1
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Graef J, Ehrt C, Reim T, Rarey M. Database-Driven Identification of Structurally Similar Protein-Protein Interfaces. J Chem Inf Model 2024; 64:3332-3349. [PMID: 38470439 DOI: 10.1021/acs.jcim.3c01462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Analyzing the similarity of protein interfaces in protein-protein interactions gives new insights into protein function and assists in discovering new drugs. Usually, tools that assess the similarity focus on the interactions between two protein interfaces, while sometimes we only have one predicted interface. Herein, we present PiMine, a database-driven protein interface similarity search. It compares interface residues of one or two interacting chains by calculating and searching tetrahedral geometric patterns of α-carbon atoms and calculating physicochemical and shape-based similarity. On a dedicated, tailor-made dataset, we show that PiMine outperforms commonly used comparison tools in terms of early enrichment when considering interfaces of sequentially and structurally unrelated proteins. In an application example, we demonstrate its usability for protein interaction partner prediction by comparing predicted interfaces to known protein-protein interfaces.
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Affiliation(s)
- Joel Graef
- Universität Hamburg, ZBH─Center for Bioinformatics , Albert-Einstein-Ring 8-10, 22761 Hamburg, Germany
| | - Christiane Ehrt
- Universität Hamburg, ZBH─Center for Bioinformatics , Albert-Einstein-Ring 8-10, 22761 Hamburg, Germany
| | - Thorben Reim
- Universität Hamburg, ZBH─Center for Bioinformatics , Albert-Einstein-Ring 8-10, 22761 Hamburg, Germany
| | - Matthias Rarey
- Universität Hamburg, ZBH─Center for Bioinformatics , Albert-Einstein-Ring 8-10, 22761 Hamburg, Germany
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2
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Shin WH, Kumazawa K, Imai K, Hirokawa T, Kihara D. Quantitative comparison of protein-protein interaction interface using physicochemical feature-based descriptors of surface patches. Front Mol Biosci 2023; 10:1110567. [PMID: 36814641 PMCID: PMC9939524 DOI: 10.3389/fmolb.2023.1110567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 01/24/2023] [Indexed: 02/09/2023] Open
Abstract
Driving mechanisms of many biological functions in a cell include physical interactions of proteins. As protein-protein interactions (PPIs) are also important in disease development, protein-protein interactions are highlighted in the pharmaceutical industry as possible therapeutic targets in recent years. To understand the variety of protein-protein interactions in a proteome, it is essential to establish a method that can identify similarity and dissimilarity between protein-protein interactions for inferring the binding of similar molecules, including drugs and other proteins. In this study, we developed a novel method, protein-protein interaction-Surfer, which compares and quantifies similarity of local surface regions of protein-protein interactions. protein-protein interaction-Surfer represents a protein-protein interaction surface with overlapping surface patches, each of which is described with a three-dimensional Zernike descriptor (3DZD), a compact mathematical representation of 3D function. 3DZD captures both the 3D shape and physicochemical properties of the protein surface. The performance of protein-protein interaction-Surfer was benchmarked on datasets of protein-protein interactions, where we were able to show that protein-protein interaction-Surfer finds similar potential drug binding regions that do not share sequence and structure similarity. protein-protein interaction-Surfer is available at https://kiharalab.org/ppi-surfer.
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Affiliation(s)
- Woong-Hee Shin
- Department of Chemistry Education, Sunchon National University, Suncheon, South Korea,Department of Advanced Components and Materials Engineering, Sunchon National University, Suncheon, South Korea
| | - Keiko Kumazawa
- Pharmaceutical Discovery Research Laboratories, Teijin Pharma Limited, Tokyo, Japan
| | - Kenichiro Imai
- Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan
| | - Takatsugu Hirokawa
- Division of Biomedical Science, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan,Transborder Medical Research Center, University of Tsukuba, Tsukuba, Japan
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN, United States,Department of Computer Science, Purdue University, West Lafayette, IN, United States,Center for Cancer Research, Purdue University, West Lafayette, IN, United States,*Correspondence: Daisuke Kihara,
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3
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Xu X, Xiaoqin Zou. Predicting Protein-Peptide Complex Structures by Accounting for Peptide Flexibility and the Physicochemical Environment. J Chem Inf Model 2022; 62:27-39. [PMID: 34931833 PMCID: PMC9020583 DOI: 10.1021/acs.jcim.1c00836] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Predicting protein-peptide complex structures is crucial to the understanding of a vast variety of peptide-mediated cellular processes and to peptide-based drug development. Peptide flexibility and binding mode ranking are the two major challenges for protein-peptide complex structure prediction. Peptides are highly flexible molecules, and therefore, brute-force modeling of peptide conformations of interest in protein-peptide docking is beyond current computing power. Inspired by the fact that the protein-peptide binding process is like protein folding, we developed a novel strategy, named MDockPeP2, which tries to address these challenges using physicochemical information embedded in abundant monomeric proteins with an exhaustive search strategy, in combination with an integrated global search and a local flexible minimization method. Only the peptide sequence and the protein crystal structure are required. The method was systemically assessed using a newly constructed structural database of 89 nonredundant protein-peptide complexes with the peptide sequence length ranging from 5 to 29 in which about half of the peptides are longer than 15 residues. MDockPeP2 yielded a total success rate of 58.4% (70.8, 79.8%) for the bound docking (i.e., with the bound receptor and fully flexible peptides) and 19.0% (44.8, 70.7%) for the challenging unbound docking when top 10 (100, 1000) models were considered for each prediction. MDockPeP2 achieved significantly higher success rates on two other datasets, peptiDB and LEADS-PEP, which contain only short- and medium-size peptides (≤ 15 residues). For peptiDB, our method obtained a success rate of 62.0% for the bound docking and 35.9% for the unbound docking when the top 10 models were considered. For LEADS-PEP, MDockPeP2 achieved a success rate of 69.8% when the top 10 models were considered. The program is available at https://zougrouptoolkit.missouri.edu/mdockpep2/download.html.
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4
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Prabakaran R, Rawat P, Yasuo N, Sekijima M, Kumar S, Gromiha MM. Effect of charged mutation on aggregation of a pentapeptide: Insights from molecular dynamics simulations. Proteins 2021; 90:405-417. [PMID: 34460128 DOI: 10.1002/prot.26230] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 06/30/2021] [Accepted: 08/24/2021] [Indexed: 12/14/2022]
Abstract
Aggregation of therapeutic monoclonal antibodies (mAbs) can negatively affect their chemistry, manufacturing, and control attributes and lead to undesirable immune responses in patients. Therefore, optimization of lead mAb drug candidates during discovery stages to mitigate aggregation is increasingly becoming an integral part of their developability assessments. The disruption of short sequence motifs called aggregation prone regions (APRs) found in amino acid sequences of mAb candidates can potentially mitigate their aggregation. In this work, we have performed molecular dynamics simulations to study the aggregation of an APR (VLVIY) found in λ light chains of human antibodies and its single point mutant KLVIY. Eighteen different multicopy peptide simulation systems of "VLVIY" and "KLVIY" were constructed by varying their concentrations, temperatures, termini capping, and flanking gate-keeper regions. Within 20 ns of the simulation, peptide "VLVIY" formed an aggregate of 100 peptides at ~0.1 M concentration with a 60% reduction in solvent accessible surface area (SASA). Furthermore, analysis of the SASA change, peptide cluster distribution, and water residence time demonstrated how Val ➔ Lys mutation resists aggregation and improves solubility. Presence of Lys slows down aggregation kinetics via charge-charge repulsions and by raising the kinetic barrier to formation of large oligomers. However, the effect of the Val ➔ Lys mutation is dependent on sequence and structural contexts around the APR. This mutation also alters the solvation shell around the peptide by favoring solute-solvent interactions, thereby increasing its solubility. This work has provided a detailed mechanistic explanation of how APR disruption can mitigate aggregation in biotherapeutics and improve their developability.
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Affiliation(s)
- R Prabakaran
- Protein Bioinformatics Laboratory, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, India
| | - Puneet Rawat
- Protein Bioinformatics Laboratory, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, India
| | - Nobuaki Yasuo
- Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan
| | - Masakazu Sekijima
- Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan
| | - Sandeep Kumar
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceutical Inc., Ridgefield, Connecticut, USA
| | - M Michael Gromiha
- Protein Bioinformatics Laboratory, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, India.,Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan
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5
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Rey J, Rasolohery I, Tufféry P, Guyon F, Moroy G. PatchSearch: a web server for off-target protein identification. Nucleic Acids Res 2020; 47:W365-W372. [PMID: 31131411 PMCID: PMC6602448 DOI: 10.1093/nar/gkz478] [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: 02/22/2019] [Revised: 04/26/2019] [Accepted: 05/21/2019] [Indexed: 01/17/2023] Open
Abstract
The large number of proteins found in the human body implies that a drug may interact with many proteins, called off-target proteins, besides its intended target. The PatchSearch web server provides an automated workflow that allows users to identify structurally conserved binding sites at the protein surfaces in a set of user-supplied protein structures. Thus, this web server may help to detect potential off-target protein. It takes as input a protein complexed with a ligand and identifies within user-defined or predefined collections of protein structures, those having a binding site compatible with this ligand in terms of geometry and physicochemical properties. It is based on a non-sequential local alignment of the patch over the entire protein surface. Then the PatchSearch web server proposes a ligand binding mode for the potential off-target, as well as an estimated affinity calculated by the Vinardo scoring function. This novel tool is able to efficiently detects potential interactions of ligands with distant off-target proteins. Furthermore, by facilitating the discovery of unexpected off-targets, PatchSearch could contribute to the repurposing of existing drugs. The server is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/services/PatchSearch.
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Affiliation(s)
- Julien Rey
- Université Paris Diderot, Sorbonne Paris Cité, INSERM UMRS-973, Molécules Thérapeutiques in silico (MTi), F-75205 Paris, France.,Ressource Parisienne en Bioinformatique Structurale (RPBS), Paris, France
| | - Inès Rasolohery
- Université Paris Diderot, Sorbonne Paris Cité, INSERM UMRS-973, Molécules Thérapeutiques in silico (MTi), F-75205 Paris, France
| | - Pierre Tufféry
- Université Paris Diderot, Sorbonne Paris Cité, INSERM UMRS-973, Molécules Thérapeutiques in silico (MTi), F-75205 Paris, France.,Ressource Parisienne en Bioinformatique Structurale (RPBS), Paris, France
| | - Frédéric Guyon
- Université Paris Diderot, Sorbonne Paris Cité, INSERM UMRS-973, Molécules Thérapeutiques in silico (MTi), F-75205 Paris, France
| | - Gautier Moroy
- Université Paris Diderot, Sorbonne Paris Cité, INSERM UMRS-973, Molécules Thérapeutiques in silico (MTi), F-75205 Paris, France
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6
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Rasolohery I, Moroy G, Guyon F. PatchSearch: A Fast Computational Method for Off-Target Detection. J Chem Inf Model 2017; 57:769-777. [PMID: 28282119 DOI: 10.1021/acs.jcim.6b00529] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Many therapeutic molecules are known to bind several proteins, which can be different from the initially targeted one. Such unexpected interactions with proteins called off-targets can lead to adverse effects. Potential off-target identification is important to predict to avoid drug side effects or to discover new targets for existing drugs. We propose a new program named PatchSearch that implements local nonsequential searching for similar binding sites on protein surfaces with a controlled amount of flexibility. It is based on detection of quasi-cliques in product graphs representing all the possible matchings between two compared structures. This method has been benchmarked on a large diversity of ligands and on five data sets ranging from 12 to more than 7000 protein structures. The experiments conducted in this study show that the PatchSearch method could be useful in the early identification of off-targets. The program and the benchmarks presented in this paper are available as an R package at https://github.com/MTiPatchSearch .
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Affiliation(s)
- Inès Rasolohery
- Molécules Thérapeutiques in Silico, UMRS 973, Université Paris Diderot, INSERM , F-75013 Paris, France
| | - Gautier Moroy
- Molécules Thérapeutiques in Silico, UMRS 973, Université Paris Diderot, INSERM , F-75013 Paris, France
| | - Frédéric Guyon
- Molécules Thérapeutiques in Silico, UMRS 973, Université Paris Diderot, INSERM , F-75013 Paris, France
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7
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Prabakaran R, Goel D, Kumar S, Gromiha MM. Aggregation prone regions in human proteome: Insights from large-scale data analyses. Proteins 2017; 85:1099-1118. [DOI: 10.1002/prot.25276] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2016] [Revised: 02/10/2017] [Accepted: 02/24/2017] [Indexed: 12/25/2022]
Affiliation(s)
- R. Prabakaran
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences; Indian Institute of Technology Madras; Chennai 600036 India
| | - Dhruv Goel
- Department of Computer Science and Engineering; Motilal Nehru National Institute of Technology; Allahabad 211004 India
| | - Sandeep Kumar
- Biotherapeutics Pharmaceutical Sciences, Pfizer Inc; 700 Chesterfield Parkway West Chesterfield Missouri 63017, USA
| | - M. Michael Gromiha
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences; Indian Institute of Technology Madras; Chennai 600036 India
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8
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Salmon L, Ahlstrom LS, Horowitz S, Dickson A, Brooks CL, Bardwell JCA. Capturing a Dynamic Chaperone-Substrate Interaction Using NMR-Informed Molecular Modeling. J Am Chem Soc 2016; 138:9826-39. [PMID: 27415450 DOI: 10.1021/jacs.6b02382] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Chaperones maintain a healthy proteome by preventing aggregation and by aiding in protein folding. Precisely how chaperones influence the conformational properties of their substrates, however, remains unclear. To achieve a detailed description of dynamic chaperone-substrate interactions, we fused site-specific NMR information with coarse-grained simulations. Our model system is the binding and folding of a chaperone substrate, immunity protein 7 (Im7), with the chaperone Spy. We first used an automated procedure in which NMR chemical shifts inform the construction of system-specific force fields that describe each partner individually. The models of the two binding partners are then combined to perform simulations on the chaperone-substrate complex. The binding simulations show excellent agreement with experimental data from multiple biophysical measurements. Upon binding, Im7 interacts with a mixture of hydrophobic and hydrophilic residues on Spy's surface, causing conformational exchange within Im7 to slow down as Im7 folds. Meanwhile, the motion of Spy's flexible loop region increases, allowing for better interaction with different substrate conformations, and helping offset losses in Im7 conformational dynamics that occur upon binding and folding. Spy then preferentially releases Im7 into a well-folded state. Our strategy has enabled a residue-level description of a dynamic chaperone-substrate interaction, improving our understanding of how chaperones facilitate substrate folding. More broadly, we validate our approach using two other binding partners, showing that this approach provides a general platform from which to investigate other flexible biomolecular complexes through the integration of NMR data with efficient computational models.
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Affiliation(s)
- Loïc Salmon
- Department of Molecular, Cellular and Developmental Biology, and the Howard Hughes Medical Institute, University of Michigan , Ann Arbor, Michigan 48109, United States
| | - Logan S Ahlstrom
- Department of Molecular, Cellular and Developmental Biology, and the Howard Hughes Medical Institute, University of Michigan , Ann Arbor, Michigan 48109, United States.,Department of Chemistry, University of Michigan , Ann Arbor, Michigan 48109, United States
| | - Scott Horowitz
- Department of Molecular, Cellular and Developmental Biology, and the Howard Hughes Medical Institute, University of Michigan , Ann Arbor, Michigan 48109, United States
| | - Alex Dickson
- Department of Chemistry, University of Michigan , Ann Arbor, Michigan 48109, United States
| | - Charles L Brooks
- Department of Chemistry, University of Michigan , Ann Arbor, Michigan 48109, United States.,Biophysics Program, University of Michigan , Ann Arbor, Michigan 48109, United States
| | - James C A Bardwell
- Department of Molecular, Cellular and Developmental Biology, and the Howard Hughes Medical Institute, University of Michigan , Ann Arbor, Michigan 48109, United States
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9
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Barman A, Hamelberg D. Coupled Dynamics and Entropic Contribution to the Allosteric Mechanism of Pin1. J Phys Chem B 2016; 120:8405-15. [DOI: 10.1021/acs.jpcb.6b02123] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Arghya Barman
- Department
of Chemistry and
the Center for Biotechnology and Drug Design, Georgia State University, Atlanta, Georgia 30302-3965, United States
| | - Donald Hamelberg
- Department
of Chemistry and
the Center for Biotechnology and Drug Design, Georgia State University, Atlanta, Georgia 30302-3965, United States
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10
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Keskin O, Tuncbag N, Gursoy A. Predicting Protein–Protein Interactions from the Molecular to the Proteome Level. Chem Rev 2016; 116:4884-909. [DOI: 10.1021/acs.chemrev.5b00683] [Citation(s) in RCA: 207] [Impact Index Per Article: 25.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
| | - Nurcan Tuncbag
- Graduate
School of Informatics, Department of Health Informatics, Middle East Technical University, 06800 Ankara, Turkey
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11
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Lee HS, Im W. G-LoSA: An efficient computational tool for local structure-centric biological studies and drug design. Protein Sci 2016; 25:865-76. [PMID: 26813336 DOI: 10.1002/pro.2890] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2015] [Revised: 01/20/2016] [Accepted: 01/21/2016] [Indexed: 11/11/2022]
Abstract
Molecular recognition by protein mostly occurs in a local region on the protein surface. Thus, an efficient computational method for accurate characterization of protein local structural conservation is necessary to better understand biology and drug design. We present a novel local structure alignment tool, G-LoSA. G-LoSA aligns protein local structures in a sequence order independent way and provides a GA-score, a chemical feature-based and size-independent structure similarity score. Our benchmark validation shows the robust performance of G-LoSA to the local structures of diverse sizes and characteristics, demonstrating its universal applicability to local structure-centric comparative biology studies. In particular, G-LoSA is highly effective in detecting conserved local regions on the entire surface of a given protein. In addition, the applications of G-LoSA to identifying template ligands and predicting ligand and protein binding sites illustrate its strong potential for computer-aided drug design. We hope that G-LoSA can be a useful computational method for exploring interesting biological problems through large-scale comparison of protein local structures and facilitating drug discovery research and development. G-LoSA is freely available to academic users at http://im.compbio.ku.edu/GLoSA/.
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Affiliation(s)
- Hui Sun Lee
- Higuchi Biosciences Center, University of Kansas, Lawrence, Kansas, 66047
| | - Wonpil Im
- Department of Molecular Biosciences and Center for Computational Biology, University of Kansas, Lawrence, Kansas, 66047
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12
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Brender JR, Zhang Y. Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles. PLoS Comput Biol 2015; 11:e1004494. [PMID: 26506533 PMCID: PMC4624718 DOI: 10.1371/journal.pcbi.1004494] [Citation(s) in RCA: 99] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2015] [Accepted: 08/06/2015] [Indexed: 11/18/2022] Open
Abstract
The formation of protein-protein complexes is essential for proteins to perform their physiological functions in the cell. Mutations that prevent the proper formation of the correct complexes can have serious consequences for the associated cellular processes. Since experimental determination of protein-protein binding affinity remains difficult when performed on a large scale, computational methods for predicting the consequences of mutations on binding affinity are highly desirable. We show that a scoring function based on interface structure profiles collected from analogous protein-protein interactions in the PDB is a powerful predictor of protein binding affinity changes upon mutation. As a standalone feature, the differences between the interface profile score of the mutant and wild-type proteins has an accuracy equivalent to the best all-atom potentials, despite being two orders of magnitude faster once the profile has been constructed. Due to its unique sensitivity in collecting the evolutionary profiles of analogous binding interactions and the high speed of calculation, the interface profile score has additional advantages as a complementary feature to combine with physics-based potentials for improving the accuracy of composite scoring approaches. By incorporating the sequence-derived and residue-level coarse-grained potentials with the interface structure profile score, a composite model was constructed through the random forest training, which generates a Pearson correlation coefficient >0.8 between the predicted and observed binding free-energy changes upon mutation. This accuracy is comparable to, or outperforms in most cases, the current best methods, but does not require high-resolution full-atomic models of the mutant structures. The binding interface profiling approach should find useful application in human-disease mutation recognition and protein interface design studies. Few proteins carry out their tasks in isolation. Instead, proteins combine with each other in complicated ways that can be affected by either the natural genetic variation that occurs among people or by disease causing mutations such as those that occur in cancer or in genetic disorders. To understand how these mutations affect our health, it is necessary to understand how mutations can affect the strength of the interactions that bind proteins together. This is a difficult task to do in a laboratory on a large scale and scientists are increasingly turning to computational methods to predict these effects in advance. We show that by looking at the multiple alignments of similar protein-protein complex structures at the interface regions, new constraints based on the evolution of the three dimensional structures of proteins can be made to predict which mutations are compatible with two proteins interacting and which are not.
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Affiliation(s)
- Jeffrey R. Brender
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Biological Chemistry, University of Michigan, Ann Arbor, Michigan, United States of America
- * E-mail:
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13
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Cheng S, Brooks CL. Protein-Protein Interfaces in Viral Capsids Are Structurally Unique. J Mol Biol 2015; 427:3613-3624. [PMID: 26375252 DOI: 10.1016/j.jmb.2015.09.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2015] [Revised: 08/08/2015] [Accepted: 09/04/2015] [Indexed: 01/20/2023]
Abstract
Viral capsids exhibit elaborate and symmetrical architectures of defined sizes and remarkable mechanical properties not seen with cellular macromolecular complexes. Given the uniqueness of the higher-order organization of viral capsid proteins in the virosphere, we explored the question of whether the patterns of protein-protein interactions within viral capsids are distinct from those in generic protein complexes. Our comparative analysis involving a non-redundant set of 551 inter-subunit interfaces in viral capsids from VIPERdb and 20,014 protein-protein interfaces in non-capsid protein complexes from the Protein Data Bank found 418 generic protein-protein interfaces that share similar physicochemical patterns with some protein-protein interfaces in the capsid set, using the program PCalign we developed for comparing protein-protein interfaces. This overlap in the structural space of protein-protein interfaces is significantly small, with a p-value <0.0001, based on a permutation test on the total set of protein-protein interfaces. Furthermore, the generic protein-protein interfaces that bear similarity in their spatial and chemical arrangement with capsid ones are mostly small in size with fewer than 20 interfacial residues, which results from the relatively limited choices of natural design for small interfaces rather than having significant biological implications in terms of functional relationships. We conclude based on this study that protein-protein interfaces in viral capsids are non-representative of patterns in the smaller, more compact cellular protein complexes. Our finding highlights the design principle of building large biological containers from repeated, self-assembling units and provides insights into specific targets for antiviral drug design for improved efficacy.
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Affiliation(s)
- Shanshan Cheng
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109-2218, USA.
| | - Charles L Brooks
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109-2218, USA; Department of Chemistry, University of Michigan, Ann Arbor, MI 48109-1055, USA; Biophysics Program, University of Michigan, Ann Arbor, MI 48109-1055, USA.
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