médecine, Paris, 75006, France.,Institut Universitaire De France, Paris, 75005, France
| | - Sophie Sacquin-Mora
- Laboratoire De Biochimie Théorique, CNRS UPR 9080, Institut De Biologie Physico-Chimique, 13 Rue Pierre Et Marie Curie, Paris, 75005, France.
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20
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Rigid-Docking Approaches to Explore Protein-Protein Interaction Space. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2016; 160:33-55. [PMID: 27830312 DOI: 10.1007/10_2016_41] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Protein-protein interactions play core roles in living cells, especially in the regulatory systems. As information on proteins has rapidly accumulated on publicly available databases, much effort has been made to obtain a better picture of protein-protein interaction networks using protein tertiary structure data. Predicting relevant interacting partners from their tertiary structure is a challenging task and computer science methods have the potential to assist with this. Protein-protein rigid docking has been utilized by several projects, docking-based approaches having the advantages that they can suggest binding poses of predicted binding partners which would help in understanding the interaction mechanisms and that comparing docking results of both non-binders and binders can lead to understanding the specificity of protein-protein interactions from structural viewpoints. In this review we focus on explaining current computational prediction methods to predict pairwise direct protein-protein interactions that form protein complexes.
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21
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Emperador A, Sfriso P, Villarreal MA, Gelpí JL, Orozco M. PACSAB: Coarse-Grained Force Field for the Study of Protein–Protein Interactions and Conformational Sampling in Multiprotein Systems. J Chem Theory Comput 2015; 11:5929-38. [DOI: 10.1021/acs.jctc.5b00660] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Agustí Emperador
- Institute for Research in Biomedicine (IRB Barcelona), Baldiri
i Reixac 10, Barcelona 08028, Spain
- Joint BSC-IRB Research Program in Computational Biology, IRB Barcelona, Barcelona 08028, Spain
| | - Pedro Sfriso
- Institute for Research in Biomedicine (IRB Barcelona), Baldiri
i Reixac 10, Barcelona 08028, Spain
- Joint BSC-IRB Research Program in Computational Biology, IRB Barcelona, Barcelona 08028, Spain
| | - Marcos Ariel Villarreal
- Instituto de Investigaciones en Fisicoquímica de Córdoba
- Departamento de Matemática y Física, CONICET-Universidad Nacional de Córdoba, University City, Córdoba 5000, Argentina
| | - Josep Lluis Gelpí
- Institute for Research in Biomedicine (IRB Barcelona), Baldiri
i Reixac 10, Barcelona 08028, Spain
- Joint BSC-IRB Research Program in Computational Biology, IRB Barcelona, Barcelona 08028, Spain
- Barcelona Supercomputing Center, Jordi Girona
29, Barcelona 08034, Spain
- Departament de Bioquímica, Facultat de Biologia, Avgda Diagonal 645, Barcelona 08028, Spain
| | - Modesto Orozco
- Institute for Research in Biomedicine (IRB Barcelona), Baldiri
i Reixac 10, Barcelona 08028, Spain
- Joint BSC-IRB Research Program in Computational Biology, IRB Barcelona, Barcelona 08028, Spain
- Barcelona Supercomputing Center, Jordi Girona
29, Barcelona 08034, Spain
- Departament de Bioquímica, Facultat de Biologia, Avgda Diagonal 645, Barcelona 08028, Spain
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22
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Vangone A, Bonvin AM. Contacts-based prediction of binding affinity in protein-protein complexes. eLife 2015. [PMID: 26193119 DOI: 10.7554/elife07454] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2023] Open
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.
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Affiliation(s)
- Anna Vangone
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry, Utrecht University, Utrecht, Netherlands
| | - Alexandre Mjj Bonvin
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry, Utrecht University, Utrecht, Netherlands
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23
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Vangone A, Bonvin AMJJ. Contacts-based prediction of binding affinity in protein-protein complexes. eLife 2015; 4:e07454. [PMID: 26193119 PMCID: PMC4523921 DOI: 10.7554/elife.07454] [Citation(s) in RCA: 313] [Impact Index Per Article: 34.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2015] [Accepted: 07/08/2015] [Indexed: 12/13/2022] Open
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.
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Affiliation(s)
- Anna Vangone
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science—Chemistry, Utrecht University, Utrecht, Netherlands
| | - Alexandre MJJ Bonvin
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science—Chemistry, Utrecht University, Utrecht, Netherlands
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24
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Schindler CEM, de Vries SJ, Zacharias M. Fully Blind Peptide-Protein Docking with pepATTRACT. Structure 2015; 23:1507-1515. [PMID: 26146186 DOI: 10.1016/j.str.2015.05.021] [Citation(s) in RCA: 87] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2015] [Revised: 05/21/2015] [Accepted: 05/25/2015] [Indexed: 02/02/2023]
Abstract
Peptide-protein interactions are ubiquitous in the cell and form an important part of the interactome. Computational docking methods can complement experimental characterization of these complexes, but current protocols are not applicable on the proteome scale. Here, we present a new fully blind flexible peptide-protein docking protocol, pepATTRACT, which combines a rapid coarse-grained global peptide docking search of the entire protein surface with a two-stage atomistic flexible refinement. Global unbound-unbound docking yielded near-native models for 70% of the docking cases when testing the protocol on the largest benchmark of peptide-protein complexes available to date. This performance is similar to that of state-of-the-art local docking protocols that rely on information about the binding site. Upon restricting the search to the peptide binding region, the resulting pepATTRACT-local approach outperformed existing methods. Docking scripts for pepATTRACT and pepATTRACT-local can be generated via a web interface at www.attract.ph.tum.de/peptide.html.
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Affiliation(s)
- Christina E M Schindler
- Physics Department T38, Technische Universität München, James-Franck-Straße 1, 85748 Garching, Germany
| | - Sjoerd J de Vries
- Physics Department T38, Technische Universität München, James-Franck-Straße 1, 85748 Garching, Germany
| | - Martin Zacharias
- Physics Department T38, Technische Universität München, James-Franck-Straße 1, 85748 Garching, Germany.
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25
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Srihari S, Yong CH, Patil A, Wong L. Methods for protein complex prediction and their contributions towards understanding the organisation, function and dynamics of complexes. FEBS Lett 2015; 589:2590-602. [PMID: 25913176 DOI: 10.1016/j.febslet.2015.04.026] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2015] [Revised: 04/14/2015] [Accepted: 04/14/2015] [Indexed: 12/30/2022]
Abstract
Complexes of physically interacting proteins constitute fundamental functional units responsible for driving biological processes within cells. A faithful reconstruction of the entire set of complexes is therefore essential to understand the functional organisation of cells. In this review, we discuss the key contributions of computational methods developed till date (approximately between 2003 and 2015) for identifying complexes from the network of interacting proteins (PPI network). We evaluate in depth the performance of these methods on PPI datasets from yeast, and highlight their limitations and challenges, in particular at detecting sparse and small or sub-complexes and discerning overlapping complexes. We describe methods for integrating diverse information including expression profiles and 3D structures of proteins with PPI networks to understand the dynamics of complex formation, for instance, of time-based assembly of complex subunits and formation of fuzzy complexes from intrinsically disordered proteins. Finally, we discuss methods for identifying dysfunctional complexes in human diseases, an application that is proving invaluable to understand disease mechanisms and to discover novel therapeutic targets. We hope this review aptly commemorates a decade of research on computational prediction of complexes and constitutes a valuable reference for further advancements in this exciting area.
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Affiliation(s)
- Sriganesh Srihari
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Queensland 4067, Australia.
| | - Chern Han Yong
- Department of Computer Science, National University of Singapore, Singapore 117417, Singapore
| | - Ashwini Patil
- Human Genome Centre, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan
| | - Limsoon Wong
- Department of Computer Science, National University of Singapore, Singapore 117417, Singapore
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26
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Andreani J, Guerois R. Evolution of protein interactions: From interactomes to interfaces. Arch Biochem Biophys 2014; 554:65-75. [DOI: 10.1016/j.abb.2014.05.010] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2014] [Revised: 04/28/2014] [Accepted: 05/12/2014] [Indexed: 12/16/2022]
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27
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Proteins Feel More Than They See: Fine-Tuning of Binding Affinity by Properties of the Non-Interacting Surface. J Mol Biol 2014; 426:2632-52. [DOI: 10.1016/j.jmb.2014.04.017] [Citation(s) in RCA: 79] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2013] [Revised: 03/11/2014] [Accepted: 04/17/2014] [Indexed: 11/21/2022]
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28
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Levieux G, Tiger G, Mader S, Zagury JF, Natkin S, Montes M. Udock, the interactive docking entertainment system. Faraday Discuss 2014; 169:425-41. [PMID: 25341068 DOI: 10.1039/c3fd00147d] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Protein-protein interactions play a crucial role in biological processes. Protein docking calculations' goal is to predict, given two proteins of known structures, the associate conformation of the corresponding complex. Here, we present a new interactive protein docking system, Udock, that makes use of users' cognitive capabilities added up. In Udock, the users tackle simplified representations of protein structures and explore protein-protein interfaces' conformational space using a gamified interactive docking system with on the fly scoring. We assumed that if given appropriate tools, a naïve user's cognitive capabilities could provide relevant data for (1) the prediction of correct interfaces in binary protein complexes and (2) the identification of the experimental partner in interaction among a set of decoys. To explore this approach experimentally, we conducted a preliminary two week long playtest where the registered users could perform a cross-docking on a dataset comprising 4 binary protein complexes. The users explored almost all the surface of the proteins that were available in the dataset but favored certain regions that seemed more attractive as potential docking spots. These favored regions were located inside or nearby the experimental binding interface for 5 out of the 8 proteins in the dataset. For most of them, the best scores were obtained with the experimental partner. The alpha version of Udock is freely accessible at http://udock.fr.
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Affiliation(s)
- Guillaume Levieux
- Equipe Interactivité pour Lire et Jouer, Laboratoire CEDRIC, EA4626, Conservatoire National des Arts et Métiers, 292 Rue Saint Martin, 75003 Paris.
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29
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Lopes A, Sacquin-Mora S, Dimitrova V, Laine E, Ponty Y, Carbone A. Protein-protein interactions in a crowded environment: an analysis via cross-docking simulations and evolutionary information. PLoS Comput Biol 2013; 9:e1003369. [PMID: 24339765 PMCID: PMC3854762 DOI: 10.1371/journal.pcbi.1003369] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2013] [Accepted: 10/15/2013] [Indexed: 12/27/2022] Open
Abstract
Large-scale analyses of protein-protein interactions based on coarse-grain molecular docking simulations and binding site predictions resulting from evolutionary sequence analysis, are possible and realizable on hundreds of proteins with variate structures and interfaces. We demonstrated this on the 168 proteins of the Mintseris Benchmark 2.0. On the one hand, we evaluated the quality of the interaction signal and the contribution of docking information compared to evolutionary information showing that the combination of the two improves partner identification. On the other hand, since protein interactions usually occur in crowded environments with several competing partners, we realized a thorough analysis of the interactions of proteins with true partners but also with non-partners to evaluate whether proteins in the environment, competing with the true partner, affect its identification. We found three populations of proteins: strongly competing, never competing, and interacting with different levels of strength. Populations and levels of strength are numerically characterized and provide a signature for the behavior of a protein in the crowded environment. We showed that partner identification, to some extent, does not depend on the competing partners present in the environment, that certain biochemical classes of proteins are intrinsically easier to analyze than others, and that small proteins are not more promiscuous than large ones. Our approach brings to light that the knowledge of the binding site can be used to reduce the high computational cost of docking simulations with no consequence in the quality of the results, demonstrating the possibility to apply coarse-grain docking to datasets made of thousands of proteins. Comparison with all available large-scale analyses aimed to partner predictions is realized. We release the complete decoys set issued by coarse-grain docking simulations of both true and false interacting partners, and their evolutionary sequence analysis leading to binding site predictions. Download site: http://www.lgm.upmc.fr/CCDMintseris/ Protein-protein interactions (PPI) are at the heart of the molecular processes governing life and constitute an increasingly important target for drug design. Given their importance, it is vital to determine which protein interactions have functional relevance and to characterize the protein competition inherent to crowded environments, as the cytoplasm or the cellular organelles. We show that combining coarse-grain molecular cross-docking simulations and binding site predictions based on evolutionary sequence analysis is a viable route to identify true interacting partners for hundreds of proteins with a variate set of protein structures and interfaces. Also, we realize a large-scale analysis of protein binding promiscuity and provide a numerical characterization of partner competition and level of interaction strength for about 28000 false-partner interactions. Finally, we demonstrate that binding site prediction is useful to discriminate native partners, but also to scale up the approach to thousands of protein interactions. This study is based on the large computational effort made by thousands of internautes helping World Community Grid over a period of 7 months. The complete dataset issued by the computation and the analysis is released to the scientific community.
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Affiliation(s)
- Anne Lopes
- Université Pierre et Marie Curie, UMR 7238, Equipe de Génomique Analytique, Paris, France
- CNRS, UMR 7238, Laboratoire de Génomique des Microorganismes, Paris, France
| | - Sophie Sacquin-Mora
- Laboratoire de Biochimie Théorique, CNRS UPR 9080, Institut de Biologie Physico-Chimique, Paris, France
| | - Viktoriya Dimitrova
- Université Pierre et Marie Curie, UMR 7238, Equipe de Génomique Analytique, Paris, France
- CNRS, UMR 7238, Laboratoire de Génomique des Microorganismes, Paris, France
| | - Elodie Laine
- Université Pierre et Marie Curie, UMR 7238, Equipe de Génomique Analytique, Paris, France
- CNRS, UMR 7238, Laboratoire de Génomique des Microorganismes, Paris, France
| | - Yann Ponty
- Université Pierre et Marie Curie, UMR 7238, Equipe de Génomique Analytique, Paris, France
- LIX, CNRS UMR 7161 - INRIA AMIB, École polytechnique, Palaiseau, France
| | - Alessandra Carbone
- Université Pierre et Marie Curie, UMR 7238, Equipe de Génomique Analytique, Paris, France
- CNRS, UMR 7238, Laboratoire de Génomique des Microorganismes, Paris, France
- * E-mail:
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Kastritis PL, Bonvin AMJJ. On the binding affinity of macromolecular interactions: daring to ask why proteins interact. J R Soc Interface 2012; 10:20120835. [PMID: 23235262 PMCID: PMC3565702 DOI: 10.1098/rsif.2012.0835] [Citation(s) in RCA: 276] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Interactions between proteins are orchestrated in a precise and time-dependent manner, underlying cellular function. The binding affinity, defined as the strength of these interactions, is translated into physico-chemical terms in the dissociation constant (Kd), the latter being an experimental measure that determines whether an interaction will be formed in solution or not. Predicting binding affinity from structural models has been a matter of active research for more than 40 years because of its fundamental role in drug development. However, all available approaches are incapable of predicting the binding affinity of protein–protein complexes from coordinates alone. Here, we examine both theoretical and experimental limitations that complicate the derivation of structure–affinity relationships. Most work so far has concentrated on binary interactions. Systems of increased complexity are far from being understood. The main physico-chemical measure that relates to binding affinity is the buried surface area, but it does not hold for flexible complexes. For the latter, there must be a significant entropic contribution that will have to be approximated in the future. We foresee that any theoretical modelling of these interactions will have to follow an integrative approach considering the biology, chemistry and physics that underlie protein–protein recognition.
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Affiliation(s)
- Panagiotis L Kastritis
- Bijvoet Center for Biomolecular Research, Faculty of Science, Chemistry, Utrecht University, , Padualaan 8, Utrecht, The Netherlands
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Zacharias M. Combining coarse-grained nonbonded and atomistic bonded interactions for protein modeling. Proteins 2012; 81:81-92. [PMID: 22911567 DOI: 10.1002/prot.24164] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2012] [Revised: 08/09/2012] [Accepted: 08/15/2012] [Indexed: 12/12/2022]
Abstract
A hybrid coarse-grained (CG) and atomistic (AT) model for protein simulations and rapid searching and refinement of peptide-protein complexes has been developed. In contrast to other hybrid models that typically represent spatially separate parts of a protein by either a CG or an AT force field model, the present approach simultaneously represents the protein by an AT (united atom) and a CG model. The interactions of the protein main chain are described based on the united atom force field allowing a realistic representation of protein secondary structures. In addition, the AT description of all other bonded interactions keeps the protein compatible with a realistic bonded geometry. Nonbonded interactions between side chains and side chains and main chain are calculated at the level of a CG model using a knowledge-based potential. Unrestrained molecular dynamics simulations on several test proteins resulted in trajectories in reasonable agreement with the corresponding experimental structures. Applications to the refinement of docked peptide-protein complexes resulted in improved complex structures. Application to the rapid refinement of docked protein-protein complex is also possible but requires further optimization of force field parameters.
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Affiliation(s)
- Martin Zacharias
- Physik-Department T38, Technische Universität München, James Franck Str. 1, 85748 Garching, Germany.
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Chen CT, Peng HP, Jian JW, Tsai KC, Chang JY, Yang EW, Chen JB, Ho SY, Hsu WL, Yang AS. Protein-protein interaction site predictions with three-dimensional probability distributions of interacting atoms on protein surfaces. PLoS One 2012; 7:e37706. [PMID: 22701576 PMCID: PMC3368894 DOI: 10.1371/journal.pone.0037706] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2011] [Accepted: 04/23/2012] [Indexed: 11/18/2022] Open
Abstract
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 the physicochemical complementarity features based on the non-covalent interaction data derived from protein interiors.
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Affiliation(s)
- Ching-Tai Chen
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
- Institute of Bioinformatics and Systems Biology, National Chiao-Tung University, Hsinchu, Taiwan
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
- Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Hung-Pin Peng
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
- Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan
- Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Jhih-Wei Jian
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
- Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan
- Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | | | - Jeng-Yih Chang
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
| | - Ei-Wen Yang
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Jun-Bo Chen
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
- Department of Computer Science, National Tsing-Hua University, Hsinchu, Taiwan
| | - Shinn-Ying Ho
- Institute of Bioinformatics and Systems Biology, National Chiao-Tung University, Hsinchu, Taiwan
| | - Wen-Lian Hsu
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
- * E-mail: (AY); (WH)
| | - An-Suei Yang
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
- * E-mail: (AY); (WH)
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Arbitrary protein-protein docking targets biologically relevant interfaces. BMC BIOPHYSICS 2012; 5:7. [PMID: 22559010 PMCID: PMC3441232 DOI: 10.1186/2046-1682-5-7] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2012] [Accepted: 04/11/2012] [Indexed: 11/10/2022]
Abstract
BACKGROUND Protein-protein recognition is of fundamental importance in the vast majority of biological processes. However, it has already been demonstrated that it is very hard to distinguish true complexes from false complexes in so-called cross-docking experiments, where binary protein complexes are separated and the isolated proteins are all docked against each other and scored. Does this result, at least in part, reflect a physical reality? False complexes could reflect possible nonspecific or weak associations. RESULTS In this paper, we investigate the twilight zone of protein-protein interactions, building on an interesting outcome of cross-docking experiments: false complexes seem to favor residues from the true interaction site, suggesting that randomly chosen partners dock in a non-random fashion on protein surfaces. Here, we carry out arbitrary docking of a non-redundant data set of 198 proteins, with more than 300 randomly chosen "probe" proteins. We investigate the tendency of arbitrary partners to aggregate at localized regions of the protein surfaces, the shape and compositional bias of the generated interfaces, and the potential of this property to predict biologically relevant binding sites. We show that the non-random localization of arbitrary partners after protein-protein docking is a generic feature of protein structures. The interfaces generated in this way are not systematically planar or curved, but tend to be closer than average to the center of the proteins. These results can be used to predict biological interfaces with an AUC value up to 0.69 alone, and 0.72 when used in combination with evolutionary information. An appropriate choice of random partners and number of docking models make this method computationally practical. It is also noted that nonspecific interfaces can point to alternate interaction sites in the case of proteins with multiple interfaces. We illustrate the usefulness of arbitrary docking using PEBP (Phosphatidylethanolamine binding protein), a kinase inhibitor with multiple partners. CONCLUSIONS An approach using arbitrary docking, and based solely on physical properties, can successfully identify biologically pertinent protein interfaces.
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Ceres N, Lavery R. Coarse-grain Protein Models. INNOVATIONS IN BIOMOLECULAR MODELING AND SIMULATIONS 2012. [DOI: 10.1039/9781849735049-00219] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Coarse-graining is a powerful approach for modeling biomolecules that, over the last few decades, has been extensively applied to proteins. Coarse-grain models offer access to large systems and to slow processes without becoming computationally unmanageable. In addition, they are very versatile, enabling both the protein representation and the energy function to be adapted to the biological problem in hand. This review concentrates on modeling soluble proteins and their assemblies. It presents an overview of the coarse-grain representations, of the associated interaction potentials, and of the optimization procedures used to define them. It then shows how coarse-grain models have been used to understand processes involving proteins, from their initial folding to their functional properties, their binary interactions, and the assembly of large complexes.
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Affiliation(s)
- N. Ceres
- Bases Moléculaires et Structurales des Systèmes Infectieux Université Lyon1/CNRS UMR 5086, IBCP, 7 Passage du Vercors, 69367, Lyon France
| | - R. Lavery
- Bases Moléculaires et Structurales des Systèmes Infectieux Université Lyon1/CNRS UMR 5086, IBCP, 7 Passage du Vercors, 69367, Lyon France
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Tuffery P, Derreumaux P. Flexibility and binding affinity in protein-ligand, protein-protein and multi-component protein interactions: limitations of current computational approaches. J R Soc Interface 2011; 9:20-33. [PMID: 21993006 DOI: 10.1098/rsif.2011.0584] [Citation(s) in RCA: 69] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The recognition process between a protein and a partner represents a significant theoretical challenge. In silico structure-based drug design carried out with nothing more than the three-dimensional structure of the protein has led to the introduction of many compounds into clinical trials and numerous drug approvals. Central to guiding the discovery process is to recognize active among non-active compounds. While large-scale computer simulations of compounds taken from a library (virtual screening) or designed de novo are highly desirable in the post-genomic area, many technical problems remain to be adequately addressed. This article presents an overview and discusses the limits of current computational methods for predicting the correct binding pose and accurate binding affinity. It also presents the performances of the most popular algorithms for exploring binary and multi-body protein interactions.
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Affiliation(s)
- Pierre Tuffery
- INSERM UMR-S 973, Université Paris Diderot, 35 rue Hélène Brion, 75251 Paris cedex, France
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Wass MN, David A, Sternberg MJE. Challenges for the prediction of macromolecular interactions. Curr Opin Struct Biol 2011; 21:382-90. [DOI: 10.1016/j.sbi.2011.03.013] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2010] [Revised: 03/04/2011] [Accepted: 03/24/2011] [Indexed: 12/14/2022]
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Yoshikawa T, Tsukamoto K, Hourai Y, Fukui K. Improving the Accuracy of an Affinity Prediction Method by Using Statistics on Shape Complementarity between Proteins. J Chem Inf Model 2009; 49:693-703. [DOI: 10.1021/ci800310f] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Tatsuya Yoshikawa
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan, and Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan
| | - Koki Tsukamoto
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan, and Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan
| | - Yuichiro Hourai
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan, and Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan
| | - Kazuhiko Fukui
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan, and Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan
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