1
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Gaudreault F, Corbeil CR, Sulea T. Enhanced antibody-antigen structure prediction from molecular docking using AlphaFold2. Sci Rep 2023; 13:15107. [PMID: 37704686 PMCID: PMC10499836 DOI: 10.1038/s41598-023-42090-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 09/05/2023] [Indexed: 09/15/2023] Open
Abstract
Predicting the structure of antibody-antigen complexes has tremendous value in biomedical research but unfortunately suffers from a poor performance in real-life applications. AlphaFold2 (AF2) has provided renewed hope for improvements in the field of protein-protein docking but has shown limited success against antibody-antigen complexes due to the lack of co-evolutionary constraints. In this study, we used physics-based protein docking methods for building decoy sets consisting of low-energy docking solutions that were either geometrically close to the native structure (positives) or not (negatives). The docking models were then fed into AF2 to assess their confidence with a novel composite score based on normalized pLDDT and pTMscore metrics after AF2 structural refinement. We show benefits of the AF2 composite score for rescoring docking poses both in terms of (1) classification of positives/negatives and of (2) success rates with particular emphasis on early enrichment. Docking models of at least medium quality present in the decoy set, but not necessarily highly ranked by docking methods, benefitted most from AF2 rescoring by experiencing large advances towards the top of the reranked list of models. These improvements, obtained without any calibration or novel methodologies, led to a notable level of performance in antibody-antigen unbound docking that was never achieved previously.
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Affiliation(s)
- Francis Gaudreault
- Human Health Therapeutics Research Centre, National Research Council Canada, 6100 Royalmount Avenue, Montreal, QC, H4P 2R2, Canada
| | - Christopher R Corbeil
- Human Health Therapeutics Research Centre, National Research Council Canada, 6100 Royalmount Avenue, Montreal, QC, H4P 2R2, Canada
| | - Traian Sulea
- Human Health Therapeutics Research Centre, National Research Council Canada, 6100 Royalmount Avenue, Montreal, QC, H4P 2R2, Canada.
- Institute of Parasitology, McGill University, 21111 Lakeshore Road, Sainte-Anne-de-Bellevue, QC, H9X 3V9, Canada.
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2
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Launay R, Teppa E, Esque J, André I. Modeling Protein Complexes and Molecular Assemblies Using Computational Methods. Methods Mol Biol 2023; 2553:57-77. [PMID: 36227539 DOI: 10.1007/978-1-0716-2617-7_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Many biological molecules are assembled into supramolecular complexes that are necessary to perform functions in the cell. Better understanding and characterization of these molecular assemblies are thus essential to further elucidate molecular mechanisms and key protein-protein interactions that could be targeted to modulate the protein binding affinity or develop new binders. Experimental access to structural information on these supramolecular assemblies is often hampered by the size of these systems that make their recombinant production and characterization rather difficult. Computational methods combining both structural data, molecular modeling techniques, and sequence coevolution information can thus offer a good alternative to gain access to the structural organization of protein complexes and assemblies. Herein, we present some computational methods to predict structural models of the protein partners, to search for interacting regions using coevolution information, and to build molecular assemblies. The approach is exemplified using a case study to model the succinate-quinone oxidoreductase heterocomplex.
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Affiliation(s)
- Romain Launay
- Toulouse Biotechnology Institute, TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse Cedex 04, France
| | - Elin Teppa
- Toulouse Biotechnology Institute, TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse Cedex 04, France
| | - Jérémy Esque
- Toulouse Biotechnology Institute, TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse Cedex 04, France.
| | - Isabelle André
- Toulouse Biotechnology Institute, TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse Cedex 04, France.
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3
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Zhang W, Meng Q, Wang J, Guo F. HDIContact: a novel predictor of residue-residue contacts on hetero-dimer interfaces via sequential information and transfer learning strategy. Brief Bioinform 2022; 23:6599074. [PMID: 35653713 DOI: 10.1093/bib/bbac169] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 03/07/2022] [Accepted: 04/16/2022] [Indexed: 11/12/2022] Open
Abstract
Proteins maintain the functional order of cell in life by interacting with other proteins. Determination of protein complex structural information gives biological insights for the research of diseases and drugs. Recently, a breakthrough has been made in protein monomer structure prediction. However, due to the limited number of the known protein structure and homologous sequences of complexes, the prediction of residue-residue contacts on hetero-dimer interfaces is still a challenge. In this study, we have developed a deep learning framework for inferring inter-protein residue contacts from sequential information, called HDIContact. We utilized transfer learning strategy to produce Multiple Sequence Alignment (MSA) two-dimensional (2D) embedding based on patterns of concatenated MSA, which could reduce the influence of noise on MSA caused by mismatched sequences or less homology. For MSA 2D embedding, HDIContact took advantage of Bi-directional Long Short-Term Memory (BiLSTM) with two-channel to capture 2D context of residue pairs. Our comprehensive assessment on the Escherichia coli (E. coli) test dataset showed that HDIContact outperformed other state-of-the-art methods, with top precision of 65.96%, the Area Under the Receiver Operating Characteristic curve (AUROC) of 83.08% and the Area Under the Precision Recall curve (AUPR) of 25.02%. In addition, we analyzed the potential of HDIContact for human-virus protein-protein complexes, by achieving top five precision of 80% on O75475-P04584 related to Human Immunodeficiency Virus. All experiments indicated that our method was a valuable technical tool for predicting inter-protein residue contacts, which would be helpful for understanding protein-protein interaction mechanisms.
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Affiliation(s)
- Wei Zhang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Qiaozhen Meng
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Fei Guo
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
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4
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Quignot C, Postic G, Bret H, Rey J, Granger P, Murail S, Chacón P, Andreani J, Tufféry P, Guerois R. InterEvDock3: a combined template-based and free docking server with increased performance through explicit modeling of complex homologs and integration of covariation-based contact maps. Nucleic Acids Res 2021; 49:W277-W284. [PMID: 33978743 PMCID: PMC8265070 DOI: 10.1093/nar/gkab358] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 04/09/2021] [Accepted: 04/23/2021] [Indexed: 12/19/2022] Open
Abstract
The InterEvDock3 protein docking server exploits the constraints of evolution by multiple means to generate structural models of protein assemblies. The server takes as input either several sequences or 3D structures of proteins known to interact. It returns a set of 10 consensus candidate complexes, together with interface predictions to guide further experimental validation interactively. Three key novelties were implemented in InterEvDock3 to help obtain more reliable models: users can (i) generate template-based structural models of assemblies using close and remote homologs of known 3D structure, detected through an automated search protocol, (ii) select the assembly models most consistent with contact maps from external methods that implement covariation-based contact prediction with or without deep learning and (iii) exploit a novel coevolution-based scoring scheme at atomic level, which leads to significantly higher free docking success rates. The performance of the server was validated on two large free docking benchmark databases, containing respectively 230 unbound targets (Weng dataset) and 812 models of unbound targets (PPI4DOCK dataset). Its effectiveness has also been proven on a number of challenging examples. The InterEvDock3 web interface is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/InterEvDock3/.
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Affiliation(s)
- Chloé Quignot
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Guillaume Postic
- Université de Paris, CNRS UMR 8251, INSERM U1133, RPBS, Paris 75205, France
| | - Hélène Bret
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Julien Rey
- Université de Paris, CNRS UMR 8251, INSERM U1133, RPBS, Paris 75205, France
| | - Pierre Granger
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Samuel Murail
- Université de Paris, CNRS UMR 8251, INSERM U1133, RPBS, Paris 75205, France
| | - Pablo Chacón
- Department of Biological Physical Chemistry, Rocasolano Institute of Physical Chemistry C.S.I.C, Madrid, Spain
| | - Jessica Andreani
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Pierre Tufféry
- Université de Paris, CNRS UMR 8251, INSERM U1133, RPBS, Paris 75205, France
| | - Raphaël Guerois
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
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5
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Molecular Dynamics Scoring of Protein-Peptide Models Derived from Coarse-Grained Docking. Molecules 2021; 26:molecules26113293. [PMID: 34070778 PMCID: PMC8197827 DOI: 10.3390/molecules26113293] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 05/22/2021] [Accepted: 05/28/2021] [Indexed: 12/30/2022] Open
Abstract
One of the major challenges in the computational prediction of protein-peptide complexes is the scoring of predicted models. Usually, it is very difficult to find the most accurate solutions out of the vast number of sometimes very different and potentially plausible predictions. In this work, we tested the protocol for Molecular Dynamics (MD)-based scoring of protein-peptide complex models obtained from coarse-grained (CG) docking simulations. In the first step of the scoring procedure, all models generated by CABS-dock were reconstructed starting from their original C-alpha trace representations to all-atom (AA) structures. The second step included geometry optimization of the reconstructed complexes followed by model scoring based on receptor-ligand interaction energy estimated from short MD simulations in explicit water. We used two well-known AA MD force fields, CHARMM and AMBER, and a CG MARTINI force field. Scoring results for 66 different protein-peptide complexes show that the proposed MD-based scoring approach can be used to identify protein-peptide models of high accuracy. The results also indicate that the scoring accuracy may be significantly affected by the quality of the reconstructed protein receptor structures.
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6
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Launay G, Ohue M, Prieto Santero J, Matsuzaki Y, Hilpert C, Uchikoga N, Hayashi T, Martin J. Evaluation of CONSRANK-Like Scoring Functions for Rescoring Ensembles of Protein–Protein Docking Poses. Front Mol Biosci 2020; 7:559005. [PMID: 33195406 PMCID: PMC7641601 DOI: 10.3389/fmolb.2020.559005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 09/28/2020] [Indexed: 11/13/2022] Open
Abstract
Scoring is a challenging step in protein–protein docking, where typically thousands of solutions are generated. In this study, we ought to investigate the contribution of consensus-rescoring, as introduced by Oliva et al. (2013) with the CONSRANK method, where the set of solutions is used to build statistics in order to identify recurrent solutions. We explore several ways to perform consensus-based rescoring on the ZDOCK decoy set for Benchmark 4. We show that the information of the interface size is critical for successful rescoring in this context, but that consensus rescoring in itself performs less well than traditional physics-based evaluation. The results of physics-based and consensus-based rescoring are partially overlapping, supporting the use of a combination of these approaches.
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Affiliation(s)
- Guillaume Launay
- CNRS, UMR 5086 Molecular Microbiology and Structural Biochemistry, University of Lyon, Lyon, France
| | - Masahito Ohue
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, Tokyo, Japan
- *Correspondence: Masahito Ohue,
| | - Julia Prieto Santero
- CNRS, UMR 5086 Molecular Microbiology and Structural Biochemistry, University of Lyon, Lyon, France
| | - Yuri Matsuzaki
- Tokyo Tech Academy for Leadership, Tokyo Institute of Technology, Tokyo, Japan
| | - Cécile Hilpert
- CNRS, UMR 5086 Molecular Microbiology and Structural Biochemistry, University of Lyon, Lyon, France
| | - Nobuyuki Uchikoga
- Department of Network Design, School of Interdisciplinary Mathematical Sciences, Meiji University, Tokyo, Japan
| | - Takanori Hayashi
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, Tokyo, Japan
| | - Juliette Martin
- CNRS, UMR 5086 Molecular Microbiology and Structural Biochemistry, University of Lyon, Lyon, France
- Juliette Martin,
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7
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Chasing coevolutionary signals in intrinsically disordered proteins complexes. Sci Rep 2020; 10:17962. [PMID: 33087759 PMCID: PMC7578644 DOI: 10.1038/s41598-020-74791-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 08/27/2020] [Indexed: 11/30/2022] Open
Abstract
Intrinsically disordered proteins/regions (IDPs/IDRs) are crucial components of the cell, they are highly abundant and participate ubiquitously in a wide range of biological functions, such as regulatory processes and cell signaling. Many of their important functions rely on protein interactions, by which they trigger or modulate different pathways. Sequence covariation, a powerful tool for protein contact prediction, has been applied successfully to predict protein structure and to identify protein–protein interactions mostly of globular proteins. IDPs/IDRs also mediate a plethora of protein–protein interactions, highlighting the importance of addressing sequence covariation-based inter-protein contact prediction of this class of proteins. Despite their importance, a systematic approach to analyze the covariation phenomena of intrinsically disordered proteins and their complexes is still missing. Here we carry out a comprehensive critical assessment of coevolution-based contact prediction in IDP/IDR complexes and detail the challenges and possible limitations that emerge from their analysis. We found that the coevolutionary signal is faint in most of the complexes of disordered proteins but positively correlates with the interface size and binding affinity between partners. In addition, we discuss the state-of-art methodology by biological interpretation of the results, formulate evaluation guidelines and suggest future directions of development to the field.
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8
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Kurcinski M, Pawel Ciemny M, Oleniecki T, Kuriata A, Badaczewska-Dawid AE, Kolinski A, Kmiecik S. CABS-dock standalone: a toolbox for flexible protein-peptide docking. Bioinformatics 2020; 35:4170-4172. [PMID: 30865258 PMCID: PMC6792116 DOI: 10.1093/bioinformatics/btz185] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Revised: 01/30/2019] [Accepted: 03/12/2019] [Indexed: 12/02/2022] Open
Abstract
Summary CABS-dock standalone is a multiplatform Python package for protein–peptide docking with backbone flexibility. The main feature of the CABS-dock method is its ability to simulate significant backbone flexibility of the entire protein–peptide system in a reasonable computational time. In the default mode, the package runs a simulation of fully flexible peptide searching for a binding site on the surface of a flexible protein receptor. The flexibility level of the molecules may be defined by the user. Furthermore, the CABS-dock standalone application provides users with full control over the docking simulation from the initial setup to the analysis of results. The standalone version is an upgrade of the original web server implementation—it introduces a number of customizable options, provides support for large-sized systems and offers a framework for deeper analysis of docking results. Availability and implementation CABS-dock standalone is distributed under the MIT licence, which is free for academic and non-profit users. It is implemented in Python and Fortran. The CABS-dock standalone source code, wiki with documentation and examples of use and installation instructions for Linux, macOS and Windows are available in the CABS-dock standalone repository at https://bitbucket.org/lcbio/cabsdock.
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Affiliation(s)
- Mateusz Kurcinski
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, Warsaw, Poland
| | - Maciej Pawel Ciemny
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, Warsaw, Poland.,Faculty of Physics, University of Warsaw, Warsaw, Poland
| | - Tymoteusz Oleniecki
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, Warsaw, Poland.,College of Inter-Faculty Individual Studies in Mathematics and Natural Sciences, University of Warsaw, Warsaw, Poland
| | - Aleksander Kuriata
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, Warsaw, Poland
| | | | - Andrzej Kolinski
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, Warsaw, Poland
| | - Sebastian Kmiecik
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, Warsaw, Poland
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9
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Andreani J, Quignot C, Guerois R. Structural prediction of protein interactions and docking using conservation and coevolution. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2020. [DOI: 10.1002/wcms.1470] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Jessica Andreani
- Université Paris‐Saclay CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC) Gif‐sur‐Yvette France
| | - Chloé Quignot
- Université Paris‐Saclay CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC) Gif‐sur‐Yvette France
| | - Raphael Guerois
- Université Paris‐Saclay CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC) Gif‐sur‐Yvette France
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10
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Weng G, Gao J, Wang Z, Wang E, Hu X, Yao X, Cao D, Hou T. Comprehensive Evaluation of Fourteen Docking Programs on Protein–Peptide Complexes. J Chem Theory Comput 2020; 16:3959-3969. [DOI: 10.1021/acs.jctc.9b01208] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Gaoqi Weng
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Junbo Gao
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Zhe Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Ercheng Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Xueping Hu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Xiaojun Yao
- State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau (SAR), China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
- State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310058, Zhejiang, China
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11
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Koukos P, Bonvin A. Integrative Modelling of Biomolecular Complexes. J Mol Biol 2020; 432:2861-2881. [DOI: 10.1016/j.jmb.2019.11.009] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2019] [Revised: 11/12/2019] [Accepted: 11/13/2019] [Indexed: 12/31/2022]
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12
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Jing X, Zeng H, Wang S, Xu J. A Web-Based Protocol for Interprotein Contact Prediction by Deep Learning. Methods Mol Biol 2020; 2074:67-80. [PMID: 31583631 DOI: 10.1007/978-1-4939-9873-9_6] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Identifying residue-residue contacts in protein-protein interactions or complex is crucial for understanding protein and cell functions. DCA (direct-coupling analysis) methods shed some light on this, but they need many sequence homologs to yield accurate prediction. Inspired by the success of our deep-learning method for intraprotein contact prediction, we have developed RaptorX-ComplexContact, a web server for interprotein residue-residue contact prediction. Given a pair of interacting protein sequences, RaptorX-ComplexContact first searches for their sequence homologs and builds two paired multiple sequence alignments (MSA) based on genomic distance and phylogeny information, respectively. Then, RaptorX-ComplexContact uses two deep convolutional residual neural networks (ResNet) to predict interprotein contacts from sequential features and coevolution information of paired MSAs. RaptorX-ComplexContact shall be useful for protein docking, protein-protein interaction prediction, and protein interaction network construction.
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Affiliation(s)
- Xiaoyang Jing
- Toyota Technological Institute at Chicago, Chicago, IL, USA
- School of Computer Science, Fudan University, Shanghai, China
| | - Hong Zeng
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Sheng Wang
- Toyota Technological Institute at Chicago, Chicago, IL, USA
| | - Jinbo Xu
- Toyota Technological Institute at Chicago, Chicago, IL, USA.
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13
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Abstract
There is a large gap between the numbers of known protein-protein interactions and the corresponding experimentally solved structures of protein complexes. Fortunately, this gap can be in part bridged by computational structure modeling methods. Currently, template-based modeling is the most accurate means to predict both individual protein structures and protein complexes. One of the major issues in template-based modeling is to identify homologous structures that could be utilized as templates. To simplify this task, we have developed the PPI3D web server. The server is not only able to search for homologous protein complexes, but also provides means to analyze identified interactions and to model protein complexes. In recent CASP and CAPRI experiments, PPI3D proved to be a useful tool for homology modeling of multimeric proteins. In this chapter, we provide a brief description of the PPI3D web server capabilities and how to use the server for modeling of protein complexes.
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Affiliation(s)
- Justas Dapkūnas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania.
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14
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Nadaradjane AA, Quignot C, Traoré S, Andreani J, Guerois R. Docking proteins and peptides under evolutionary constraints in Critical Assessment of PRediction of Interactions rounds 38 to 45. Proteins 2019; 88:986-998. [PMID: 31746034 DOI: 10.1002/prot.25857] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 11/13/2019] [Accepted: 11/15/2019] [Indexed: 01/25/2023]
Abstract
Computational structural prediction of macromolecular interactions is a fundamental tool toward the global understanding of cellular processes. The Critical Assessment of PRediction of Interactions (CAPRI) community-wide experiment provides excellent opportunities for blind testing computational docking methods and includes original targets, thus widening the range of docking applications. Our participation in CAPRI rounds 38 to 45 enabled us to expand the way we include evolutionary information in structural predictions beyond our standard free docking InterEvDock pipeline. InterEvDock integrates a coarse-grained potential that accounts for interface coevolution based on joint multiple sequence alignments of two protein partners (co-alignments). However, even though such co-alignments could be built for none of the CAPRI targets in rounds 38 to 45, including host-pathogen and protein-oligosaccharide complexes and a redesigned interface, we identified multiple strategies that can be used to incorporate evolutionary constraints, which helped us to identify the most likely macromolecular binding modes. These strategies include template-based modeling where only local adjustments should be applied when query-template sequence identity is above 30% and larger perturbations are needed below this threshold; covariation-based structure prediction for individual protein partners; and the identification of evolutionarily conserved and structurally recurrent anchoring interface motifs. Overall, we submitted correct predictions among the top 5 models for 12 out of 19 interface challenges, including four High- and five Medium-quality predictions. Our top 20 models included correct predictions for three out of the five targets we missed in the top 5, including two targets for which misleading biological data led us to downgrade correct free docking models.
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Affiliation(s)
- Aravindan Arun Nadaradjane
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, University of Paris-Sud, Université Paris-Saclay, Gif-sur-Yvette Cedex, France
| | - Chloé Quignot
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, University of Paris-Sud, Université Paris-Saclay, Gif-sur-Yvette Cedex, France
| | - Seydou Traoré
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, University of Paris-Sud, Université Paris-Saclay, Gif-sur-Yvette Cedex, France
| | - Jessica Andreani
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, University of Paris-Sud, Université Paris-Saclay, Gif-sur-Yvette Cedex, France
| | - Raphaël Guerois
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, University of Paris-Sud, Université Paris-Saclay, Gif-sur-Yvette Cedex, France
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15
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Blaszczyk M, Ciemny MP, Kolinski A, Kurcinski M, Kmiecik S. Protein-peptide docking using CABS-dock and contact information. Brief Bioinform 2019; 20:2299-2305. [PMID: 30247502 PMCID: PMC6954405 DOI: 10.1093/bib/bby080] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Revised: 08/09/2018] [Accepted: 08/10/2018] [Indexed: 12/11/2022] Open
Abstract
CABS-dock is a computational method for protein-peptide molecular docking that does not require predefinition of the binding site. The peptide is treated as fully flexible, while the protein backbone undergoes small fluctuations and, optionally, large-scale rearrangements. Here, we present a specific CABS-dock protocol that enhances the docking procedure using fragmentary information about protein-peptide contacts. The contact information is used to narrow down the search for the binding peptide pose to the proximity of the binding site. We used information on a single-chosen and randomly chosen native protein-peptide contact to validate the protocol on the peptiDB benchmark. The contact information significantly improved CABS-dock performance. The protocol has been made available as a new feature of the CABS-dock web server (at http://biocomp.chem.uw.edu.pl/CABSdock/). SHORT ABSTRACT CABS-dock is a tool for flexible docking of peptides to proteins. In this article, we present a protocol for CABS-dock docking driven by information about protein-peptide contact(s). Using information on individual protein-peptide contacts allows to improve the accuracy of CABS-dock docking.
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16
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Molecular Docking Analysis of 120 Potential HPV Therapeutic Epitopes Using a New Analytical Method. Int J Pept Res Ther 2019. [DOI: 10.1007/s10989-019-09985-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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17
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Quignot C, Rey J, Yu J, Tufféry P, Guerois R, Andreani J. InterEvDock2: an expanded server for protein docking using evolutionary and biological information from homology models and multimeric inputs. Nucleic Acids Res 2019; 46:W408-W416. [PMID: 29741647 PMCID: PMC6030979 DOI: 10.1093/nar/gky377] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 05/02/2018] [Indexed: 12/15/2022] Open
Abstract
Computational protein docking is a powerful strategy to predict structures of protein-protein interactions and provides crucial insights for the functional characterization of macromolecular cross-talks. We previously developed InterEvDock, a server for ab initio protein docking based on rigid-body sampling followed by consensus scoring using physics-based and statistical potentials, including the InterEvScore function specifically developed to incorporate co-evolutionary information in docking. InterEvDock2 is a major evolution of InterEvDock which allows users to submit input sequences – not only structures – and multimeric inputs and to specify constraints for the pairwise docking process based on previous knowledge about the interaction. For this purpose, we added modules in InterEvDock2 for automatic template search and comparative modeling of the input proteins. The InterEvDock2 pipeline was benchmarked on 812 complexes for which unbound homology models of the two partners and co-evolutionary information are available in the PPI4DOCK database. InterEvDock2 identified a correct model among the top 10 consensus in 29% of these cases (compared to 15–24% for individual scoring functions) and at least one correct interface residue among 10 predicted in 91% of these cases. InterEvDock2 is thus a unique protein docking server, designed to be useful for the experimental biology community. The InterEvDock2 web interface is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/InterEvDock2/.
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Affiliation(s)
- Chloé Quignot
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, Université Paris-Saclay, 91198, Gif-sur-Yvette cedex, France
| | - Julien Rey
- INSERM UMR-S 973, Université Paris Diderot, Sorbonne Paris Cité, RPBS, Paris 75205, France
| | - Jinchao Yu
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, Université Paris-Saclay, 91198, Gif-sur-Yvette cedex, France
| | - Pierre Tufféry
- INSERM UMR-S 973, Université Paris Diderot, Sorbonne Paris Cité, RPBS, Paris 75205, France
| | - Raphaël Guerois
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, Université Paris-Saclay, 91198, Gif-sur-Yvette cedex, France
| | - Jessica Andreani
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, Université Paris-Saclay, 91198, Gif-sur-Yvette cedex, France
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18
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Dapkūnas J, Olechnovič K, Venclovas Č. Structural modeling of protein complexes: Current capabilities and challenges. Proteins 2019; 87:1222-1232. [PMID: 31294859 DOI: 10.1002/prot.25774] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 06/21/2019] [Accepted: 07/06/2019] [Indexed: 12/27/2022]
Abstract
Proteins frequently interact with each other, and the knowledge of structures of the corresponding protein complexes is necessary to understand how they function. Computational methods are increasingly used to provide structural models of protein complexes. Not surprisingly, community-wide Critical Assessment of protein Structure Prediction (CASP) experiments have recently started monitoring the progress in this research area. We participated in CASP13 with the aim to evaluate our current capabilities in modeling of protein complexes and to gain a better understanding of factors that exert the largest impact on these capabilities. To model protein complexes in CASP13, we applied template-based modeling, free docking and hybrid techniques that enabled us to generate models of the topmost quality for 27 of 42 multimers. If templates for protein complexes could be identified, we modeled the structures with reasonable accuracy by straightforward homology modeling. If only partial templates were available, it was nevertheless possible to predict the interaction interfaces correctly or to generate acceptable models for protein complexes by combining template-based modeling with docking. If no templates were available, we used rigid-body docking with limited success. However, in some free docking models, despite the incorrect subunit orientation and missed interface contacts, the approximate location of protein binding sites was identified correctly. Apparently, our overall performance in docking was limited by the quality of monomer models and by the imperfection of scoring methods. The impact of human intervention on our results in modeling of protein complexes was significant indicating the need for improvements of automatic methods.
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Affiliation(s)
- Justas Dapkūnas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Kliment Olechnovič
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
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19
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Lee ACL, Harris JL, Khanna KK, Hong JH. A Comprehensive Review on Current Advances in Peptide Drug Development and Design. Int J Mol Sci 2019; 20:ijms20102383. [PMID: 31091705 PMCID: PMC6566176 DOI: 10.3390/ijms20102383] [Citation(s) in RCA: 352] [Impact Index Per Article: 70.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 05/09/2019] [Accepted: 05/10/2019] [Indexed: 11/16/2022] Open
Abstract
Protein-protein interactions (PPIs) execute many fundamental cellular functions and have served as prime drug targets over the last two decades. Interfering intracellular PPIs with small molecules has been extremely difficult for larger or flat binding sites, as antibodies cannot cross the cell membrane to reach such target sites. In recent years, peptides smaller size and balance of conformational rigidity and flexibility have made them promising candidates for targeting challenging binding interfaces with satisfactory binding affinity and specificity. Deciphering and characterizing peptide-protein recognition mechanisms is thus central for the invention of peptide-based strategies to interfere with endogenous protein interactions, or improvement of the binding affinity and specificity of existing approaches. Importantly, a variety of computation-aided rational designs for peptide therapeutics have been developed, which aim to deliver comprehensive docking for peptide-protein interaction interfaces. Over 60 peptides have been approved and administrated globally in clinics. Despite this, advances in various docking models are only on the merge of making their contribution to peptide drug development. In this review, we provide (i) a holistic overview of peptide drug development and the fundamental technologies utilized to date, and (ii) an updated review on key developments of computational modeling of peptide-protein interactions (PepPIs) with an aim to assist experimental biologists exploit suitable docking methods to advance peptide interfering strategies against PPIs.
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Affiliation(s)
- Andy Chi-Lung Lee
- QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia.
- Radiation Biology Research Center, Institute for Radiological Research, Chang Gung Memorial Hospital, Chang Gung University, Taoyuan 333, Taiwan.
- Department of Radiation Oncology, Chang Gung Memorial Hospital, Linkou 333, Taiwan.
| | | | - Kum Kum Khanna
- QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia.
| | - Ji-Hong Hong
- Radiation Biology Research Center, Institute for Radiological Research, Chang Gung Memorial Hospital, Chang Gung University, Taoyuan 333, Taiwan.
- Department of Radiation Oncology, Chang Gung Memorial Hospital, Linkou 333, Taiwan.
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20
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Takemura K, Matubayasi N, Kitao A. Binding free energy analysis of protein-protein docking model structures by evERdock. J Chem Phys 2018; 148:105101. [PMID: 29544320 DOI: 10.1063/1.5019864] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
To aid the evaluation of protein-protein complex model structures generated by protein docking prediction (decoys), we previously developed a method to calculate the binding free energies for complexes. The method combines a short (2 ns) all-atom molecular dynamics simulation with explicit solvent and solution theory in the energy representation (ER). We showed that this method successfully selected structures similar to the native complex structure (near-native decoys) as the lowest binding free energy structures. In our current work, we applied this method (evERdock) to 100 or 300 model structures of four protein-protein complexes. The crystal structures and the near-native decoys showed the lowest binding free energy of all the examined structures, indicating that evERdock can successfully evaluate decoys. Several decoys that show low interface root-mean-square distance but relatively high binding free energy were also identified. Analysis of the fraction of native contacts, hydrogen bonds, and salt bridges at the protein-protein interface indicated that these decoys were insufficiently optimized at the interface. After optimizing the interactions around the interface by including interfacial water molecules, the binding free energies of these decoys were improved. We also investigated the effect of solute entropy on binding free energy and found that consideration of the entropy term does not necessarily improve the evaluations of decoys using the normal model analysis for entropy calculation.
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Affiliation(s)
- Kazuhiro Takemura
- Institute of Molecular and Cellular Biosciences, University of Tokyo, Bunkyo, Tokyo 113-0032, Japan
| | - Nobuyuki Matubayasi
- Division of Chemical Engineering, Graduate School of Engineering Science, Osaka University, Toyonaka, Osaka 560-8531, Japan
| | - Akio Kitao
- Institute of Molecular and Cellular Biosciences, University of Tokyo, Bunkyo, Tokyo 113-0032, Japan
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21
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Ciemny M, Kurcinski M, Kamel K, Kolinski A, Alam N, Schueler-Furman O, Kmiecik S. Protein-peptide docking: opportunities and challenges. Drug Discov Today 2018; 23:1530-1537. [PMID: 29733895 DOI: 10.1016/j.drudis.2018.05.006] [Citation(s) in RCA: 153] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Revised: 03/20/2018] [Accepted: 05/02/2018] [Indexed: 12/31/2022]
Abstract
Peptides have recently attracted much attention as promising drug candidates. Rational design of peptide-derived therapeutics usually requires structural characterization of the underlying protein-peptide interaction. Given that experimental characterization can be difficult, reliable computational tools are needed. In recent years, a variety of approaches have been developed for 'protein-peptide docking', that is, predicting the structure of the protein-peptide complex, starting from the protein structure and the peptide sequence, including variable degrees of information about the peptide binding site and/or conformation. In this review, we provide an overview of protein-peptide docking methods and outline their capabilities, limitations, and applications in structure-based drug design. Key challenges are also briefly discussed, such as modeling of large-scale conformational changes upon binding, scoring of predicted models, and optimal inclusion of varied types of experimental data and theoretical predictions into an integrative modeling process.
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Affiliation(s)
- Maciej Ciemny
- Biological and Chemical Research Center, Faculty of Chemistry, University of Warsaw, Warsaw, Poland; Faculty of Physics, University of Warsaw, Warsaw, Poland
| | - Mateusz Kurcinski
- Biological and Chemical Research Center, Faculty of Chemistry, University of Warsaw, Warsaw, Poland
| | - Karol Kamel
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Andrzej Kolinski
- Biological and Chemical Research Center, Faculty of Chemistry, University of Warsaw, Warsaw, Poland
| | - Nawsad Alam
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University, Jerusalem, Israel
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University, Jerusalem, Israel
| | - Sebastian Kmiecik
- Biological and Chemical Research Center, Faculty of Chemistry, University of Warsaw, Warsaw, Poland.
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22
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Berto A, Yu J, Morchoisne-Bolhy S, Bertipaglia C, Vallee R, Dumont J, Ochsenbein F, Guerois R, Doye V. Disentangling the molecular determinants for Cenp-F localization to nuclear pores and kinetochores. EMBO Rep 2018; 19:embr.201744742. [PMID: 29632243 DOI: 10.15252/embr.201744742] [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: 07/05/2017] [Revised: 03/02/2018] [Accepted: 03/08/2018] [Indexed: 11/09/2022] Open
Abstract
Cenp-F is a multifaceted protein implicated in cancer and developmental pathologies. The Cenp-F C-terminal region contains overlapping binding sites for numerous proteins that contribute to its functions throughout the cell cycle. Here, we focus on the nuclear pore protein Nup133 that interacts with Cenp-F both at nuclear pores in prophase and at kinetochores in mitosis, and on the kinase Bub1, known to contribute to Cenp-F targeting to kinetochores. By combining in silico structural modeling and yeast two-hybrid assays, we generate an interaction model between a conserved helix within the Nup133 β-propeller and a short leucine zipper-containing dimeric segment of Cenp-F. We thereby create mutants affecting the Nup133/Cenp-F interface and show that they prevent Cenp-F localization to the nuclear envelope, but not to kinetochores. Conversely, a point mutation within an adjacent leucine zipper affecting the kinetochore targeting of Cenp-F KT-core domain impairs its interaction with Bub1, but not with Nup133, identifying Bub1 as the direct KT-core binding partner of Cenp-F. Finally, we show that Cenp-E redundantly contributes together with Bub1 to the recruitment of Cenp-F to kinetochores.
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Affiliation(s)
- Alessandro Berto
- Institut Jacques Monod, UMR7592, CNRS, Université Paris Diderot, Sorbonne Paris Cité, Paris, France.,Ecole Doctorale Structure et Dynamique des Systèmes Vivants (#577), Univ Paris Sud, Université Paris-Saclay, Orsay, France
| | - Jinchao Yu
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ Paris Sud, Université Paris-Saclay, Gif sur Yvette, France
| | | | - Chiara Bertipaglia
- Department of Pathology and Cell Biology, Columbia University, New York, NY, USA
| | - Richard Vallee
- Department of Pathology and Cell Biology, Columbia University, New York, NY, USA
| | - Julien Dumont
- Institut Jacques Monod, UMR7592, CNRS, Université Paris Diderot, Sorbonne Paris Cité, Paris, France
| | - Francoise Ochsenbein
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ Paris Sud, Université Paris-Saclay, Gif sur Yvette, France
| | - Raphael Guerois
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ Paris Sud, Université Paris-Saclay, Gif sur Yvette, France
| | - Valérie Doye
- Institut Jacques Monod, UMR7592, CNRS, Université Paris Diderot, Sorbonne Paris Cité, Paris, France
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23
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Lamothe G, Malliavin TE. re-TAMD: exploring interactions between H3 peptide and YEATS domain using enhanced sampling. BMC STRUCTURAL BIOLOGY 2018; 18:4. [PMID: 29615024 PMCID: PMC5883362 DOI: 10.1186/s12900-018-0083-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Accepted: 03/04/2018] [Indexed: 01/05/2023]
Abstract
BACKGROUND Analysis of preferred binding regions of a ligand on a protein is important for detecting cryptic binding pockets and improving the ligand selectivity. RESULT The enhanced sampling approach TAMD has been adapted to allow a ligand to unbind from its native binding site and explore the protein surface. This so-called re-TAMD procedure was then used to explore the interaction between the N terminal peptide of histone H3 and the YEATS domain. Depending on the length of the peptide, several regions of the protein surface were explored. The peptide conformations sampled during the re-TAMD correspond to peptide free diffusion around the protein surface. CONCLUSIONS The re-TAMD approach permitted to get information on the relative influence of different regions of the N terminal peptide of H3 on the interaction between H3 and YEATS.
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Affiliation(s)
- Gilles Lamothe
- Unité de Bioinformatique Structurale, UMR CNRS 3528 and Institut Pasteur, Paris, France.,Université Denis Diderot Paris 7, Paris, France
| | - Thérèse E Malliavin
- Unité de Bioinformatique Structurale, UMR CNRS 3528 and Institut Pasteur, Paris, France.
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24
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Abstract
The structural modeling of protein complexes by docking simulations has been attracting increasing interest with the rise of proteomics and of the number of experimentally identified binary interactions. Structures of unbound partners, either modeled or experimentally determined, can be used as input to sample as extensively as possible all putative binding modes and single out the most plausible ones. At the scoring step, evolutionary information contained in the joint multiple sequence alignments of both partners can provide key insights to recognize correct interfaces. Here, we describe a computational protocol based on the InterEvDock web server to exploit coevolution constraints in protein-protein docking methods. We provide methodology guidelines to prepare the input protein structures and generate improved alignments. We also explain how to extract and use the information returned by the server through the analysis of two representative examples.
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Affiliation(s)
- Aravindan Arun Nadaradjane
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, Université Paris-Saclay, 91198, Gif-sur-Yvette Cedex, France
| | - Raphael Guerois
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, Université Paris-Saclay, 91198, Gif-sur-Yvette Cedex, France.
| | - Jessica Andreani
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, Université Paris-Saclay, 91198, Gif-sur-Yvette Cedex, France.
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25
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Suplatov D, Sharapova Y, Timonina D, Kopylov K, Švedas V. The visualCMAT: A web-server to select and interpret correlated mutations/co-evolving residues in protein families. J Bioinform Comput Biol 2017; 16:1840005. [PMID: 29361894 DOI: 10.1142/s021972001840005x] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The visualCMAT web-server was designed to assist experimental research in the fields of protein/enzyme biochemistry, protein engineering, and drug discovery by providing an intuitive and easy-to-use interface to the analysis of correlated mutations/co-evolving residues. Sequence and structural information describing homologous proteins are used to predict correlated substitutions by the Mutual information-based CMAT approach, classify them into spatially close co-evolving pairs, which either form a direct physical contact or interact with the same ligand (e.g. a substrate or a crystallographic water molecule), and long-range correlations, annotate and rank binding sites on the protein surface by the presence of statistically significant co-evolving positions. The results of the visualCMAT are organized for a convenient visual analysis and can be downloaded to a local computer as a content-rich all-in-one PyMol session file with multiple layers of annotation corresponding to bioinformatic, statistical and structural analyses of the predicted co-evolution, or further studied online using the built-in interactive analysis tools. The online interactivity is implemented in HTML5 and therefore neither plugins nor Java are required. The visualCMAT web-server is integrated with the Mustguseal web-server capable of constructing large structure-guided sequence alignments of protein families and superfamilies using all available information about their structures and sequences in public databases. The visualCMAT web-server can be used to understand the relationship between structure and function in proteins, implemented at selecting hotspots and compensatory mutations for rational design and directed evolution experiments to produce novel enzymes with improved properties, and employed at studying the mechanism of selective ligand's binding and allosteric communication between topologically independent sites in protein structures. The web-server is freely available at https://biokinet.belozersky.msu.ru/visualcmat and there are no login requirements.
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Affiliation(s)
- Dmitry Suplatov
- 1 Belozersky Institute of Physicochemical Biology, Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Leninskiye Gory 1-73, Moscow 119991, Russia
| | - Yana Sharapova
- 1 Belozersky Institute of Physicochemical Biology, Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Leninskiye Gory 1-73, Moscow 119991, Russia
| | - Daria Timonina
- 1 Belozersky Institute of Physicochemical Biology, Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Leninskiye Gory 1-73, Moscow 119991, Russia
| | - Kirill Kopylov
- 1 Belozersky Institute of Physicochemical Biology, Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Leninskiye Gory 1-73, Moscow 119991, Russia
| | - Vytas Švedas
- 1 Belozersky Institute of Physicochemical Biology, Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Leninskiye Gory 1-73, Moscow 119991, Russia
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26
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Dapkūnas J, Olechnovič K, Venclovas Č. Modeling of protein complexes in CAPRI Round 37 using template-based approach combined with model selection. Proteins 2017; 86 Suppl 1:292-301. [DOI: 10.1002/prot.25378] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Revised: 08/25/2017] [Accepted: 09/10/2017] [Indexed: 01/14/2023]
Affiliation(s)
- Justas Dapkūnas
- Institute of Biotechnology, Vilnius University, Saulėtekio 7; Vilnius LT-10257 Lithuania
| | - Kliment Olechnovič
- Institute of Biotechnology, Vilnius University, Saulėtekio 7; Vilnius LT-10257 Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Vilnius University, Saulėtekio 7; Vilnius LT-10257 Lithuania
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27
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Abstract
Co-evolution techniques were originally conceived to assist in protein structure prediction by inferring pairs of residues that share spatial proximity. However, the functional relationships that can be extrapolated from co-evolution have also proven to be useful in a wide array of structural bioinformatics applications. These techniques are a powerful way to extract structural and functional information in a sequence-rich world.
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