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Pozzati G, Kundrotas P, Elofsson A. Scoring of protein–protein docking models utilizing predicted interface residues. Proteins 2022; 90:1493-1505. [PMID: 35246997 PMCID: PMC9314140 DOI: 10.1002/prot.26330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 02/23/2022] [Accepted: 02/28/2022] [Indexed: 11/08/2022]
Abstract
Scoring docking solutions is a difficult task, and many methods have been developed for this purpose. In docking, only a handful of the hundreds of thousands of models generated by docking algorithms are acceptable, causing difficulties when developing scoring functions. Today's best scoring functions can significantly increase the number of top‐ranked models but still fail for most targets. Here, we examine the possibility of utilizing predicted interface residues to score docking models generated during the scan stage of a docking algorithm. Many methods have been developed to infer the regions of a protein surface that interact with another protein, but most have not been benchmarked using docking algorithms. This study systematically tests different interface prediction methods for scoring >300.000 low‐resolution rigid‐body template free docking decoys. Overall we find that contact‐based interface prediction by BIPSPI is the best method to score docking solutions, with >12% of first ranked docking models being acceptable. Additional experiments indicated precision as a high‐importance metric when estimating interface prediction quality, focusing on docking constraints production. Finally, we discussed several limitations for adopting interface predictions as constraints in a docking protocol.
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Affiliation(s)
- Gabriele Pozzati
- Department of Biochemistry and Biophysics and Science for Life Laboratory Stockholm University Solna Sweden
| | - Petras Kundrotas
- Department of Biochemistry and Biophysics and Science for Life Laboratory Stockholm University Solna Sweden
- Center for Bioinformatics and Department of Molecular Biosciences University of Kansas Lawrence Kansas USA
| | - Arne Elofsson
- Department of Biochemistry and Biophysics and Science for Life Laboratory Stockholm University Solna Sweden
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2
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Enhancement of SARS-CoV-2 Receptor Binding Domain -CR3022 Human Antibody Binding Affinity via In silico Engineering Approach. JOURNAL OF MEDICAL MICROBIOLOGY AND INFECTIOUS DISEASES 2021. [DOI: 10.52547/jommid.9.3.156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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3
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Zhang J, Ghadermarzi S, Kurgan L. Prediction of protein-binding residues: dichotomy of sequence-based methods developed using structured complexes versus disordered proteins. Bioinformatics 2021; 36:4729-4738. [PMID: 32860044 DOI: 10.1093/bioinformatics/btaa573] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Revised: 05/22/2020] [Accepted: 06/10/2020] [Indexed: 01/08/2023] Open
Abstract
MOTIVATION There are over 30 sequence-based predictors of the protein-binding residues (PBRs). They use either structure-annotated or disorder-annotated training datasets, potentially creating a dichotomy where the structure-/disorder-specific models may not be able to cross-over to accurately predict the other type. Moreover, the structure-trained predictors were shown to substantially cross-predict PBRs among residues that interact with non-protein partners (nucleic acids and small ligands). We address these issues by performing first-of-its-kind comparative study of a representative collection of disorder- and structure-trained predictors using a comprehensive benchmark set with the structure- and disorder-derived annotations of PBRs (to analyze the cross-over) and the protein-, nucleic acid- and small ligand-binding proteins (to study the cross-predictions). RESULTS Three predictors provide accurate results: SCRIBER, ANCHOR and disoRDPbind. Some of the structure-trained methods make accurate predictions on the structure-annotated proteins. Similarly, the disorder-trained predictors predict well on the disorder-annotated proteins. However, the considered predictors generally fail to cross-over, with the exception of SCRIBER. Our study also reveals that virtually all methods substantially cross-predict PBRs, except for SCRIBER for the structure-annotated proteins and disoRDPbind for the disorder-annotated proteins. We formulate a novel hybrid predictor, hybridPBRpred, that combines results produced by disoRDPbind and SCRIBER to accurately predict disorder- and structure-annotated PBRs. HybridPBRpred generates accurate results that cross-over structure- and disorder-annotated proteins and produces relatively low amount of cross-predictions, offering an accurate alternative to predict PBRs. AVAILABILITY AND IMPLEMENTATION HybridPBRpred webserver, benchmark dataset and supplementary information are available at http://biomine.cs.vcu.edu/servers/hybridPBRpred/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jian Zhang
- School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China
| | - Sina Ghadermarzi
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
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4
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Zhao B, Katuwawala A, Oldfield CJ, Dunker AK, Faraggi E, Gsponer J, Kloczkowski A, Malhis N, Mirdita M, Obradovic Z, Söding J, Steinegger M, Zhou Y, Kurgan L. DescribePROT: database of amino acid-level protein structure and function predictions. Nucleic Acids Res 2021; 49:D298-D308. [PMID: 33119734 PMCID: PMC7778963 DOI: 10.1093/nar/gkaa931] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 09/11/2020] [Accepted: 10/05/2020] [Indexed: 12/30/2022] Open
Abstract
We present DescribePROT, the database of predicted amino acid-level descriptors of structure and function of proteins. DescribePROT delivers a comprehensive collection of 13 complementary descriptors predicted using 10 popular and accurate algorithms for 83 complete proteomes that cover key model organisms. The current version includes 7.8 billion predictions for close to 600 million amino acids in 1.4 million proteins. The descriptors encompass sequence conservation, position specific scoring matrix, secondary structure, solvent accessibility, intrinsic disorder, disordered linkers, signal peptides, MoRFs and interactions with proteins, DNA and RNAs. Users can search DescribePROT by the amino acid sequence and the UniProt accession number and entry name. The pre-computed results are made available instantaneously. The predictions can be accesses via an interactive graphical interface that allows simultaneous analysis of multiple descriptors and can be also downloaded in structured formats at the protein, proteome and whole database scale. The putative annotations included by DescriPROT are useful for a broad range of studies, including: investigations of protein function, applied projects focusing on therapeutics and diseases, and in the development of predictors for other protein sequence descriptors. Future releases will expand the coverage of DescribePROT. DescribePROT can be accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/.
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Affiliation(s)
- Bi Zhao
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - Akila Katuwawala
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | | | - A Keith Dunker
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Eshel Faraggi
- Battelle Center for Mathematical Medicine at the Nationwide Children's Hospital, and Department of Pediatrics, The Ohio State University, Columbus, OH, USA
| | - Jörg Gsponer
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
| | - Andrzej Kloczkowski
- Battelle Center for Mathematical Medicine at the Nationwide Children's Hospital, and Department of Pediatrics, The Ohio State University, Columbus, OH, USA
| | - Nawar Malhis
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
| | - Milot Mirdita
- Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Zoran Obradovic
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA
| | - Johannes Söding
- Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Martin Steinegger
- School of Biological Sciences and Institute of Molecular Biology & Genetics, Seoul National University, Seoul, Republic of Korea
| | - Yaoqi Zhou
- Institute for Glycomics, Griffith University, Gold Coast, Queensland, Australia
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
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5
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Geng C, Xue LC, Roel‐Touris J, Bonvin AMJJ. Finding the ΔΔ
G
spot: Are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it? WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2019. [DOI: 10.1002/wcms.1410] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Cunliang Geng
- Bijvoet Center for Biomolecular Research, Faculty of Science—Chemistry Utrecht University Utrecht The Netherlands
| | - Li C. Xue
- Bijvoet Center for Biomolecular Research, Faculty of Science—Chemistry Utrecht University Utrecht The Netherlands
| | - Jorge Roel‐Touris
- Bijvoet Center for Biomolecular Research, Faculty of Science—Chemistry Utrecht University Utrecht The Netherlands
| | - Alexandre M. J. J. Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science—Chemistry Utrecht University Utrecht The Netherlands
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6
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Lazar T, Guharoy M, Schad E, Tompa P. Unique Physicochemical Patterns of Residues in Protein–Protein Interfaces. J Chem Inf Model 2018; 58:2164-2173. [DOI: 10.1021/acs.jcim.8b00270] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Affiliation(s)
- Tamas Lazar
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnologie, Pleinlaan 2, 1050 Brussels, Belgium
- Structural Biology Brussels, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
| | - Mainak Guharoy
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnologie, Pleinlaan 2, 1050 Brussels, Belgium
- Structural Biology Brussels, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
| | - Eva Schad
- Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudosok korutja 2, 1117 Budapest, Hungary
| | - Peter Tompa
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnologie, Pleinlaan 2, 1050 Brussels, Belgium
- Structural Biology Brussels, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
- Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudosok korutja 2, 1117 Budapest, Hungary
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7
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Rosell M, Fernández-Recio J. Hot-spot analysis for drug discovery targeting protein-protein interactions. Expert Opin Drug Discov 2018; 13:327-338. [PMID: 29376444 DOI: 10.1080/17460441.2018.1430763] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
INTRODUCTION Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.
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Affiliation(s)
- Mireia Rosell
- a Department of Life Sciences , Barcelona Supercomputing Center (BSC) , Barcelona , Spain
| | - Juan Fernández-Recio
- a Department of Life Sciences , Barcelona Supercomputing Center (BSC) , Barcelona , Spain.,b Structural Biology Unit , Institut de Biologia Molecular de Barcelona (IBMB), CSIC , Barcelona , Spain
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8
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Protein binding hot spots prediction from sequence only by a new ensemble learning method. Amino Acids 2017; 49:1773-1785. [DOI: 10.1007/s00726-017-2474-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2016] [Accepted: 07/24/2017] [Indexed: 01/31/2023]
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9
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Zhang J, Kurgan L. Review and comparative assessment of sequence-based predictors of protein-binding residues. Brief Bioinform 2017; 19:821-837. [DOI: 10.1093/bib/bbx022] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Indexed: 12/31/2022] Open
Affiliation(s)
- Jian Zhang
- School of Computer and Information Technology, Xinyang Normal University
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
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10
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Vamparys L, Laurent B, Carbone A, Sacquin-Mora S. Great interactions: How binding incorrect partners can teach us about protein recognition and function. Proteins 2016; 84:1408-21. [PMID: 27287388 PMCID: PMC5516155 DOI: 10.1002/prot.25086] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2016] [Revised: 06/01/2016] [Accepted: 06/02/2016] [Indexed: 12/29/2022]
Abstract
Protein–protein interactions play a key part in most biological processes and understanding their mechanism is a fundamental problem leading to numerous practical applications. The prediction of protein binding sites in particular is of paramount importance since proteins now represent a major class of therapeutic targets. Amongst others methods, docking simulations between two proteins known to interact can be a useful tool for the prediction of likely binding patches on a protein surface. From the analysis of the protein interfaces generated by a massive cross‐docking experiment using the 168 proteins of the Docking Benchmark 2.0, where all possible protein pairs, and not only experimental ones, have been docked together, we show that it is also possible to predict a protein's binding residues without having any prior knowledge regarding its potential interaction partners. Evaluating the performance of cross‐docking predictions using the area under the specificity‐sensitivity ROC curve (AUC) leads to an AUC value of 0.77 for the complete benchmark (compared to the 0.5 AUC value obtained for random predictions). Furthermore, a new clustering analysis performed on the binding patches that are scattered on the protein surface show that their distribution and growth will depend on the protein's functional group. Finally, in several cases, the binding‐site predictions resulting from the cross‐docking simulations will lead to the identification of an alternate interface, which corresponds to the interaction with a biomolecular partner that is not included in the original benchmark. Proteins 2016; 84:1408–1421. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.
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Affiliation(s)
- Lydie Vamparys
- Laboratoire De Biochimie Théorique, CNRS UPR 9080, Institut De Biologie Physico-Chimique, 13 Rue Pierre Et Marie Curie, Paris, 75005, France
| | - Benoist Laurent
- Laboratoire De Biochimie Théorique, CNRS UPR 9080, Institut De Biologie Physico-Chimique, 13 Rue Pierre Et Marie Curie, Paris, 75005, France
| | - Alessandra Carbone
- Sorbonne Universités, UPMC Univ-Paris 6, CNRS UMR7238, Laboratoire De Biologie Computationnelle Et Quantitative, 15 Rue De L'Ecole De Médecine, Paris, 75006, France.,Institut Universitaire De France, Paris, 75005, France
| | - Sophie Sacquin-Mora
- Laboratoire De Biochimie Théorique, CNRS UPR 9080, Institut De Biologie Physico-Chimique, 13 Rue Pierre Et Marie Curie, Paris, 75005, France.
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11
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Hu G, Xiao F, Li Y, Li Y, Vongsangnak W. Protein-Protein Interface and Disease: Perspective from Biomolecular Networks. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2016; 160:57-74. [PMID: 27928579 DOI: 10.1007/10_2016_40] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Protein-protein interactions are involved in many important biological processes and molecular mechanisms of disease association. Structural studies of interfacial residues in protein complexes provide information on protein-protein interactions. Characterizing protein-protein interfaces, including binding sites and allosteric changes, thus pose an imminent challenge. With special focus on protein complexes, approaches based on network theory are proposed to meet this challenge. In this review we pay attention to protein-protein interfaces from the perspective of biomolecular networks and their roles in disease. We first describe the different roles of protein complexes in disease through several structural aspects of interfaces. We then discuss some recent advances in predicting hot spots and communication pathway analysis in terms of amino acid networks. Finally, we highlight possible future aspects of this area with respect to both methodology development and applications for disease treatment.
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Affiliation(s)
- Guang Hu
- Center for Systems Biology, School of Electronic and Information Engineering, Soochow University, Suzhou, 215006, China.
| | - Fei Xiao
- School of Basic Medicine and Biological Sciences, Medical College of Soochow University, Suzhou, 215123, China
| | - Yuqian Li
- School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Yuan Li
- Center for Systems Biology, School of Electronic and Information Engineering, Soochow University, Suzhou, 215006, China
| | - Wanwipa Vongsangnak
- Department of Zoology, Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand.
- Computational Biomodelling Laboratory for Agricultural Science and Technology (CBLAST), Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand.
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12
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Esmaielbeiki R, Krawczyk K, Knapp B, Nebel JC, Deane CM. Progress and challenges in predicting protein interfaces. Brief Bioinform 2016; 17:117-31. [PMID: 25971595 PMCID: PMC4719070 DOI: 10.1093/bib/bbv027] [Citation(s) in RCA: 100] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Revised: 03/18/2015] [Indexed: 12/31/2022] Open
Abstract
The majority of biological processes are mediated via protein-protein interactions. Determination of residues participating in such interactions improves our understanding of molecular mechanisms and facilitates the development of therapeutics. Experimental approaches to identifying interacting residues, such as mutagenesis, are costly and time-consuming and thus, computational methods for this purpose could streamline conventional pipelines. Here we review the field of computational protein interface prediction. We make a distinction between methods which address proteins in general and those targeted at antibodies, owing to the radically different binding mechanism of antibodies. We organize the multitude of currently available methods hierarchically based on required input and prediction principles to provide an overview of the field.
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13
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Sgrignani J, Olsson S, Ekonomiuk D, Genini D, Krause R, Catapano CV, Cavalli A. Molecular Determinants for Unphosphorylated STAT3 Dimerization Determined by Integrative Modeling. Biochemistry 2015; 54:5489-501. [DOI: 10.1021/bi501529x] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Jacopo Sgrignani
- Institute of Research in Biomedicine (IRB) and Universitá della Svizzera italiana (USI), Via Vincenzo
Vela 6, CH-6500 Bellinzona, Switzerland
| | - Simon Olsson
- Institute of Research in Biomedicine (IRB) and Universitá della Svizzera italiana (USI), Via Vincenzo
Vela 6, CH-6500 Bellinzona, Switzerland
- Laboratorium
für Physikalische Chemie, Eidgenössische Technische Hochschule Zürich, Vladimir-Prelog-Weg 2, CH-8093 Zürich, Switzerland
| | - Dariusz Ekonomiuk
- Institute of Research in Biomedicine (IRB) and Universitá della Svizzera italiana (USI), Via Vincenzo
Vela 6, CH-6500 Bellinzona, Switzerland
| | - Davide Genini
- IOR Institute of Oncology Research, Via Vincenzo Vela 6, CH-6500 Bellinzona, Switzerland
| | - Rolf Krause
- Institute
of Computational Science, Faculty of Informatics, Universitá della Svizzera Italiana (USI), Via Giuseppe Buffi 13, CH-6900 Lugano, Switzerland
| | - Carlo V. Catapano
- IOR Institute of Oncology Research, Via Vincenzo Vela 6, CH-6500 Bellinzona, Switzerland
| | - Andrea Cavalli
- Institute of Research in Biomedicine (IRB) and Universitá della Svizzera italiana (USI), Via Vincenzo
Vela 6, CH-6500 Bellinzona, Switzerland
- Department
of Chemistry, University of Cambridge, Lansfield Road, Cambridge CB2 1EW, U.K
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14
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Laraia L, McKenzie G, Spring DR, Venkitaraman AR, Huggins DJ. Overcoming Chemical, Biological, and Computational Challenges in the Development of Inhibitors Targeting Protein-Protein Interactions. CHEMISTRY & BIOLOGY 2015; 22:689-703. [PMID: 26091166 PMCID: PMC4518475 DOI: 10.1016/j.chembiol.2015.04.019] [Citation(s) in RCA: 113] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2014] [Revised: 04/01/2015] [Accepted: 04/08/2015] [Indexed: 01/19/2023]
Abstract
Protein-protein interactions (PPIs) underlie the majority of biological processes, signaling, and disease. Approaches to modulate PPIs with small molecules have therefore attracted increasing interest over the past decade. However, there are a number of challenges inherent in developing small-molecule PPI inhibitors that have prevented these approaches from reaching their full potential. From target validation to small-molecule screening and lead optimization, identifying therapeutically relevant PPIs that can be successfully modulated by small molecules is not a simple task. Following the recent review by Arkin et al., which summarized the lessons learnt from prior successes, we focus in this article on the specific challenges of developing PPI inhibitors and detail the recent advances in chemistry, biology, and computation that facilitate overcoming them. We conclude by providing a perspective on the field and outlining four innovations that we see as key enabling steps for successful development of small-molecule inhibitors targeting PPIs.
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Affiliation(s)
- Luca Laraia
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK; Medical Research Council Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Hills Road, Cambridge CB2 0XZ, UK
| | - Grahame McKenzie
- Medical Research Council Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Hills Road, Cambridge CB2 0XZ, UK
| | - David R Spring
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
| | - Ashok R Venkitaraman
- Medical Research Council Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Hills Road, Cambridge CB2 0XZ, UK
| | - David J Huggins
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK; Medical Research Council Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Hills Road, Cambridge CB2 0XZ, UK; Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, 19 JJ Thomson Avenue, Cambridge CB3 0HE, UK.
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15
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A structure-based classification and analysis of protein domain family binding sites and their interactions. BIOLOGY 2015; 4:327-43. [PMID: 25860777 PMCID: PMC4498303 DOI: 10.3390/biology4020327] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2015] [Revised: 03/24/2015] [Accepted: 03/31/2015] [Indexed: 11/29/2022]
Abstract
While the number of solved 3D protein structures continues to grow rapidly, the structural rules that distinguish protein-protein interactions between different structural families are still not clear. Here, we classify and analyse the secondary structural features and promiscuity of a comprehensive non-redundant set of domain family binding sites (DFBSs) and hetero domain-domain interactions (DDIs) extracted from our updated KBDOCK resource. We have partitioned 4001 DFBSs into five classes using their propensities for three types of secondary structural elements (“α” for helices, “β” for strands, and “γ” for irregular structure) and we have analysed how frequently these classes occur in DDIs. Our results show that β elements are not highly represented in DFBSs compared to α and γ elements. At the DDI level, all classes of binding sites tend to preferentially bind to the same class of binding sites and α/β contacts are significantly disfavored. Very few DFBSs are promiscuous: 80% of them interact with just one Pfam domain. About 50% of our Pfam domains bear only one single-partner DFBS and are therefore monogamous in their interactions with other domains. Conversely, promiscuous Pfam domains bear several DFBSs among which one or two are promiscuous, thereby multiplying the promiscuity of the concerned protein.
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16
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Barbany M, Meyer T, Hospital A, Faustino I, D'Abramo M, Morata J, Orozco M, de la Cruz X. Molecular dynamics study of naturally existing cavity couplings in proteins. PLoS One 2015; 10:e0119978. [PMID: 25816327 PMCID: PMC4376744 DOI: 10.1371/journal.pone.0119978] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2014] [Accepted: 01/26/2015] [Indexed: 11/18/2022] Open
Abstract
Couplings between protein sub-structures are a common property of protein dynamics. Some of these couplings are especially interesting since they relate to function and its regulation. In this article we have studied the case of cavity couplings because cavities can host functional sites, allosteric sites, and are the locus of interactions with the cell milieu. We have divided this problem into two parts. In the first part, we have explored the presence of cavity couplings in the natural dynamics of 75 proteins, using 20 ns molecular dynamics simulations. For each of these proteins, we have obtained two trajectories around their native state. After applying a stringent filtering procedure, we found significant cavity correlations in 60% of the proteins. We analyze and discuss the structure origins of these correlations, including neighbourhood, cavity distance, etc. In the second part of our study, we have used longer simulations (≥100 ns) from the MoDEL project, to obtain a broader view of cavity couplings, particularly about their dependence on time. Using moving window computations we explored the fluctuations of cavity couplings along time, finding that these couplings could fluctuate substantially during the trajectory, reaching in several cases correlations above 0.25/0.5. In summary, we describe the structural origin and the variations with time of cavity couplings. We complete our work with a brief discussion of the biological implications of these results.
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Affiliation(s)
- Montserrat Barbany
- Translational Bioinformatics in Neurosciences, Vall d'Hebron Research Institute (VHIR), Barcelona, Spain
| | - Tim Meyer
- Theoretische und computergestützte Biophysik, Max-Planck-Institut für biophysikalische Chemie, Göttingen, Germany
| | - Adam Hospital
- Joint IRB (Institute for Research in Biomedicine)—BSC (Barcelona Supercomputing Center) Program on Computational Biology, Barcelona, Spain
| | - Ignacio Faustino
- Joint IRB (Institute for Research in Biomedicine)—BSC (Barcelona Supercomputing Center) Program on Computational Biology, Barcelona, Spain
| | - Marco D'Abramo
- Department of Chemistry, Università degli Studi di Roma "La Sapienza", Roma, Italy
| | - Jordi Morata
- Centre for Research in Agricultural Genomics (CRAG), Barcelona, Spain
| | - Modesto Orozco
- Joint IRB (Institute for Research in Biomedicine)—BSC (Barcelona Supercomputing Center) Program on Computational Biology, Barcelona, Spain
- Departament de Bioquímica i Biologia Molecular, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
| | - Xavier de la Cruz
- Translational Bioinformatics in Neurosciences, Vall d'Hebron Research Institute (VHIR), Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- * E-mail:
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Kuenemann MA, Sperandio O, Labbé CM, Lagorce D, Miteva MA, Villoutreix BO. In silico design of low molecular weight protein-protein interaction inhibitors: Overall concept and recent advances. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2015; 119:20-32. [PMID: 25748546 DOI: 10.1016/j.pbiomolbio.2015.02.006] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2014] [Revised: 02/18/2015] [Accepted: 02/24/2015] [Indexed: 12/22/2022]
Abstract
Protein-protein interactions (PPIs) are carrying out diverse functions in living systems and are playing a major role in the health and disease states. Low molecular weight (LMW) "drug-like" inhibitors of PPIs would be very valuable not only to enhance our understanding over physiological processes but also for drug discovery endeavors. However, PPIs were deemed intractable by LMW chemicals during many years. But today, with the new experimental and in silico technologies that have been developed, about 50 PPIs have already been inhibited by LMW molecules. Here, we first focus on general concepts about protein-protein interactions, present a consensual view about ligandable pockets at the protein interfaces and the possibilities of using fast and cost effective structure-based virtual screening methods to identify PPI hits. We then discuss the design of compound collections dedicated to PPIs. Recent financial analyses of the field suggest that LMW PPI modulators could be gaining momentum over biologics in the coming years supporting further research in this area.
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Affiliation(s)
- Mélaine A Kuenemann
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm, Paris 75013, France; Inserm, U973, Paris 75013, France
| | - Olivier Sperandio
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm, Paris 75013, France; Inserm, U973, Paris 75013, France; CDithem, Faculté de Pharmacie, 1 rue du Prof Laguesse, 59000 Lille, France
| | - Céline M Labbé
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm, Paris 75013, France; Inserm, U973, Paris 75013, France; CDithem, Faculté de Pharmacie, 1 rue du Prof Laguesse, 59000 Lille, France
| | - David Lagorce
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm, Paris 75013, France; Inserm, U973, Paris 75013, France
| | - Maria A Miteva
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm, Paris 75013, France; Inserm, U973, Paris 75013, France
| | - Bruno O Villoutreix
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm, Paris 75013, France; Inserm, U973, Paris 75013, France; CDithem, Faculté de Pharmacie, 1 rue du Prof Laguesse, 59000 Lille, France.
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18
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Aumentado-Armstrong TT, Istrate B, Murgita RA. Algorithmic approaches to protein-protein interaction site prediction. Algorithms Mol Biol 2015; 10:7. [PMID: 25713596 PMCID: PMC4338852 DOI: 10.1186/s13015-015-0033-9] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2014] [Accepted: 01/07/2015] [Indexed: 12/19/2022] Open
Abstract
Interaction sites on protein surfaces mediate virtually all biological activities, and their identification holds promise for disease treatment and drug design. Novel algorithmic approaches for the prediction of these sites have been produced at a rapid rate, and the field has seen significant advancement over the past decade. However, the most current methods have not yet been reviewed in a systematic and comprehensive fashion. Herein, we describe the intricacies of the biological theory, datasets, and features required for modern protein-protein interaction site (PPIS) prediction, and present an integrative analysis of the state-of-the-art algorithms and their performance. First, the major sources of data used by predictors are reviewed, including training sets, evaluation sets, and methods for their procurement. Then, the features employed and their importance in the biological characterization of PPISs are explored. This is followed by a discussion of the methodologies adopted in contemporary prediction programs, as well as their relative performance on the datasets most recently used for evaluation. In addition, the potential utility that PPIS identification holds for rational drug design, hotspot prediction, and computational molecular docking is described. Finally, an analysis of the most promising areas for future development of the field is presented.
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19
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Cukuroglu E, Engin HB, Gursoy A, Keskin O. Hot spots in protein–protein interfaces: Towards drug discovery. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2014; 116:165-73. [DOI: 10.1016/j.pbiomolbio.2014.06.003] [Citation(s) in RCA: 113] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2014] [Revised: 05/30/2014] [Accepted: 06/12/2014] [Indexed: 11/16/2022]
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20
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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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21
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Martin J. Benchmarking protein-protein interface predictions: Why you should care about protein size. Proteins 2014; 82:1444-52. [DOI: 10.1002/prot.24512] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2013] [Revised: 12/12/2013] [Accepted: 12/26/2013] [Indexed: 11/10/2022]
Affiliation(s)
- Juliette Martin
- Bases Moléculaires et Structurales des Systèmes Infectieux; CNRS, UMR 5086; Université Lyon 1; IBCP, 7 passage du Vercors F-69367 France
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22
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CompASM: an Amber-VMD alanine scanning mutagenesis plug-in. MARCO ANTONIO CHAER NASCIMENTO 2014. [DOI: 10.1007/978-3-642-41163-2_8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
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23
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Ozbek P, Soner S, Haliloglu T. Hot spots in a network of functional sites. PLoS One 2013; 8:e74320. [PMID: 24023934 PMCID: PMC3759471 DOI: 10.1371/journal.pone.0074320] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2012] [Accepted: 08/02/2013] [Indexed: 12/05/2022] Open
Abstract
It is of significant interest to understand how proteins interact, which holds the key phenomenon in biological functions. Using dynamic fluctuations in high frequency modes, we show that the Gaussian Network Model (GNM) predicts hot spot residues with success rates ranging between S 8–58%, C 84–95%, P 5–19% and A 81–92% on unbound structures and S 8–51%, C 97–99%, P 14–50%, A 94–97% on complex structures for sensitivity, specificity, precision and accuracy, respectively. High specificity and accuracy rates with a single property on unbound protein structures suggest that hot spots are predefined in the dynamics of unbound structures and forming the binding core of interfaces, whereas the prediction of other functional residues with similar dynamic behavior explains the lower precision values. The latter is demonstrated with the case studies; ubiquitin, hen egg-white lysozyme and M2 proton channel. The dynamic fluctuations suggest a pseudo network of residues with high frequency fluctuations, which could be plausible for the mechanism of biological interactions and allosteric regulation.
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Affiliation(s)
- Pemra Ozbek
- Department of Bioengineering, Marmara University, Goztepe, Istanbul, Turkey
| | - Seren Soner
- Department of Chemical Engineering and Polymer Research Center, Bogazici University, Bebek, Turkey
| | - Turkan Haliloglu
- Department of Chemical Engineering and Polymer Research Center, Bogazici University, Bebek, Turkey
- * E-mail:
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24
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Ramos RM, Moreira IS. Computational Alanine Scanning Mutagenesis-An Improved Methodological Approach for Protein-DNA Complexes. J Chem Theory Comput 2013; 9:4243-56. [PMID: 26592413 DOI: 10.1021/ct400387r] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Proteins and protein-based complexes are the basis of many key systems in nature and have been the subject of intense research in the last decades, in an attempt to acquire comprehensive knowledge of reactions that take place in nature. Computational Alanine Scanning Mutagenesis approaches have been extensively used in the study of protein interfaces and in the determination of the most important residues for complex formation, the Hot-spots. However, as it is usually applied to the study of protein-protein interfaces, we tried to modify and apply it to the study of protein-DNA interfaces, which are also crucial in nature but have not been the subject of as much research. In this work, we carry out MD simulations of seven protein-DNA complexes and tested the influence of the variation of different parameters on the determination of the binding free energy terms (ΔΔGbinding) of 78 mutations: solvent representation, internal dielectric constant, Linear and Nonlinear Poisson-Boltzmann equation, Generalized Born model, simulation time, number of structures analyzed, number of MD trajectories, force field used, and energetic terms involved. Overall, this new approach gave an average error of 1.55 kcal/mol, and P, R, F1, accuracy, and specificity values of 0.78, 0.50, 0.61, 0.77, and 0.92, respectively. This improved computational alanine scanning mutagenesis approach may serve as a tool to explore the behavior of this important class of complexes.
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Affiliation(s)
- Rui M Ramos
- REQUIMTE/Departamento de Química e Bioquímica, Faculdade de Ciências da Universidade do Porto , Rua do Campo Alegre s/n, 4169-007 Porto, Portugal
| | - Irina S Moreira
- REQUIMTE/Departamento de Química e Bioquímica, Faculdade de Ciências da Universidade do Porto , Rua do Campo Alegre s/n, 4169-007 Porto, Portugal
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25
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Renciuk D, Blacque O, Vorlickova M, Spingler B. Crystal structures of B-DNA dodecamer containing the epigenetic modifications 5-hydroxymethylcytosine or 5-methylcytosine. Nucleic Acids Res 2013; 41:9891-900. [PMID: 23963698 PMCID: PMC3834816 DOI: 10.1093/nar/gkt738] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
5-Hydroxymethylcytosine (5-hmC) was recently identified as a relatively frequent base in eukaryotic genomes. Its physiological function is still unclear, but it is supposed to serve as an intermediate in DNA de novo demethylation. Using X-ray diffraction, we solved five structures of four variants of the d(CGCGAATTCGCG) dodecamer, containing either 5-hmC or 5-methylcytosine (5-mC) at position 3 or at position 9. The observed resolutions were between 1.42 and 1.99 Å. Cytosine modification in all cases influences neither the whole B-DNA double helix structure nor the modified base pair geometry. The additional hydroxyl group of 5-hmC with rotational freedom along the C5-C5A bond is preferentially oriented in the 3′ direction. A comparison of thermodynamic properties of the dodecamers shows no effect of 5-mC modification and a sequence-dependent only slight destabilizing effect of 5-hmC modification. Also taking into account the results of a previous functional study [Münzel et al. (2011) (Improved synthesis and mutagenicity of oligonucleotides containing 5-hydroxymethylcytosine, 5-formylcytosine and 5-carboxylcytosine. Chem. Eur. J., 17, 13782−13788)], we conclude that the 5 position of cytosine is an ideal place to encode epigenetic information. Like this, neither the helical structure nor the thermodynamics are changed, and polymerases cannot distinguish 5-hmC and 5-mC from unmodified cytosine, all these effects are making the former ones non-mutagenic.
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Affiliation(s)
- Daniel Renciuk
- Institute of Inorganic Chemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland, Institute of Biophysics, Academy of Sciences of the Czech Republic, Kralovopolska 135, 61265 Brno, Czech Republic and CEITEC-Central European Institute of Technology, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic
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26
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Moal IH, Fernandez-Recio J. Intermolecular Contact Potentials for Protein-Protein Interactions Extracted from Binding Free Energy Changes upon Mutation. J Chem Theory Comput 2013; 9:3715-27. [PMID: 26584123 DOI: 10.1021/ct400295z] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Understanding and predicting the energetics of protein-protein interactions is fundamental to the structural modeling of protein complexes. Binding free energy can be approximated as a sum of pairwise atomic or residue contact energies, which are commonly inferred from contact frequencies observed in experimental protein structures. However, such statistically inferred potentials require certain assumptions and approximation. Here, we explore the possibility of deriving atomic and residue contact potentials directly from experimental binding free energy changes following mutation and present a number of such potentials. The first set of potentials is obtained by unweighted least-squares fitting and bootsrap aggregating. The second set is calculated using a weighting scheme optimized against absolute binding affinity data, so as to account for the over-representation of certain complexes, residues, and families of interactions. The congruence of the potentials with known physical chemistry is investigated. The potentials are further validated by ranking and clustering protein-protein docking poses.
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Affiliation(s)
- Iain H Moal
- Joint BSC-IRB Research Program in Computational Biology, Life Science Department, Barcelona Supercomputing Center , C/Jordi Girona 29, 08034 Barcelona, Spain
| | - Juan Fernandez-Recio
- Joint BSC-IRB Research Program in Computational Biology, Life Science Department, Barcelona Supercomputing Center , C/Jordi Girona 29, 08034 Barcelona, Spain
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27
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Kysilka J, Vondrášek J. Towards a better understanding of the specificity of protein-protein interaction. J Mol Recognit 2013; 25:604-15. [PMID: 23108620 DOI: 10.1002/jmr.2219] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
In order to predict interaction interface for proteins, it is crucial to identify their characteristic features controlling the interaction process. We present analysis of 69 crystal structures of dimer protein complexes that provides a basis for reasonable description of the phenomenon. Interaction interfaces of two proteins at amino acids level were localized and described in terms of their chemical composition, binding preferences, and residue interaction energies utilizing Amber empirical force field. The characteristic properties of the interaction interface were compared against set of corresponding intramolecular binding parameters for amino acids in proteins. It has been found that geometrically distinct clusters of large hydrophobic amino acids (leucine, valine, isoleucine, and phenylalanine) as well as polar tyrosines and charged arginines are signatures of the protein-protein interaction interface. At some extent, we can generalize that protein-protein interaction (seen through interaction between amino acids) is very similar to the intramolecular arrangement of amino acids, although intermolecular pairs have generally lower interaction energies with their neighbors. Interfaces, therefore, possess high degree of complementarity suggesting also high selectivity of the process. The utilization of our results can improve interface prediction algorithms and improve our understanding of protein-protein recognition.
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Affiliation(s)
- Jiří Kysilka
- Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, Prague, Czech Republic
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28
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Moreira I, Martins J, Ramos R, Fernandes P, Ramos M. Understanding the importance of the aromatic amino-acid residues as hot-spots. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2013; 1834:404-14. [DOI: 10.1016/j.bbapap.2012.07.005] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2012] [Revised: 06/26/2012] [Accepted: 07/17/2012] [Indexed: 12/12/2022]
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29
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Kastritis PL, Bonvin AMJJ. On the binding affinity of macromolecular interactions: daring to ask why proteins interact. J R Soc Interface 2012; 10:20120835. [PMID: 23235262 PMCID: PMC3565702 DOI: 10.1098/rsif.2012.0835] [Citation(s) in RCA: 276] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Interactions between proteins are orchestrated in a precise and time-dependent manner, underlying cellular function. The binding affinity, defined as the strength of these interactions, is translated into physico-chemical terms in the dissociation constant (Kd), the latter being an experimental measure that determines whether an interaction will be formed in solution or not. Predicting binding affinity from structural models has been a matter of active research for more than 40 years because of its fundamental role in drug development. However, all available approaches are incapable of predicting the binding affinity of protein–protein complexes from coordinates alone. Here, we examine both theoretical and experimental limitations that complicate the derivation of structure–affinity relationships. Most work so far has concentrated on binary interactions. Systems of increased complexity are far from being understood. The main physico-chemical measure that relates to binding affinity is the buried surface area, but it does not hold for flexible complexes. For the latter, there must be a significant entropic contribution that will have to be approximated in the future. We foresee that any theoretical modelling of these interactions will have to follow an integrative approach considering the biology, chemistry and physics that underlie protein–protein recognition.
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Affiliation(s)
- Panagiotis L Kastritis
- Bijvoet Center for Biomolecular Research, Faculty of Science, Chemistry, Utrecht University, , Padualaan 8, Utrecht, The Netherlands
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30
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Zawaira A, Shibayama Y. A simple recipe for the non-expert bioinformaticist for building experimentally-testable hypotheses for proteins with no known homologs. JOURNAL OF STRUCTURAL AND FUNCTIONAL GENOMICS 2012; 13:185-200. [PMID: 22956349 DOI: 10.1007/s10969-012-9141-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/29/2012] [Accepted: 08/08/2012] [Indexed: 06/01/2023]
Abstract
The study of the protein-protein interactions (PPIs) of unique ORFs is a strategy for deciphering the biological roles of unique ORFs of interest. For uniform reference, we define unique ORFs as those for which no matching protein is found after PDB-BLAST search with default parameters. The uniqueness of the ORFs generally precludes the straightforward use of structure-based approaches in the design of experiments to explore PPIs. Many open-source bioinformatics tools, from the commonly-used to the relatively esoteric, have been built and validated to perform analyses and/or predictions of sorts on proteins. How can these available tools be combined into a protocol that helps the non-expert bioinformaticist researcher to design experiments to explore the PPIs of their unique ORF? Here we define a pragmatic protocol based on accessibility of software to achieve this and we make it concrete by applying it on two proteins-the ImuB and ImuA' proteins from Mycobacterium tuberculosis. The protocol is pragmatic in that decisions are made largely based on the availability of easy-to-use freeware. We define the following basic and user-friendly software pathway to build testable PPI hypotheses for a query protein sequence: PSI-PRED → MUSTER → metaPPISP → ASAView and ConSurf. Where possible, other analytical and/or predictive tools may be included. Our protocol combines the software predictions and analyses with general bioinformatics principles to arrive at consensus, prioritised and testable PPI hypotheses.
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Affiliation(s)
- Alexander Zawaira
- Gene Expression and Biophysics Group, Synthetic Biology, ERA, CSIR Biosciences, Brummeria, Pretoria, South Africa.
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31
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Ribeiro JV, Cerqueira NMFSA, Moreira IS, Fernandes PA, Ramos MJ. CompASM: an Amber-VMD alanine scanning mutagenesis plug-in. Theor Chem Acc 2012. [DOI: 10.1007/s00214-012-1271-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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32
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HASHMI IRINA, AKBAL-DELIBAS BAHAR, HASPEL NURIT, SHEHU AMARDA. GUIDING PROTEIN DOCKING WITH GEOMETRIC AND EVOLUTIONARY INFORMATION. J Bioinform Comput Biol 2012; 10:1242008. [DOI: 10.1142/s0219720012420085] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Structural modeling of molecular assemblies promises to improve our understanding of molecular interactions and biological function. Even when focusing on modeling structures of protein dimers from knowledge of monomeric native structure, docking two rigid structures onto one another entails exploring a large configurational space. This paper presents a novel approach for docking protein molecules and elucidating native-like configurations of protein dimers. The approach makes use of geometric hashing to focus the docking of monomeric units on geometrically complementary regions through rigid-body transformations. This geometry-based approach improves the feasibility of searching the combined configurational space. The search space is narrowed even further by focusing the sought rigid-body transformations around molecular surface regions composed of amino acids with high evolutionary conservation. This condition is based on recent findings, where analysis of protein assemblies reveals that many functional interfaces are significantly conserved throughout evolution. Different search procedures are employed in this work to search the resulting narrowed configurational space. A proof-of-concept energy-guided probabilistic search procedure is also presented. Results are shown on a broad list of 18 protein dimers and additionally compared with data reported by other labs. Our analysis shows that focusing the search around evolutionary-conserved interfaces results in lower lRMSDs.
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Affiliation(s)
- IRINA HASHMI
- Department of Computer Science, George Mason University, Fairfax, VA, 22030, USA
| | - BAHAR AKBAL-DELIBAS
- Department of Computer Science, University of Massachusetts at Boston, Boston, MA, 02125, USA
| | - NURIT HASPEL
- Department of Computer Science, University of Massachusetts at Boston, Boston, MA, 02125, USA
| | - AMARDA SHEHU
- Department of Computer Science, George Mason University, Fairfax, VA, 22030, USA
- Department of Bioinformatics and Computational Biology, George Mason University, Fairfax, VA, 22030, USA
- Department of Bioengineering, George Mason University, Fairfax, VA, 22030, USA
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33
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Taboureau O, Baell JB, Fernández-Recio J, Villoutreix BO. Established and emerging trends in computational drug discovery in the structural genomics era. ACTA ACUST UNITED AC 2012; 19:29-41. [PMID: 22284352 DOI: 10.1016/j.chembiol.2011.12.007] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2011] [Revised: 12/05/2011] [Accepted: 12/08/2011] [Indexed: 12/01/2022]
Abstract
Bioinformatics and chemoinformatics approaches contribute to hit discovery, hit-to-lead optimization, safety profiling, and target identification and enhance our overall understanding of the health and disease states. A vast repertoire of computational methods has been reported and increasingly combined in order to address more and more challenging targets or complex molecular mechanisms in the context of large-scale integration of structure and bioactivity data produced by private and public drug research. This review explores some key computational methods directly linked to drug discovery and chemical biology with a special emphasis on compound collection preparation, virtual screening, protein docking, and systems pharmacology. A list of generally freely available software packages and online resources is provided, and examples of successful applications are briefly commented upon.
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Affiliation(s)
- Olivier Taboureau
- Center for Biological Sequences Analysis, Department of Systems Biology, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
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