1
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Aderinwale T, Christoffer C, Kihara D. RL-MLZerD: Multimeric protein docking using reinforcement learning. Front Mol Biosci 2022; 9:969394. [PMID: 36090027 PMCID: PMC9459051 DOI: 10.3389/fmolb.2022.969394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 08/08/2022] [Indexed: 11/24/2022] Open
Abstract
Numerous biological processes in a cell are carried out by protein complexes. To understand the molecular mechanisms of such processes, it is crucial to know the quaternary structures of the complexes. Although the structures of protein complexes have been determined by biophysical experiments at a rapid pace, there are still many important complex structures that are yet to be determined. To supplement experimental structure determination of complexes, many computational protein docking methods have been developed; however, most of these docking methods are designed only for docking with two chains. Here, we introduce a novel method, RL-MLZerD, which builds multiple protein complexes using reinforcement learning (RL). In RL-MLZerD a multi-chain assembly process is considered as a series of episodes of selecting and integrating pre-computed pairwise docking models in a RL framework. RL is effective in correctly selecting plausible pairwise models that fit well with other subunits in a complex. When tested on a benchmark dataset of protein complexes with three to five chains, RL-MLZerD showed better modeling performance than other existing multiple docking methods under different evaluation criteria, except against AlphaFold-Multimer in unbound docking. Also, it emerged that the docking order of multi-chain complexes can be naturally predicted by examining preferred paths of episodes in the RL computation.
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Affiliation(s)
- Tunde Aderinwale
- Department of Computer Science, Purdue University, West Lafayette, IN, United States
| | - Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, IN, United States
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN, United States
- Department of Biological Sciences, Purdue University, West Lafayette, IN, United States
- *Correspondence: Daisuke Kihara,
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2
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Ruiz Echartea ME, Ritchie DW, Chauvot de Beauchêne I. Using restraints in
EROS‐DOCK
improves model quality in pairwise and multicomponent protein docking. Proteins 2020; 88:1121-1128. [DOI: 10.1002/prot.25959] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 03/04/2020] [Accepted: 05/27/2020] [Indexed: 12/26/2022]
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3
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Popov P, Grudinin S, Kurdiuk A, Buslaev P, Redon S. Controlled-advancement rigid-body optimization of nanosystems. J Comput Chem 2019; 40:2391-2399. [PMID: 31254466 DOI: 10.1002/jcc.26016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 05/23/2019] [Accepted: 06/06/2019] [Indexed: 11/11/2022]
Abstract
In this study, we propose a novel optimization algorithm, with application to the refinement of molecular complexes. Particularly, we consider optimization problem as the calculation of quasi-static trajectories of rigid bodies influenced by the inverse-inertia-weighted energy gradient and introduce the concept of advancement region that guarantees displacement of a molecule strictly within a relevant region of conformational space. The advancement region helps to avoid typical energy minimization pitfalls, thus, the algorithm is suitable to work with arbitrary energy functions and arbitrary types of molecular complexes without necessary tuning of its hyper-parameters. Our method, called controlled-advancement rigid-body optimization of nanosystems (Carbon), is particularly useful for the large-scale molecular refinement, as for example, the putative binding candidates obtained with protein-protein docking pipelines. Implementation of Carbon with user-friendly interface is available in the SAMSON platform for molecular modeling at https://www.samson-connect.net. © 2019 Wiley Periodicals, Inc.
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Affiliation(s)
- Petr Popov
- Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Sergei Grudinin
- CNRS, Grenoble INP, LJK, University Grenoble Alpes, Inria, 38000, Grenoble, France
| | - Andrii Kurdiuk
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Pavel Buslaev
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Stephane Redon
- CNRS, Grenoble INP, LJK, University Grenoble Alpes, Inria, 38000, Grenoble, France
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4
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Neveu E, Popov P, Hoffmann A, Migliosi A, Besseron X, Danoy G, Bouvry P, Grudinin S. RapidRMSD: rapid determination of RMSDs corresponding to motions of flexible molecules. Bioinformatics 2019; 34:2757-2765. [PMID: 29554205 DOI: 10.1093/bioinformatics/bty160] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Accepted: 03/13/2018] [Indexed: 12/27/2022] Open
Abstract
Motivation The root mean square deviation (RMSD) is one of the most used similarity criteria in structural biology and bioinformatics. Standard computation of the RMSD has a linear complexity with respect to the number of atoms in a molecule, making RMSD calculations time-consuming for the large-scale modeling applications, such as assessment of molecular docking predictions or clustering of spatially proximate molecular conformations. Previously, we introduced the RigidRMSD algorithm to compute the RMSD corresponding to the rigid-body motion of a molecule. In this study, we go beyond the limits of the rigid-body approximation by taking into account conformational flexibility of the molecule. We model the flexibility with a reduced set of collective motions computed with e.g. normal modes or principal component analysis. Results The initialization of our algorithm is linear in the number of atoms and all the subsequent evaluations of RMSD values between flexible molecular conformations depend only on the number of collective motions that are selected to model the flexibility. Therefore, our algorithm is much faster compared to the standard RMSD computation for large-scale modeling applications. We demonstrate the efficiency of our method on several clustering examples, including clustering of flexible docking results and molecular dynamics (MD) trajectories. We also demonstrate how to use the presented formalism to generate pseudo-random constant-RMSD structural molecular ensembles and how to use these in cross-docking. Availability and implementation We provide the algorithm written in C++ as the open-source RapidRMSD library governed by the BSD-compatible license, which is available at http://team.inria.fr/nano-d/software/RapidRMSD/. The constant-RMSD structural ensemble application and clustering of MD trajectories is available at http://team.inria.fr/nano-d/software/nolb-normal-modes/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Emilie Neveu
- Inria/Univ. Grenoble Alpes/LJK-CNRS, Grenoble, France.,Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Petr Popov
- Inria/Univ. Grenoble Alpes/LJK-CNRS, Grenoble, France.,Moscow Institute of Physics and Technology, Dolgoprudniy, Russia
| | | | - Angelo Migliosi
- Faculté des Sciences, de la Technologie et de la Communication, University of Luxembourg, Luxembourg, Luxembourg
| | - Xavier Besseron
- Faculté des Sciences, de la Technologie et de la Communication, University of Luxembourg, Luxembourg, Luxembourg
| | - Grégoire Danoy
- Faculté des Sciences, de la Technologie et de la Communication, University of Luxembourg, Luxembourg, Luxembourg
| | - Pascal Bouvry
- Faculté des Sciences, de la Technologie et de la Communication, University of Luxembourg, Luxembourg, Luxembourg
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5
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Peterson LX, Togawa Y, Esquivel-Rodriguez J, Terashi G, Christoffer C, Roy A, Shin WH, Kihara D. Modeling the assembly order of multimeric heteroprotein complexes. PLoS Comput Biol 2018; 14:e1005937. [PMID: 29329283 PMCID: PMC5785014 DOI: 10.1371/journal.pcbi.1005937] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 01/25/2018] [Accepted: 12/19/2017] [Indexed: 12/31/2022] Open
Abstract
Protein-protein interactions are the cornerstone of numerous biological processes. Although an increasing number of protein complex structures have been determined using experimental methods, relatively fewer studies have been performed to determine the assembly order of complexes. In addition to the insights into the molecular mechanisms of biological function provided by the structure of a complex, knowing the assembly order is important for understanding the process of complex formation. Assembly order is also practically useful for constructing subcomplexes as a step toward solving the entire complex experimentally, designing artificial protein complexes, and developing drugs that interrupt a critical step in the complex assembly. There are several experimental methods for determining the assembly order of complexes; however, these techniques are resource-intensive. Here, we present a computational method that predicts the assembly order of protein complexes by building the complex structure. The method, named Path-LzerD, uses a multimeric protein docking algorithm that assembles a protein complex structure from individual subunit structures and predicts assembly order by observing the simulated assembly process of the complex. Benchmarked on a dataset of complexes with experimental evidence of assembly order, Path-LZerD was successful in predicting the assembly pathway for the majority of the cases. Moreover, when compared with a simple approach that infers the assembly path from the buried surface area of subunits in the native complex, Path-LZerD has the strong advantage that it can be used for cases where the complex structure is not known. The path prediction accuracy decreased when starting from unbound monomers, particularly for larger complexes of five or more subunits, for which only a part of the assembly path was correctly identified. As the first method of its kind, Path-LZerD opens a new area of computational protein structure modeling and will be an indispensable approach for studying protein complexes.
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Affiliation(s)
- Lenna X. Peterson
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America
| | - Yoichiro Togawa
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America
| | - Juan Esquivel-Rodriguez
- Department of Computer Science, Purdue University, West Lafayette, Indiana, United States of America
| | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America
| | - Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, Indiana, United States of America
| | - Amitava Roy
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, Indiana, United States of America
- Bioinformatics and Computational Biosciences Branch, Rocky Mountain Laboratories, NIAID, National Institutes of Health, Hamilton, Montana, United States of America
| | - Woong-Hee Shin
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America
- Department of Computer Science, Purdue University, West Lafayette, Indiana, United States of America
- * E-mail:
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6
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de Vries SJ, Zacharias M. Fast and accurate grid representations for atom-based docking with partner flexibility. J Comput Chem 2017; 38:1538-1546. [DOI: 10.1002/jcc.24795] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2016] [Revised: 01/18/2017] [Accepted: 01/19/2017] [Indexed: 12/12/2022]
Affiliation(s)
- Sjoerd J. de Vries
- MTi, UMR-S 973, Physics Department T38; Technische Universität München; James-Franck-Strasse 1 85748 Garching Germany
| | - Martin Zacharias
- MTi, UMR-S 973, Physics Department T38; Technische Universität München; James-Franck-Strasse 1 85748 Garching Germany
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7
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Hasani HJ, Barakat KH. Protein-Protein Docking. PHARMACEUTICAL SCIENCES 2017. [DOI: 10.4018/978-1-5225-1762-7.ch042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Protein-protein docking algorithms are powerful computational tools, capable of analyzing the protein-protein interactions at the atomic-level. In this chapter, we will review the theoretical concepts behind different protein-protein docking algorithms, highlighting their strengths as well as their limitations and pointing to important case studies for each method. The methods we intend to cover in this chapter include various search strategies and scoring techniques. This includes exhaustive global search, fast Fourier transform search, spherical Fourier transform-based search, direct search in Cartesian space, local shape feature matching, geometric hashing, genetic algorithm, randomized search, and Monte Carlo search. We will also discuss the different ways that have been used to incorporate protein flexibility within the docking procedure and some other future directions in this field, suggesting possible ways to improve the different methods.
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8
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Aumayr M, Fedosyuk S, Ruzicska K, Sousa-Blin C, Kontaxis G, Skern T. NMR analysis of the interaction of picornaviral proteinases Lb and 2A with their substrate eukaryotic initiation factor 4GII. Protein Sci 2015; 24:1979-96. [PMID: 26384734 PMCID: PMC4815241 DOI: 10.1002/pro.2807] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2015] [Revised: 09/14/2015] [Accepted: 09/17/2015] [Indexed: 11/09/2022]
Abstract
Messenger RNA is recruited to the eukaryotic ribosome by a complex including the eukaryotic initiation factor (eIF) 4E (the cap-binding protein), the scaffold protein eIF4G and the RNA helicase eIF4A. To shut-off host-cell protein synthesis, eIF4G is cleaved during picornaviral infection by a virally encoded proteinase; the structural basis of this reaction and its stimulation by eIF4E is unclear. We have structurally and biochemically investigated the interaction of purified foot-and-mouth disease virus (FMDV) leader proteinase (Lb(pro)), human rhinovirus 2 (HRV2) 2A proteinase (2A(pro)) and coxsackievirus B4 (CVB4) 2A(pro) with purified eIF4GII, eIF4E and the eIF4GII/eIF4E complex. Using nuclear magnetic resonance (NMR), we completed (13)C/(15) N sequential backbone assignment of human eIF4GII residues 551-745 and examined their binding to murine eIF4E. eIF4GII551-745 is intrinsically unstructured and remains so when bound to eIF4E. NMR and biophysical techniques for determining stoichiometry and binding constants revealed that the papain-like Lb(pro) only forms a stable complex with eIF4GII(551-745) in the presence of eIF4E, with KD values in the low nanomolar range; Lb(pro) contacts both eIF4GII and eIF4E. Furthermore, the unrelated chymotrypsin-like 2A(pro) from HRV2 and CVB4 also build a stable complex with eIF4GII/eIF4E, but with K(D) values in the low micromolar range. The HRV2 enzyme also forms a stable complex with eIF4E; however, none of the proteinases tested complex stably with eIF4GII alone. Thus, these three picornaviral proteinases have independently evolved to establish distinct triangular heterotrimeric protein complexes that may actively target ribosomes involved in mRNA recruitment to ensure efficient host cell shut-off.
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Affiliation(s)
- Martina Aumayr
- Department of Medical Biochemistry, Max F. Perutz Laboratories, Medical University of Vienna, Vienna Biocenter, Dr. Bohr-Gasse 9/3, Vienna, A-1030, Austria
| | - Sofiya Fedosyuk
- Department of Medical Biochemistry, Max F. Perutz Laboratories, Medical University of Vienna, Vienna Biocenter, Dr. Bohr-Gasse 9/3, Vienna, A-1030, Austria
| | - Katharina Ruzicska
- Department of Medical Biochemistry, Max F. Perutz Laboratories, Medical University of Vienna, Vienna Biocenter, Dr. Bohr-Gasse 9/3, Vienna, A-1030, Austria
| | - Carla Sousa-Blin
- Department of Medical Biochemistry, Max F. Perutz Laboratories, Medical University of Vienna, Vienna Biocenter, Dr. Bohr-Gasse 9/3, Vienna, A-1030, Austria
| | - Georg Kontaxis
- Department of Structural and Computational Biology, Max F. Perutz Laboratories, University of Vienna, Campus Vienna Biocenter 5, Vienna, A-1030, Austria
| | - Tim Skern
- Department of Medical Biochemistry, Max F. Perutz Laboratories, Medical University of Vienna, Vienna Biocenter, Dr. Bohr-Gasse 9/3, Vienna, A-1030, Austria
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9
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Dietzen M, Kalinina OV, Taškova K, Kneissl B, Hildebrandt AK, Jaenicke E, Decker H, Lengauer T, Hildebrandt A. Large oligomeric complex structures can be computationally assembled by efficiently combining docked interfaces. Proteins 2015; 83:1887-99. [PMID: 26248608 PMCID: PMC5049452 DOI: 10.1002/prot.24873] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2015] [Revised: 07/20/2015] [Accepted: 07/29/2015] [Indexed: 11/06/2022]
Abstract
Macromolecular oligomeric assemblies are involved in many biochemical processes of living organisms. The benefits of such assemblies in crowded cellular environments include increased reaction rates, efficient feedback regulation, cooperativity and protective functions. However, an atom-level structural determination of large assemblies is challenging due to the size of the complex and the difference in binding affinities of the involved proteins. In this study, we propose a novel combinatorial greedy algorithm for assembling large oligomeric complexes from information on the approximate position of interaction interfaces of pairs of monomers in the complex. Prior information on complex symmetry is not required but rather the symmetry is inferred during assembly. We implement an efficient geometric score, the transformation match score, that bypasses the model ranking problems of state-of-the-art scoring functions by scoring the similarity between the inferred dimers of the same monomer simultaneously with different binding partners in a (sub)complex with a set of pregenerated docking poses. We compiled a diverse benchmark set of 308 homo and heteromeric complexes containing 6 to 60 monomers. To explore the applicability of the method, we considered 48 sets of parameters and selected those three sets of parameters, for which the algorithm can correctly reconstruct the maximum number, namely 252 complexes (81.8%) in, at least one of the respective three runs. The crossvalidation coverage, that is, the mean fraction of correctly reconstructed benchmark complexes during crossvalidation, was 78.1%, which demonstrates the ability of the presented method to correctly reconstruct topology of a large variety of biological complexes.
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Affiliation(s)
- Matthias Dietzen
- Max Planck Institute for Informatics, Campus E1 4, Saarbrücken, 66123, Germany
| | - Olga V Kalinina
- Max Planck Institute for Informatics, Campus E1 4, Saarbrücken, 66123, Germany
| | - Katerina Taškova
- Institute of Computer Science, Johannes Gutenberg University, Staudingerweg 9, Mainz, 55128, Germany.,Institute for Molecular Biology, Johannes Gutenberg University, Ackermannweg 4, Mainz, 55128, Germany
| | - Benny Kneissl
- Institute of Computer Science, Johannes Gutenberg University, Staudingerweg 9, Mainz, 55128, Germany.,Roche Pharma Research and Early Development, pRED Informatics, Roche Innovation Center Penzberg, Nonnenwald 2, Penzberg, 82377, Germany
| | | | - Elmar Jaenicke
- Institute of Molecular Biophysics, Johannes Gutenberg University, Jakob-Welder-Weg 26, Mainz, 55128, Germany
| | - Heinz Decker
- Institute of Molecular Biophysics, Johannes Gutenberg University, Jakob-Welder-Weg 26, Mainz, 55128, Germany
| | - Thomas Lengauer
- Max Planck Institute for Informatics, Campus E1 4, Saarbrücken, 66123, Germany
| | - Andreas Hildebrandt
- Institute of Computer Science, Johannes Gutenberg University, Staudingerweg 9, Mainz, 55128, Germany
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10
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Bonjack-Shterengartz M, Avnir D. The near-symmetry of proteins. Proteins 2015; 83:722-34. [PMID: 25354765 DOI: 10.1002/prot.24706] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2014] [Revised: 10/06/2014] [Accepted: 10/18/2014] [Indexed: 11/07/2022]
Abstract
The majority of protein oligomers form clusters which are nearly symmetric. Understanding of that imperfection, its origins, and perhaps also its advantages requires the conversion of the currently used vague qualitative descriptive language of the near-symmetry into an accurate quantitative measure that will allow to answer questions such as: "What is the degree of symmetry deviation of the protein?," "how do these deviations compare within a family of proteins?," and so on. We developed quantitative methods to answer this type of questions, which are capable of analyzing the whole protein, its backbone or selected portions of it, down to comparison of symmetry-related specific amino-acids, and which are capable of visualizing the various levels of symmetry deviations in the form of symmetry maps. We have applied these methods on an extensive list of homomers and heteromers and found that apparently all proteins never reach perfect symmetry. Strikingly, even homomeric protein clusters are never ideally symmetric. We also found that the main burden of symmetry distortion is on the amino-acids near the symmetry axis; that it is mainly the more hydrophilic amino-acids that take place in symmetry-distortive interactions; and more. The remarkable ability of heteromers to preserve near-symmetry, despite the different sequences, was also shown and analyzed. The comprehensive literature on the suggested advantages symmetric oligomerizations raises a yet-unsolved key question: If symmetry is so advantageous, why do proteins stop shy of perfect symmetry? Some tentative answers to be tested in further studies are suggested in a concluding outlook.
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Affiliation(s)
- Maayan Bonjack-Shterengartz
- Institute of Chemistry and the Lise Meitner Minerva Center for Computational Quantum Chemistry, the Hebrew University of Jerusalem, Jerusalem, 91904, Israel
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11
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Villoutreix BO, Kuenemann MA, Poyet JL, Bruzzoni-Giovanelli H, Labbé C, Lagorce D, Sperandio O, Miteva MA. Drug-Like Protein-Protein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology. Mol Inform 2014; 33:414-437. [PMID: 25254076 PMCID: PMC4160817 DOI: 10.1002/minf.201400040] [Citation(s) in RCA: 84] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2014] [Accepted: 04/21/2014] [Indexed: 12/13/2022]
Abstract
[Formula: see text] Fundamental processes in living cells are largely controlled by macromolecular interactions and among them, protein-protein interactions (PPIs) have a critical role while their dysregulations can contribute to the pathogenesis of numerous diseases. Although PPIs were considered as attractive pharmaceutical targets already some years ago, they have been thus far largely unexploited for therapeutic interventions with low molecular weight compounds. Several limiting factors, from technological hurdles to conceptual barriers, are known, which, taken together, explain why research in this area has been relatively slow. However, this last decade, the scientific community has challenged the dogma and became more enthusiastic about the modulation of PPIs with small drug-like molecules. In fact, several success stories were reported both, at the preclinical and clinical stages. In this review article, written for the 2014 International Summer School in Chemoinformatics (Strasbourg, France), we discuss in silico tools (essentially post 2012) and databases that can assist the design of low molecular weight PPI modulators (these tools can be found at www.vls3d.com). We first introduce the field of protein-protein interaction research, discuss key challenges and comment recently reported in silico packages, protocols and databases dedicated to PPIs. Then, we illustrate how in silico methods can be used and combined with experimental work to identify PPI modulators.
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Affiliation(s)
- Bruno O Villoutreix
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 InsermParis 75013, France
- Inserm, U973Paris 75013, France
- CDithem, Faculté de Pharmacie, 1 rue du Prof Laguesse59000 Lille, France
| | - Melaine A Kuenemann
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 InsermParis 75013, France
- Inserm, U973Paris 75013, France
| | - Jean-Luc Poyet
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 InsermParis 75013, France
- Inserm, U973Paris 75013, France
- IUH, Hôpital Saint-LouisParis, France
- CDithem, Faculté de Pharmacie, 1 rue du Prof Laguesse59000 Lille, France
| | - Heriberto Bruzzoni-Giovanelli
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 InsermParis 75013, France
- Inserm, U973Paris 75013, France
- CIC, Clinical investigation center, Hôpital Saint-LouisParis, France
| | - Céline Labbé
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 InsermParis 75013, France
- Inserm, U973Paris 75013, France
| | - David Lagorce
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 InsermParis 75013, France
- Inserm, U973Paris 75013, France
| | - Olivier Sperandio
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 InsermParis 75013, France
- Inserm, U973Paris 75013, France
- CDithem, Faculté de Pharmacie, 1 rue du Prof Laguesse59000 Lille, France
| | - Maria A Miteva
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 InsermParis 75013, France
- Inserm, U973Paris 75013, France
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12
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Popov P, Grudinin S. Rapid determination of RMSDs corresponding to macromolecular rigid body motions. J Comput Chem 2014; 35:950-6. [PMID: 24615729 DOI: 10.1002/jcc.23569] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2013] [Revised: 01/31/2014] [Accepted: 02/04/2014] [Indexed: 11/08/2022]
Abstract
Finding the root mean sum of squared deviations (RMSDs) between two coordinate vectors that correspond to the rigid body motion of a macromolecule is an important problem in structural bioinformatics, computational chemistry, and molecular modeling. Standard algorithms compute the RMSD with time proportional to the number of atoms in the molecule. Here, we present RigidRMSD, a new algorithm that determines a set of RMSDs corresponding to a set of rigid body motions of a macromolecule in constant time with respect to the number of atoms in the molecule. Our algorithm is particularly useful for rigid body modeling applications, such as rigid body docking, and also for high-throughput analysis of rigid body modeling and simulation results. We also introduce a constant-time rotation RMSD as a similarity measure for rigid molecules. A C++ implementation of our algorithm is available at http://nano-d.inrialpes.fr/software/RigidRMSD.
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Affiliation(s)
- Petr Popov
- NANO-D, INRIA Grenoble-Rhone-Alpes, 38334 Saint Ismier Cedex, Montbonnot, France; Laboratoire Jean Kuntzmann, B.P. 53, 38041 Grenoble Cedex 9, France
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