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Singh A, Copeland MM, Kundrotas PJ, Vakser IA. GRAMM Web Server for Protein Docking. Methods Mol Biol 2024; 2714:101-112. [PMID: 37676594 DOI: 10.1007/978-1-0716-3441-7_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Prediction of the structure of protein complexes by docking methods is a well-established research field. The intermolecular energy landscapes in protein-protein interactions can be used to refine docking predictions and to detect macro-characteristics, such as the binding funnel. A new GRAMM web server for protein docking predicts a spectrum of docking poses that characterize the intermolecular energy landscape in protein interaction. A user-friendly interface provides options to choose free or template-based docking, as well as other advanced features, such as clustering of the docking poses, and interactive visualization of the docked models.
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Affiliation(s)
- Amar Singh
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, KS, USA
| | - Matthew M Copeland
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, KS, USA
| | - Petras J Kundrotas
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, KS, USA.
| | - Ilya A Vakser
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, KS, USA.
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2
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Vakser IA. Prediction of protein interactions is essential for studying biomolecular mechanisms. Proteomics 2023; 23:e2300219. [PMID: 37667816 DOI: 10.1002/pmic.202300219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 06/26/2023] [Accepted: 07/05/2023] [Indexed: 09/06/2023]
Abstract
Structural characterization of protein interactions is essential for our ability to understand and modulate physiological processes. Computational approaches to modeling of protein complexes provide structural information that far exceeds capabilities of the existing experimental techniques. Protein structure prediction in general, and prediction of protein interactions in particular, has been revolutionized by the rapid progress in Deep Learning techniques. The work of Schweke et al. (Proteomics 2023, 23, 2200323) presents a community-wide study of an important problem of distinguishing physiological protein-protein complexes/interfaces (experimentally determined or modeled) from non-physiological ones. The authors designed and generated a large benchmark set of physiological and non-physiological homodimeric complexes, and evaluated a large set of scoring functions, as well as AlphaFold predictions, on their ability to discriminate the non-physiological interfaces. The problem of separating physiological interfaces from non-physiological ones is very difficult, largely due to the lack of a clear distinction between the two categories in a crowded environment inside a living cell. Still, the ability to identify key physiologically significant interfaces in the variety of possible configurations of a protein-protein complex is important. The study presents a major data resource and methodological development in this important direction for molecular and cellular biology.
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Affiliation(s)
- Ilya A Vakser
- Computational Biology Program, The University of Kansas, Lawrence, Kansas, USA
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Singh A, Copeland MM, Kundrotas PJ, Vakser IA. Gramm: A webserver for free and template-based protein docking. Biophys J 2023; 122:47a. [PMID: 36784471 DOI: 10.1016/j.bpj.2022.11.463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023] Open
Affiliation(s)
- Amar Singh
- Computational Biology Program, The University of Kansas, Lawrence, KS, USA
| | - Matthew M Copeland
- Computational Biology Program, The University of Kansas, Lawrence, KS, USA
| | - Petras J Kundrotas
- Computational Biology Program, The University of Kansas, Lawrence, KS, USA
| | - Ilya A Vakser
- Computational Biology Program, The University of Kansas, Lawrence, KS, USA
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Collins KW, Copeland MM, Kotthoff I, Singh A, Kundrotas PJ, Vakser IA. Dockground resource for protein recognition studies. Protein Sci 2022; 31:e4481. [PMID: 36281025 PMCID: PMC9667896 DOI: 10.1002/pro.4481] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 10/19/2022] [Accepted: 10/20/2022] [Indexed: 12/13/2022]
Abstract
Structural information of protein-protein interactions is essential for characterization of life processes at the molecular level. While a small fraction of known protein interactions has experimentally determined structures, computational modeling of protein complexes (protein docking) has to fill the gap. The Dockground resource (http://dockground.compbio.ku.edu) provides a collection of datasets for the development and testing of protein docking techniques. Currently, Dockground contains datasets for the bound and the unbound (experimentally determined and simulated) protein structures, model-model complexes, docking decoys of experimentally determined and modeled proteins, and templates for comparative docking. The Dockground bound proteins dataset is a core set, from which other Dockground datasets are generated. It is devised as a relational PostgreSQL database containing information on experimentally determined protein-protein complexes. This report on the Dockground resource describes current status of the datasets, new automated update procedures and further development of the core datasets. We also present a new Dockground interactive web interface, which allows search by various parameters, such as release date, multimeric state, complex type, structure resolution, and so on, visualization of the search results with a number of customizable parameters, as well as downloadable datasets with predefined levels of sequence and structure redundancy.
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Affiliation(s)
| | | | - Ian Kotthoff
- Computational Biology ProgramThe University of KansasKansasUSA
| | - Amar Singh
- Computational Biology ProgramThe University of KansasKansasUSA
| | | | - Ilya A. Vakser
- Computational Biology ProgramThe University of KansasKansasUSA
- Department of Molecular BiosciencesThe University of KansasKansasUSA
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Jenkins NW, Kundrotas PJ, Vakser IA. Size of the protein-protein energy funnel in crowded environment. Front Mol Biosci 2022; 9:1031225. [PMID: 36425657 PMCID: PMC9679368 DOI: 10.3389/fmolb.2022.1031225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 10/26/2022] [Indexed: 11/09/2022] Open
Abstract
Association of proteins to a significant extent is determined by their geometric complementarity. Large-scale recognition factors, which directly relate to the funnel-like intermolecular energy landscape, provide important insights into the basic rules of protein recognition. Previously, we showed that simple energy functions and coarse-grained models reveal major characteristics of the energy landscape. As new computational approaches increasingly address structural modeling of a whole cell at the molecular level, it becomes important to account for the crowded environment inside the cell. The crowded environment drastically changes protein recognition properties, and thus significantly alters the underlying energy landscape. In this study, we addressed the effect of crowding on the protein binding funnel, focusing on the size of the funnel. As crowders occupy the funnel volume, they make it less accessible to the ligands. Thus, the funnel size, which can be defined by ligand occupancy, is generally reduced with the increase of the crowders concentration. This study quantifies this reduction for different concentration of crowders and correlates this dependence with the structural details of the interacting proteins. The results provide a better understanding of the rules of protein association in the crowded environment.
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Affiliation(s)
- Nathan W. Jenkins
- Computational Biology Program, The University of Kansas, Lawrence, KS, United States
| | - Petras J. Kundrotas
- Computational Biology Program, The University of Kansas, Lawrence, KS, United States
- *Correspondence: Petras J. Kundrotas, ; Ilya A. Vakser,
| | - Ilya A. Vakser
- Computational Biology Program, The University of Kansas, Lawrence, KS, United States
- Department of Molecular Biosciences, The University of Kansas, Lawrence, KS, United States
- *Correspondence: Petras J. Kundrotas, ; Ilya A. Vakser,
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Kotthoff I, Kundrotas PJ, Vakser IA. Dockground
scoring benchmarks for protein docking. Proteins 2022; 90:1259-1266. [DOI: 10.1002/prot.26306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 12/06/2021] [Accepted: 01/21/2022] [Indexed: 11/05/2022]
Affiliation(s)
- Ian Kotthoff
- Computational Biology Program The University of Kansas Lawrence Kansas USA
| | | | - Ilya A. Vakser
- Computational Biology Program The University of Kansas Lawrence Kansas USA
- Department of Molecular Biosciences The University of Kansas Lawrence Kansas USA
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Malladi S, Powell HR, David A, Islam SA, Copeland MM, Kundrotas PJ, Sternberg MJ, Vakser IA. GWYRE: A resource for mapping variants onto experimental and modeled structures of human protein complexes. J Mol Biol 2022; 434:167608. [PMID: 35662458 PMCID: PMC9188266 DOI: 10.1016/j.jmb.2022.167608] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 03/31/2022] [Accepted: 04/20/2022] [Indexed: 02/08/2023]
Abstract
Structure of protein complexes is important for interpreting genetic variation. Data on single amino acid variants is available from high-throughput sequencing. Integrated modeling approach was applied to proteins and their complexes. GWYRE resource incorporates predicted protein complexes with mapped mutations.
Rapid progress in structural modeling of proteins and their interactions is powered by advances in knowledge-based methodologies along with better understanding of physical principles of protein structure and function. The pool of structural data for modeling of proteins and protein–protein complexes is constantly increasing due to the rapid growth of protein interaction databases and Protein Data Bank. The GWYRE (Genome Wide PhYRE) project capitalizes on these developments by advancing and applying new powerful modeling methodologies to structural modeling of protein–protein interactions and genetic variation. The methods integrate knowledge-based tertiary structure prediction using Phyre2 and quaternary structure prediction using template-based docking by a full-structure alignment protocol to generate models for binary complexes. The predictions are incorporated in a comprehensive public resource for structural characterization of the human interactome and the location of human genetic variants. The GWYRE resource facilitates better understanding of principles of protein interaction and structure/function relationships. The resource is available at http://www.gwyre.org.
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Lensink MF, Brysbaert G, Mauri T, Nadzirin N, Velankar S, Chaleil RAG, Clarence T, Bates PA, Kong R, Liu B, Yang G, Liu M, Shi H, Lu X, Chang S, Roy RS, Quadir F, Liu J, Cheng J, Antoniak A, Czaplewski C, Giełdoń A, Kogut M, Lipska AG, Liwo A, Lubecka EA, Maszota-Zieleniak M, Sieradzan AK, Ślusarz R, Wesołowski PA, Zięba K, Del Carpio Muñoz CA, Ichiishi E, Harmalkar A, Gray JJ, Bonvin AMJJ, Ambrosetti F, Vargas Honorato R, Jandova Z, Jiménez-García B, Koukos PI, Van Keulen S, Van Noort CW, Réau M, Roel-Touris J, Kotelnikov S, Padhorny D, Porter KA, Alekseenko A, Ignatov M, Desta I, Ashizawa R, Sun Z, Ghani U, Hashemi N, Vajda S, Kozakov D, Rosell M, Rodríguez-Lumbreras LA, Fernandez-Recio J, Karczynska A, Grudinin S, Yan Y, Li H, Lin P, Huang SY, Christoffer C, Terashi G, Verburgt J, Sarkar D, Aderinwale T, Wang X, Kihara D, Nakamura T, Hanazono Y, Gowthaman R, Guest JD, Yin R, Taherzadeh G, Pierce BG, Barradas-Bautista D, Cao Z, Cavallo L, Oliva R, Sun Y, Zhu S, Shen Y, Park T, Woo H, Yang J, Kwon S, Won J, Seok C, Kiyota Y, Kobayashi S, Harada Y, Takeda-Shitaka M, Kundrotas PJ, Singh A, Vakser IA, Dapkūnas J, Olechnovič K, Venclovas Č, Duan R, Qiu L, Xu X, Zhang S, Zou X, Wodak SJ. Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment. Proteins 2021; 89:1800-1823. [PMID: 34453465 PMCID: PMC8616814 DOI: 10.1002/prot.26222] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 07/24/2021] [Accepted: 08/05/2021] [Indexed: 12/19/2022]
Abstract
We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individual subunits. Twenty-five CAPRI groups including eight automatic servers submitted ~1250 models per target. Twenty groups including six servers participated in the CAPRI scoring challenge submitted ~190 models per target. The accuracy of the predicted models was evaluated using the classical CAPRI criteria. The prediction performance was measured by a weighted scoring scheme that takes into account the number of models of acceptable quality or higher submitted by each group as part of their five top-ranking models. Compared to the previous CASP-CAPRI challenge, top performing groups submitted such models for a larger fraction (70-75%) of the targets in this Round, but fewer of these models were of high accuracy. Scorer groups achieved stronger performance with more groups submitting correct models for 70-80% of the targets or achieving high accuracy predictions. Servers performed less well in general, except for the MDOCKPP and LZERD servers, who performed on par with human groups. In addition to these results, major advances in methodology are discussed, providing an informative overview of where the prediction of protein assemblies currently stands.
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Affiliation(s)
- Marc F Lensink
- CNRS UMR8576 UGSF, Institute for Structural and Functional Glycobiology, University of Lille, Lille, France
| | - Guillaume Brysbaert
- CNRS UMR8576 UGSF, Institute for Structural and Functional Glycobiology, University of Lille, Lille, France
| | - Théo Mauri
- CNRS UMR8576 UGSF, Institute for Structural and Functional Glycobiology, University of Lille, Lille, France
| | - Nurul Nadzirin
- Protein Data Bank in Europe (PDBe), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Sameer Velankar
- Protein Data Bank in Europe (PDBe), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | | | - Tereza Clarence
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK
| | - Paul A Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK
| | - Ren Kong
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Bin Liu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Guangbo Yang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Ming Liu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Hang Shi
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Xufeng Lu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Shan Chang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Raj S Roy
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
| | - Farhan Quadir
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
| | - Jian Liu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
- Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri, USA
| | - Anna Antoniak
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | | | - Artur Giełdoń
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Mateusz Kogut
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | | | - Adam Liwo
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Emilia A Lubecka
- Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk, Poland
| | | | | | - Rafał Ślusarz
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Patryk A Wesołowski
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
- Intercollegiate Faculty of Biotechnology, University of Gdansk and Medical University of Gdansk, Gdansk, Poland
| | - Karolina Zięba
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | | | - Eiichiro Ichiishi
- International University of Health and Welfare Hospital (IUHW Hospital), Nasushiobara City, Japan
| | - Ameya Harmalkar
- Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jeffrey J Gray
- Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Alexandre M J J Bonvin
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Francesco Ambrosetti
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Rodrigo Vargas Honorato
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Zuzana Jandova
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Brian Jiménez-García
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Panagiotis I Koukos
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Siri Van Keulen
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Charlotte W Van Noort
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Manon Réau
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Jorge Roel-Touris
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Sergei Kotelnikov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
- Innopolis University, Russia
| | - Dzmitry Padhorny
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Kathryn A Porter
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Andrey Alekseenko
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
- Institute of Computer-Aided Design of the Russian Academy of Sciences, Moscow, Russia
| | - Mikhail Ignatov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Israel Desta
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Ryota Ashizawa
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Zhuyezi Sun
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Usman Ghani
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Nasser Hashemi
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
- Department of Chemistry, Boston University, Boston, Massachusetts, USA
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Mireia Rosell
- Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC - Universidad de la Rioja - Gobierno de La Rioja, Logrono, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - Luis A Rodríguez-Lumbreras
- Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC - Universidad de la Rioja - Gobierno de La Rioja, Logrono, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - Juan Fernandez-Recio
- Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC - Universidad de la Rioja - Gobierno de La Rioja, Logrono, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | | | - Sergei Grudinin
- Université Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Grenoble, France
| | - Yumeng Yan
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
| | - Hao Li
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
| | - Peicong Lin
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
| | - Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
| | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Jacob Verburgt
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Daipayan Sarkar
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Tunde Aderinwale
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
| | - Xiao Wang
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Tsukasa Nakamura
- Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi, Japan
| | - Yuya Hanazono
- Institute for Quantum Life Science, National Institutes for Quantum and Radiological Science and Technology, Tokai, Ibaraki, Japan
| | - Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | - Johnathan D Guest
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | - Rui Yin
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | - Ghazaleh Taherzadeh
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | - Brian G Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | | | - Zhen Cao
- King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Luigi Cavallo
- King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Romina Oliva
- University of Naples "Parthenope", Napoli, Italy
| | - Yuanfei Sun
- Department of Electrical and Computer Engineering, Texas A&M University, Texas, USA
| | - Shaowen Zhu
- Department of Electrical and Computer Engineering, Texas A&M University, Texas, USA
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, Texas, USA
| | - Taeyong Park
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Hyeonuk Woo
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Jinsol Yang
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Sohee Kwon
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Jonghun Won
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Yasuomi Kiyota
- School of Pharmacy, Kitasato University, Minato-ku, Tokyo, Japan
| | | | - Yoshiki Harada
- School of Pharmacy, Kitasato University, Minato-ku, Tokyo, Japan
| | | | - Petras J Kundrotas
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas, USA
| | - Amar Singh
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas, USA
| | - Ilya A Vakser
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas, USA
| | - Justas Dapkūnas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Kliment Olechnovič
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Rui Duan
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
| | - Liming Qiu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
| | - Xianjin Xu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
| | - Shuang Zhang
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
| | - Xiaoqin Zou
- Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri, USA
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri, USA
- Department of Biochemistry, University of Missouri, Columbia, Missouri, USA
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9
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Badal VD, Kundrotas PJ, Vakser IA. Text mining for modeling of protein complexes enhanced by machine learning. Bioinformatics 2021; 37:497-505. [PMID: 32960948 PMCID: PMC8088328 DOI: 10.1093/bioinformatics/btaa823] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 09/04/2020] [Accepted: 09/08/2020] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Procedures for structural modeling of protein-protein complexes (protein docking) produce a number of models which need to be further analyzed and scored. Scoring can be based on independently determined constraints on the structure of the complex, such as knowledge of amino acids essential for the protein interaction. Previously, we showed that text mining of residues in freely available PubMed abstracts of papers on studies of protein-protein interactions may generate such constraints. However, absence of post-processing of the spotted residues reduced usability of the constraints, as a significant number of the residues were not relevant for the binding of the specific proteins. RESULTS We explored filtering of the irrelevant residues by two machine learning approaches, Deep Recursive Neural Network (DRNN) and Support Vector Machine (SVM) models with different training/testing schemes. The results showed that the DRNN model is superior to the SVM model when training is performed on the PMC-OA full-text articles and applied to classification (interface or non-interface) of the residues spotted in the PubMed abstracts. When both training and testing is performed on full-text articles or on abstracts, the performance of these models is similar. Thus, in such cases, there is no need to utilize computationally demanding DRNN approach, which is computationally expensive especially at the training stage. The reason is that SVM success is often determined by the similarity in data/text patterns in the training and the testing sets, whereas the sentence structures in the abstracts are, in general, different from those in the full text articles. AVAILABILITYAND IMPLEMENTATION The code and the datasets generated in this study are available at https://gitlab.ku.edu/vakser-lab-public/text-mining/-/tree/2020-09-04. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | - Ilya A Vakser
- Computational Biology Program.,Department of Molecular Biosciences, The University of Kansas, Lawrence, KS 66045, USA
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10
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Hadarovich A, Chakravarty D, Tuzikov AV, Ben-Tal N, Kundrotas PJ, Vakser IA. Structural motifs in protein cores and at protein-protein interfaces are different. Protein Sci 2020; 30:381-390. [PMID: 33166001 DOI: 10.1002/pro.3996] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 10/30/2020] [Accepted: 10/31/2020] [Indexed: 11/10/2022]
Abstract
Structures of proteins and protein-protein complexes are determined by the same physical principles and thus share a number of similarities. At the same time, there could be differences because in order to function, proteins interact with other molecules, undergo conformations changes, and so forth, which might impose different restraints on the tertiary versus quaternary structures. This study focuses on structural properties of protein-protein interfaces in comparison with the protein core, based on the wealth of currently available structural data and new structure-based approaches. The results showed that physicochemical characteristics, such as amino acid composition, residue-residue contact preferences, and hydrophilicity/hydrophobicity distributions, are similar in protein core and protein-protein interfaces. On the other hand, characteristics that reflect the evolutionary pressure, such as structural composition and packing, are largely different. The results provide important insight into fundamental properties of protein structure and function. At the same time, the results contribute to better understanding of the ways to dock proteins. Recent progress in predicting structures of individual proteins follows the advancement of deep learning techniques and new approaches to residue coevolution data. Protein core could potentially provide large amounts of data for application of the deep learning to docking. However, our results showed that the core motifs are significantly different from those at protein-protein interfaces, and thus may not be directly useful for docking. At the same time, such difference may help to overcome a major obstacle in application of the coevolutionary data to docking-discrimination of the intramolecular information not directly relevant to docking.
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Affiliation(s)
- Anna Hadarovich
- Computational Biology Program, The University of Kansas, Lawrence, Kansas, USA.,United Institute of Informatics Problems, National Academy of Sciences, Minsk, Belarus
| | - Devlina Chakravarty
- Computational Biology Program, The University of Kansas, Lawrence, Kansas, USA.,Department of Chemistry, Rutgers University, Camden, New Jersey, USA
| | - Alexander V Tuzikov
- United Institute of Informatics Problems, National Academy of Sciences, Minsk, Belarus
| | - Nir Ben-Tal
- Department of Biochemistry and Molecular Biology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Petras J Kundrotas
- Computational Biology Program, The University of Kansas, Lawrence, Kansas, USA
| | - Ilya A Vakser
- Computational Biology Program, The University of Kansas, Lawrence, Kansas, USA.,Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, USA
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11
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Abstract
Current developments in protein docking aim at improvement of applicability, accuracy and utility of modeling macromolecular complexes. The challenges include the need for greater emphasis on protein docking to molecules of different types, proper accounting for conformational flexibility upon binding, new promising methodologies based on residue co-evolution and deep learning, affinity prediction, and further development of fully automated docking servers. Importantly, new developments increasingly focus on realistic modeling of protein interactions in vivo, including crowded environment inside a cell, which involves multiple transient encounters, and propagating the system in time. This opinion paper offers the author's perspective on these challenges in structural modeling of protein interactions and the future of protein docking.
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Affiliation(s)
- Ilya A Vakser
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, KS 66045, USA.
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12
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Singh A, Dauzhenka T, Kundrotas PJ, Sternberg MJE, Vakser IA. Application of docking methodologies to modeled proteins. Proteins 2020; 88:1180-1188. [PMID: 32170770 DOI: 10.1002/prot.25889] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 02/15/2020] [Accepted: 03/07/2020] [Indexed: 12/12/2022]
Abstract
Protein docking is essential for structural characterization of protein interactions. Besides providing the structure of protein complexes, modeling of proteins and their complexes is important for understanding the fundamental principles and specific aspects of protein interactions. The accuracy of protein modeling, in general, is still less than that of the experimental approaches. Thus, it is important to investigate the applicability of docking techniques to modeled proteins. We present new comprehensive benchmark sets of protein models for the development and validation of protein docking, as well as a systematic assessment of free and template-based docking techniques on these sets. As opposed to previous studies, the benchmark sets reflect the real case modeling/docking scenario where the accuracy of the models is assessed by the modeling procedure, without reference to the native structure (which would be unknown in practical applications). We also expanded the analysis to include docking of protein pairs where proteins have different structural accuracy. The results show that, in general, the template-based docking is less sensitive to the structural inaccuracies of the models than the free docking. The near-native docking poses generated by the template-based approach, typically, also have higher ranks than those produces by the free docking (although the free docking is indispensable in modeling the multiplicity of protein interactions in a crowded cellular environment). The results show that docking techniques are applicable to protein models in a broad range of modeling accuracy. The study provides clear guidelines for practical applications of docking to protein models.
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Affiliation(s)
- Amar Singh
- Computational Biology Program, The University of Kansas, Lawrence, Kansas, USA
| | - Taras Dauzhenka
- Computational Biology Program, The University of Kansas, Lawrence, Kansas, USA
| | - Petras J Kundrotas
- Computational Biology Program, The University of Kansas, Lawrence, Kansas, USA
| | - Michael J E Sternberg
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, South Kensington, London, UK
| | - Ilya A Vakser
- Computational Biology Program, The University of Kansas, Lawrence, Kansas, USA.,Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, USA
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13
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Chakravarty D, McElfresh GW, Kundrotas PJ, Vakser IA. How to choose templates for modeling of protein complexes: Insights from benchmarking template-based docking. Proteins 2020; 88:1070-1081. [PMID: 31994759 DOI: 10.1002/prot.25875] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 01/07/2020] [Accepted: 01/22/2020] [Indexed: 01/01/2023]
Abstract
Comparative docking is based on experimentally determined structures of protein-protein complexes (templates), following the paradigm that proteins with similar sequences and/or structures form similar complexes. Modeling utilizing structure similarity of target monomers to template complexes significantly expands structural coverage of the interactome. Template-based docking by structure alignment can be performed for the entire structures or by aligning targets to the bound interfaces of the experimentally determined complexes. Systematic benchmarking of docking protocols based on full and interface structure alignment showed that both protocols perform similarly, with top 1 docking success rate 26%. However, in terms of the models' quality, the interface-based docking performed marginally better. The interface-based docking is preferable when one would suspect a significant conformational change in the full protein structure upon binding, for example, a rearrangement of the domains in multidomain proteins. Importantly, if the same structure is selected as the top template by both full and interface alignment, the docking success rate increases 2-fold for both top 1 and top 10 predictions. Matching structural annotations of the target and template proteins for template detection, as a computationally less expensive alternative to structural alignment, did not improve the docking performance. Sophisticated remote sequence homology detection added templates to the pool of those identified by structure-based alignment, suggesting that for practical docking, the combination of the structure alignment protocols and the remote sequence homology detection may be useful in order to avoid potential flaws in generation of the structural templates library.
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Affiliation(s)
| | - G W McElfresh
- Computational Biology Program, The University of Kansas, Lawrence, Kansas
| | - Petras J Kundrotas
- Computational Biology Program, The University of Kansas, Lawrence, Kansas
| | - Ilya A Vakser
- Computational Biology Program, The University of Kansas, Lawrence, Kansas.,Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas
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14
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Kotthoff IP, Kundrotas PJ, Vakser IA. Docking Decoys for Modeled Proteins. Biophys J 2020. [DOI: 10.1016/j.bpj.2019.11.1729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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15
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Abstract
Databases of protein-protein complexes are essential for the development of protein modeling/docking techniques. Such databases provide a knowledge base for docking algorithms, intermolecular potentials, search procedures, scoring functions, and refinement protocols. Development of docking techniques requires systematic validation of the modeling protocols on carefully curated benchmark sets of complexes. We present a description and a guide to the DOCKGROUND resource ( http://dockground.compbio.ku.edu ) for structural modeling of protein interactions. The resource integrates various datasets of protein complexes and other data for the development and testing of protein docking techniques. The sets include bound complexes, experimentally determined unbound, simulated unbound, model-model complexes, and docking decoys. The datasets are available to the user community through a Web interface.
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Affiliation(s)
- Petras J Kundrotas
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, KS, USA.
| | - Ian Kotthoff
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, KS, USA
| | - Sherman W Choi
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, KS, USA
| | - Matthew M Copeland
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, KS, USA
| | - Ilya A Vakser
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, KS, USA.
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16
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Lensink MF, Brysbaert G, Nadzirin N, Velankar S, Chaleil RAG, Gerguri T, Bates PA, Laine E, Carbone A, Grudinin S, Kong R, Liu RR, Xu XM, Shi H, Chang S, Eisenstein M, Karczynska A, Czaplewski C, Lubecka E, Lipska A, Krupa P, Mozolewska M, Golon Ł, Samsonov S, Liwo A, Crivelli S, Pagès G, Karasikov M, Kadukova M, Yan Y, Huang SY, Rosell M, Rodríguez-Lumbreras LA, Romero-Durana M, Díaz-Bueno L, Fernandez-Recio J, Christoffer C, Terashi G, Shin WH, Aderinwale T, Subraman SRMV, Kihara D, Kozakov D, Vajda S, Porter K, Padhorny D, Desta I, Beglov D, Ignatov M, Kotelnikov S, Moal IH, Ritchie DW, de Beauchêne IC, Maigret B, Devignes MD, Echartea MER, Barradas-Bautista D, Cao Z, Cavallo L, Oliva R, Cao Y, Shen Y, Baek M, Park T, Woo H, Seok C, Braitbard M, Bitton L, Scheidman-Duhovny D, Dapkūnas J, Olechnovič K, Venclovas Č, Kundrotas PJ, Belkin S, Chakravarty D, Badal VD, Vakser IA, Vreven T, Vangaveti S, Borrman T, Weng Z, Guest JD, Gowthaman R, Pierce BG, Xu X, Duan R, Qiu L, Hou J, Merideth BR, Ma Z, Cheng J, Zou X, Koukos PI, Roel-Touris J, Ambrosetti F, Geng C, Schaarschmidt J, Trellet ME, Melquiond ASJ, Xue L, Jiménez-García B, van Noort CW, Honorato RV, Bonvin AMJJ, Wodak SJ. Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment. Proteins 2019; 87:1200-1221. [PMID: 31612567 PMCID: PMC7274794 DOI: 10.1002/prot.25838] [Citation(s) in RCA: 79] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Revised: 09/26/2019] [Accepted: 09/27/2019] [Indexed: 12/28/2022]
Abstract
We present the results for CAPRI Round 46, the third joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo-oligomers and 6 heterocomplexes. Eight of the homo-oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher-order assemblies. These were more difficult to model, as their prediction mainly involved "ab-initio" docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance "gap" was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template-based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements.
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Affiliation(s)
- Marc F. Lensink
- University of Lille, CNRS UMR8576 UGSF, Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France
| | - Guillaume Brysbaert
- University of Lille, CNRS UMR8576 UGSF, Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France
| | - Nurul Nadzirin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Sameer Velankar
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | | | - Tereza Gerguri
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK
| | - Paul A. Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK
| | - Elodie Laine
- CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), Sorbonne Université, Paris, France
| | - Alessandra Carbone
- CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), Sorbonne Université, Paris, France
- Institut Universitaire de France (IUF), Paris, France
| | - Sergei Grudinin
- Université Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, Grenoble, France
| | - Ren Kong
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Ran-Ran Liu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Xi-Ming Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Hang Shi
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Shan Chang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Miriam Eisenstein
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
| | | | | | - Emilia Lubecka
- Institute of Informatics, Faculty of Mathematics, Physics, and Informatics, University of Gdańsk, Gdańsk, Poland
| | | | - Paweł Krupa
- Polish Academy of Sciences, Institute of Physics, Warsaw, Poland
| | | | - Łukasz Golon
- Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
| | | | - Adam Liwo
- Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
- School of Computational Sciences, Korea Institute for Advanced Study, Seoul, South Korea
| | | | - Guillaume Pagès
- Université Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, Grenoble, France
| | | | - Maria Kadukova
- Université Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, Grenoble, France
- Moscow Institute of Physics and Technology, Dolgoprudniy, Russia
| | - Yumeng Yan
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Mireia Rosell
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Instituto de Ciencias de la Vid y del Vino (ICVV-CSIC), Logroño, Spain
| | - Luis A. Rodríguez-Lumbreras
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Instituto de Ciencias de la Vid y del Vino (ICVV-CSIC), Logroño, Spain
| | | | | | - Juan Fernandez-Recio
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Instituto de Ciencias de la Vid y del Vino (ICVV-CSIC), Logroño, Spain
- Instituto de Biología Molecular de Barcelona (IBMB-CSIC), Barcelona, Spain
| | | | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana
| | - Woong-Hee Shin
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana
| | - Tunde Aderinwale
- Department of Computer Science, Purdue University, West Lafayette, Indiana
| | | | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, Indiana
| | - Dima Kozakov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
- Department of Chemistry, Boston University, Boston, Massachusetts
| | - Kathryn Porter
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Dzmitry Padhorny
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Israel Desta
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Mikhail Ignatov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Sergey Kotelnikov
- Moscow Institute of Physics and Technology, Dolgoprudniy, Russia
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Iain H. Moal
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | | | | | | | | | | | - Didier Barradas-Bautista
- Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Zhen Cao
- Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Luigi Cavallo
- Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Romina Oliva
- Department of Sciences and Technologies, University of Naples “Parthenope”, Napoli, Italy
| | - Yue Cao
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas
| | - Minkyung Baek
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Taeyong Park
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Hyeonuk Woo
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Merav Braitbard
- Department of Biological Chemistry, Institute of Live Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Lirane Bitton
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Dina Scheidman-Duhovny
- Department of Biological Chemistry, Institute of Live Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Justas Dapkūnas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Kliment Olechnovič
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Petras J. Kundrotas
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Saveliy Belkin
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Devlina Chakravarty
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Varsha D. Badal
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Ilya A. Vakser
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Thom Vreven
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts
| | - Sweta Vangaveti
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts
| | - Tyler Borrman
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts
| | - Zhiping Weng
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts
| | - Johnathan D. Guest
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland
| | - Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland
| | - Brian G. Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland
| | - Xianjin Xu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
| | - Rui Duan
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
| | - Liming Qiu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
| | - Jie Hou
- Department of Computer Science, University of Missouri, Columbia, Missouri
| | - Benjamin Ryan Merideth
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
- Informatics Institute, University of Missouri, Columbia, Missouri
| | - Zhiwei Ma
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri
| | - Jianlin Cheng
- Department of Computer Science, University of Missouri, Columbia, Missouri
- Informatics Institute, University of Missouri, Columbia, Missouri
| | - Xiaoqin Zou
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
- Informatics Institute, University of Missouri, Columbia, Missouri
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri
- Department of Biochemistry, University of Missouri, Columbia, Missouri
| | - Panagiotis I. Koukos
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Jorge Roel-Touris
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Francesco Ambrosetti
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Cunliang Geng
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Jörg Schaarschmidt
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Mikael E. Trellet
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Adrien S. J. Melquiond
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Li Xue
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Brian Jiménez-García
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Charlotte W. van Noort
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Rodrigo V. Honorato
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Alexandre M. J. J. Bonvin
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
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17
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Ofoegbu TC, David A, Kelley LA, Mezulis S, Islam SA, Mersmann SF, Strömich L, Vakser IA, Houlston RS, Sternberg MJE. PhyreRisk: A Dynamic Web Application to Bridge Genomics, Proteomics and 3D Structural Data to Guide Interpretation of Human Genetic Variants. J Mol Biol 2019; 431:2460-2466. [PMID: 31075275 PMCID: PMC6597944 DOI: 10.1016/j.jmb.2019.04.043] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 04/02/2019] [Accepted: 04/29/2019] [Indexed: 12/12/2022]
Abstract
PhyreRisk is an open-access, publicly accessible web application for interactively bridging genomic, proteomic and structural data facilitating the mapping of human variants onto protein structures. A major advance over other tools for sequence-structure variant mapping is that PhyreRisk provides information on 20,214 human canonical proteins and an additional 22,271 alternative protein sequences (isoforms). Specifically, PhyreRisk provides structural coverage (partial or complete) for 70% (14,035 of 20,214 canonical proteins) of the human proteome, by storing 18,874 experimental structures and 84,818 pre-built models of canonical proteins and their isoforms generated using our in house Phyre2. PhyreRisk reports 55,732 experimentally, multi-validated protein interactions from IntAct and 24,260 experimental structures of protein complexes. Another major feature of PhyreRisk is that, rather than presenting a limited set of precomputed variant-structure mapping of known genetic variants, it allows the user to explore novel variants using, as input, genomic coordinates formats (Ensembl, VCF, reference SNP ID and HGVS notations) and Human Build GRCh37 and GRCh38. PhyreRisk also supports mapping variants using amino acid coordinates and searching for genes or proteins of interest. PhyreRisk is designed to empower researchers to translate genetic data into protein structural information, thereby providing a more comprehensive appreciation of the functional impact of variants. PhyreRisk is freely available at http://phyrerisk.bc.ic.ac.uk.
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Affiliation(s)
- Tochukwu C Ofoegbu
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK
| | - Alessia David
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK.
| | - Lawrence A Kelley
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK
| | - Stefans Mezulis
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK
| | - Suhail A Islam
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK
| | - Sophia F Mersmann
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK
| | - Léonie Strömich
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK
| | - Ilya A Vakser
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, KS 66045, USA
| | - Richard S Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, SM2 5NG, UK
| | - Michael J E Sternberg
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK
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18
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Hadarovich A, Anishchenko I, Tuzikov AV, Kundrotas PJ, Vakser IA. Gene ontology improves template selection in comparative protein docking. Proteins 2018; 87:245-253. [PMID: 30520123 DOI: 10.1002/prot.25645] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2018] [Revised: 10/21/2018] [Accepted: 11/29/2018] [Indexed: 02/06/2023]
Abstract
Structural characterization of protein-protein interactions is essential for our ability to study life processes at the molecular level. Computational modeling of protein complexes (protein docking) is important as the source of their structure and as a way to understand the principles of protein interaction. Rapidly evolving comparative docking approaches utilize target/template similarity metrics, which are often based on the protein structure. Although the structural similarity, generally, yields good performance, other characteristics of the interacting proteins (eg, function, biological process, and localization) may improve the prediction quality, especially in the case of weak target/template structural similarity. For the ranking of a pool of models for each target, we tested scoring functions that quantify similarity of Gene Ontology (GO) terms assigned to target and template proteins in three ontology domains-biological process, molecular function, and cellular component (GO-score). The scoring functions were tested in docking of bound, unbound, and modeled proteins. The results indicate that the combined structural and GO-terms functions improve the scoring, especially in the twilight zone of structural similarity, typical for protein models of limited accuracy.
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Affiliation(s)
- Anna Hadarovich
- Computational Biology Program, The University of Kansas, Lawrence, Kansas.,United Institute of Informatics Problems, National Academy of Sciences, Minsk, Belarus
| | - Ivan Anishchenko
- Computational Biology Program, The University of Kansas, Lawrence, Kansas
| | - Alexander V Tuzikov
- United Institute of Informatics Problems, National Academy of Sciences, Minsk, Belarus
| | - Petras J Kundrotas
- Computational Biology Program, The University of Kansas, Lawrence, Kansas
| | - Ilya A Vakser
- Computational Biology Program, The University of Kansas, Lawrence, Kansas.,Department of Molecular Biosciences, The University of Kansas, Kansas, Lawrence
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19
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Dauzhenka T, Kundrotas PJ, Vakser IA. Computational Feasibility of an Exhaustive Search of Side-Chain Conformations in Protein-Protein Docking. J Comput Chem 2018; 39:2012-2021. [PMID: 30226647 DOI: 10.1002/jcc.25381] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Revised: 03/24/2018] [Accepted: 05/26/2018] [Indexed: 11/07/2022]
Abstract
Protein-protein docking procedures typically perform the global scan of the proteins relative positions, followed by the local refinement of the putative matches. Because of the size of the search space, the global scan is usually implemented as rigid-body search, using computationally inexpensive intermolecular energy approximations. An adequate refinement has to take into account structural flexibility. Since the refinement performs conformational search of the interacting proteins, it is extremely computationally challenging, given the enormous amount of the internal degrees of freedom. Different approaches limit the search space by restricting the search to the side chains, rotameric states, coarse-grained structure representation, principal normal modes, and so on. Still, even with the approximations, the refinement presents an extreme computational challenge due to the very large number of the remaining degrees of freedom. Given the complexity of the search space, the advantage of the exhaustive search is obvious. The obstacle to such search is computational feasibility. However, the growing computational power of modern computers, especially due to the increasing utilization of Graphics Processing Unit (GPU) with large amount of specialized computing cores, extends the ranges of applicability of the brute-force search methods. This proof-of-concept study demonstrates computational feasibility of an exhaustive search of side-chain conformations in protein pocking. The procedure, implemented on the GPU architecture, was used to generate the optimal conformations in a large representative set of protein-protein complexes. © 2018 Wiley Periodicals, Inc.
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Affiliation(s)
- Taras Dauzhenka
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66047
| | - Petras J Kundrotas
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66047
| | - Ilya A Vakser
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66047.,Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, 66047
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20
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Anishchenko I, Kundrotas PJ, Vakser IA. Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model. Biophys J 2018; 115:809-821. [PMID: 30122295 DOI: 10.1016/j.bpj.2018.07.035] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 07/16/2018] [Accepted: 07/31/2018] [Indexed: 12/18/2022] Open
Abstract
The energy function is the key component of protein modeling methodology. This work presents a semianalytical approach to the development of contact potentials for protein structure modeling. Residue-residue and atom-atom contact energies were derived by maximizing the probability of observing native sequences in a nonredundant set of protein structures. The optimization task was formulated as an inverse statistical mechanics problem applied to the Potts model. Its solution by pseudolikelihood maximization provides consistent estimates of coupling constants at atomic and residue levels. The best performance was achieved when interacting atoms were grouped according to their physicochemical properties. For individual protein structures, the performance of the contact potentials in distinguishing near-native structures from the decoys is similar to the top-performing scoring functions. The potentials also yielded significant improvement in the protein docking success rates. The potentials recapitulated experimentally determined protein stability changes upon point mutations and protein-protein binding affinities. The approach offers a different perspective on knowledge-based potentials and may serve as the basis for their further development.
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Affiliation(s)
- Ivan Anishchenko
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas
| | - Petras J Kundrotas
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas.
| | - Ilya A Vakser
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas.
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21
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Badal VD, Kundrotas PJ, Vakser IA. Natural language processing in text mining for structural modeling of protein complexes. BMC Bioinformatics 2018; 19:84. [PMID: 29506465 PMCID: PMC5838950 DOI: 10.1186/s12859-018-2079-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Accepted: 02/20/2018] [Indexed: 12/04/2022] Open
Abstract
Background Structural modeling of protein-protein interactions produces a large number of putative configurations of the protein complexes. Identification of the near-native models among them is a serious challenge. Publicly available results of biomedical research may provide constraints on the binding mode, which can be essential for the docking. Our text-mining (TM) tool, which extracts binding site residues from the PubMed abstracts, was successfully applied to protein docking (Badal et al., PLoS Comput Biol, 2015; 11: e1004630). Still, many extracted residues were not relevant to the docking. Results We present an extension of the TM tool, which utilizes natural language processing (NLP) for analyzing the context of the residue occurrence. The procedure was tested using generic and specialized dictionaries. The results showed that the keyword dictionaries designed for identification of protein interactions are not adequate for the TM prediction of the binding mode. However, our dictionary designed to distinguish keywords relevant to the protein binding sites led to considerable improvement in the TM performance. We investigated the utility of several methods of context analysis, based on dissection of the sentence parse trees. The machine learning-based NLP filtered the pool of the mined residues significantly more efficiently than the rule-based NLP. Constraints generated by NLP were tested in docking of unbound proteins from the DOCKGROUND X-ray benchmark set 4. The output of the global low-resolution docking scan was post-processed, separately, by constraints from the basic TM, constraints re-ranked by NLP, and the reference constraints. The quality of a match was assessed by the interface root-mean-square deviation. The results showed significant improvement of the docking output when using the constraints generated by the advanced TM with NLP. Conclusions The basic TM procedure for extracting protein-protein binding site residues from the PubMed abstracts was significantly advanced by the deep parsing (NLP techniques for contextual analysis) in purging of the initial pool of the extracted residues. Benchmarking showed a substantial increase of the docking success rate based on the constraints generated by the advanced TM with NLP. Electronic supplementary material The online version of this article (10.1186/s12859-018-2079-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Varsha D Badal
- Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, 66047, USA
| | - Petras J Kundrotas
- Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, 66047, USA.
| | - Ilya A Vakser
- Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, 66047, USA.
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22
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Kundrotas PJ, Anishchenko I, Badal VD, Das M, Dauzhenka T, Vakser IA. Modeling CAPRI targets 110-120 by template-based and free docking using contact potential and combined scoring function. Proteins 2018; 86 Suppl 1:302-310. [PMID: 28905425 PMCID: PMC5820180 DOI: 10.1002/prot.25380] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2017] [Revised: 08/25/2017] [Accepted: 09/10/2017] [Indexed: 01/12/2023]
Abstract
The paper presents analysis of our template-based and free docking predictions in the joint CASP12/CAPRI37 round. A new scoring function for template-based docking was developed, benchmarked on the Dockground resource, and applied to the targets. The results showed that the function successfully discriminates the incorrect docking predictions. In correctly predicted targets, the scoring function was complemented by other considerations, such as consistency of the oligomeric states among templates, similarity of the biological functions, biological interface relevance, etc. The scoring function still does not distinguish well biological from crystal packing interfaces, and needs further development for the docking of bundles of α-helices. In the case of the trimeric targets, sequence-based methods did not find common templates, despite similarity of the structures, suggesting complementary use of structure- and sequence-based alignments in comparative docking. The results showed that if a good docking template is found, an accurate model of the interface can be built even from largely inaccurate models of individual subunits. Free docking however is very sensitive to the quality of the individual models. However, our newly developed contact potential detected approximate locations of the binding sites.
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Affiliation(s)
- Petras J. Kundrotas
- Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas 66045, USA
| | | | - Varsha D. Badal
- Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas 66045, USA
| | - Madhurima Das
- Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas 66045, USA
| | - Taras Dauzhenka
- Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas 66045, USA
| | - Ilya A. Vakser
- Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas 66045, USA
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23
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Dauzhenka T, Anishchenko I, Kundrotas PJ, Vakser IA. Relative Contribution of the Refinement Steps to the Protein-Protein Docking Success Rate. Biophys J 2018. [DOI: 10.1016/j.bpj.2017.11.3147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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24
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Kundrotas PJ, Anishchenko I, Dauzhenka T, Kotthoff I, Mnevets D, Copeland MM, Vakser IA. Dockground: A comprehensive data resource for modeling of protein complexes. Protein Sci 2017; 27:172-181. [PMID: 28891124 DOI: 10.1002/pro.3295] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2017] [Revised: 09/06/2017] [Accepted: 09/07/2017] [Indexed: 12/28/2022]
Abstract
Characterization of life processes at the molecular level requires structural details of protein interactions. The number of experimentally determined structures of protein-protein complexes accounts only for a fraction of known protein interactions. This gap in structural description of the interactome has to be bridged by modeling. An essential part of the development of structural modeling/docking techniques for protein interactions is databases of protein-protein complexes. They are necessary for studying protein interfaces, providing a knowledge base for docking algorithms, and developing intermolecular potentials, search procedures, and scoring functions. Development of protein-protein docking techniques requires thorough benchmarking of different parts of the docking protocols on carefully curated sets of protein-protein complexes. We present a comprehensive description of the Dockground resource (http://dockground.compbio.ku.edu) for structural modeling of protein interactions, including previously unpublished unbound docking benchmark set 4, and the X-ray docking decoy set 2. The resource offers a variety of interconnected datasets of protein-protein complexes and other data for the development and testing of different aspects of protein docking methodologies. Based on protein-protein complexes extracted from the PDB biounit files, Dockground offers sets of X-ray unbound, simulated unbound, model, and docking decoy structures. All datasets are freely available for download, as a whole or selecting specific structures, through a user-friendly interface on one integrated website.
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Affiliation(s)
- Petras J Kundrotas
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66045
| | - Ivan Anishchenko
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66045
| | - Taras Dauzhenka
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66045
| | - Ian Kotthoff
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66045
| | - Daniil Mnevets
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66045
| | - Matthew M Copeland
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66045
| | - Ilya A Vakser
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66045.,Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, 66045
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25
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Dauzhenka T, Anishchenko I, Kundrotas PJ, Vakser IA. Refinement of Protein Docking with Atom-Atom Contact Potentials, Backbone Flexibility and Side-Chain Repacking. Biophys J 2017. [DOI: 10.1016/j.bpj.2016.11.328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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26
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Anishchenko I, Kundrotas PJ, Vakser IA. Structural quality of unrefined models in protein docking. Proteins 2017; 85:39-45. [PMID: 27756103 PMCID: PMC5167671 DOI: 10.1002/prot.25188] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2016] [Revised: 09/29/2016] [Accepted: 10/11/2016] [Indexed: 11/11/2022]
Abstract
Structural characterization of protein-protein interactions is essential for understanding life processes at the molecular level. However, only a fraction of protein interactions have experimentally resolved structures. Thus, reliable computational methods for structural modeling of protein interactions (protein docking) are important for generating such structures and understanding the principles of protein recognition. Template-based docking techniques that utilize structural similarity between target protein-protein interaction and cocrystallized protein-protein complexes (templates) are gaining popularity due to generally higher reliability than that of the template-free docking. However, the template-based approach lacks explicit penalties for intermolecular penetration, as opposed to the typical free docking where such penalty is inherent due to the shape complementarity paradigm. Thus, template-based docking models are commonly assumed to require special treatment to remove large structural penetrations. In this study, we compared clashes in the template-based and free docking of the same proteins, with crystallographically determined and modeled structures. The results show that for the less accurate protein models, free docking produces fewer clashes than the template-based approach. However, contrary to the common expectation, in acceptable and better quality docking models of unbound crystallographically determined proteins, the clashes in the template-based docking are comparable to those in the free docking, due to the overall higher quality of the template-based docking predictions. This suggests that the free docking refinement protocols can in principle be applied to the template-based docking predictions as well. Proteins 2016; 85:39-45. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Ivan Anishchenko
- Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas 66047, USA
| | - Petras J. Kundrotas
- Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas 66047, USA
| | - Ilya A. Vakser
- Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas 66047, USA
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27
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Anishchenko I, Kundrotas PJ, Vakser IA. Modeling complexes of modeled proteins. Proteins 2016; 85:470-478. [PMID: 27701777 DOI: 10.1002/prot.25183] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2016] [Revised: 09/22/2016] [Accepted: 10/02/2016] [Indexed: 12/21/2022]
Abstract
Structural characterization of proteins is essential for understanding life processes at the molecular level. However, only a fraction of known proteins have experimentally determined structures. This fraction is even smaller for protein-protein complexes. Thus, structural modeling of protein-protein interactions (docking) primarily has to rely on modeled structures of the individual proteins, which typically are less accurate than the experimentally determined ones. Such "double" modeling is the Grand Challenge of structural reconstruction of the interactome. Yet it remains so far largely untested in a systematic way. We present a comprehensive validation of template-based and free docking on a set of 165 complexes, where each protein model has six levels of structural accuracy, from 1 to 6 Å Cα RMSD. Many template-based docking predictions fall into acceptable quality category, according to the CAPRI criteria, even for highly inaccurate proteins (5-6 Å RMSD), although the number of such models (and, consequently, the docking success rate) drops significantly for models with RMSD > 4 Å. The results show that the existing docking methodologies can be successfully applied to protein models with a broad range of structural accuracy, and the template-based docking is much less sensitive to inaccuracies of protein models than the free docking. Proteins 2017; 85:470-478. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Ivan Anishchenko
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66047, USA
| | - Petras J Kundrotas
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66047, USA
| | - Ilya A Vakser
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66047, USA.,Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, 66047, USA
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28
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Abstract
Protein-RNA complexes formed by specific recognition between RNA and RNA-binding proteins play an important role in biological processes. More than a thousand of such proteins in human are curated and many novel RNA-binding proteins are to be discovered. Due to limitations of experimental approaches, computational techniques are needed for characterization of protein-RNA interactions. Although much progress has been made, adequate methodologies reliably providing atomic resolution structural details are still lacking. Although protein-RNA free docking approaches proved to be useful, in general, the template-based approaches provide higher quality of predictions. Templates are key to building a high quality model. Sequence/structure relationships were studied based on a representative set of binary protein-RNA complexes from PDB. Several approaches were tested for pairwise target/template alignment. The analysis revealed a transition point between random and correct binding modes. The results showed that structural alignment is better than sequence alignment in identifying good templates, suitable for generating protein-RNA complexes close to the native structure, and outperforms free docking, successfully predicting complexes where the free docking fails, including cases of significant conformational change upon binding. A template-based protein-RNA interaction modeling protocol PRIME was developed and benchmarked on a representative set of complexes. Structures of protein-RNA complexes are important for characterization of biological processes. The number of experimentally determined protein-RNA complexes is limited. Thus modeling of these complexes is important. Reliable structural predictions of proteins and their complexes are provided by comparative modeling, which takes advantage of similar complexes with experimentally determined structures. Thus, in the case of protein-RNA complexes, it is important to determine if similar proteins and RNAs bind in a similar way. We show that, similarly to the earlier published results on protein-protein complexes, such correlation of the protein-RNA binding mode and the monomers similarity indeed exists, and is stronger when the similarity is determined by structure rather than sequence alignment. The data shows clear transition from random to similar binding mode with the increase of the structural similarity of the monomers. On the basis of the results we designed and implemented a predictive tool, which should be useful for the biological community interested in modeling of protein-RNA interactions.
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Affiliation(s)
- Jinfang Zheng
- School of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Petras J. Kundrotas
- Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, United States of America
| | - Ilya A. Vakser
- Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, United States of America
- * E-mail: (IAV); (SL)
| | - Shiyong Liu
- School of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan, Hubei, China
- * E-mail: (IAV); (SL)
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29
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Lensink MF, Velankar S, Kryshtafovych A, Huang SY, Schneidman-Duhovny D, Sali A, Segura J, Fernandez-Fuentes N, Viswanath S, Elber R, Grudinin S, Popov P, Neveu E, Lee H, Baek M, Park S, Heo L, Rie Lee G, Seok C, Qin S, Zhou HX, Ritchie DW, Maigret B, Devignes MD, Ghoorah A, Torchala M, Chaleil RAG, Bates PA, Ben-Zeev E, Eisenstein M, Negi SS, Weng Z, Vreven T, Pierce BG, Borrman TM, Yu J, Ochsenbein F, Guerois R, Vangone A, Rodrigues JPGLM, van Zundert G, Nellen M, Xue L, Karaca E, Melquiond ASJ, Visscher K, Kastritis PL, Bonvin AMJJ, Xu X, Qiu L, Yan C, Li J, Ma Z, Cheng J, Zou X, Shen Y, Peterson LX, Kim HR, Roy A, Han X, Esquivel-Rodriguez J, Kihara D, Yu X, Bruce NJ, Fuller JC, Wade RC, Anishchenko I, Kundrotas PJ, Vakser IA, Imai K, Yamada K, Oda T, Nakamura T, Tomii K, Pallara C, Romero-Durana M, Jiménez-García B, Moal IH, Férnandez-Recio J, Joung JY, Kim JY, Joo K, Lee J, Kozakov D, Vajda S, Mottarella S, Hall DR, Beglov D, Mamonov A, Xia B, Bohnuud T, Del Carpio CA, Ichiishi E, Marze N, Kuroda D, Roy Burman SS, Gray JJ, Chermak E, Cavallo L, Oliva R, Tovchigrechko A, Wodak SJ. Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment. Proteins 2016; 84 Suppl 1:323-48. [PMID: 27122118 PMCID: PMC5030136 DOI: 10.1002/prot.25007] [Citation(s) in RCA: 116] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2015] [Revised: 12/30/2015] [Accepted: 02/02/2016] [Indexed: 12/26/2022]
Abstract
We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein-protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. Proteins 2016; 84(Suppl 1):323-348. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Marc F Lensink
- University Lille, CNRS UMR8576 UGSF, Lille, F-59000, France.
| | - Sameer Velankar
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | | | - Shen-You Huang
- Research Support Computing, University of Missouri Bioinformatics Consortium, and Department of Computer Science, University of Missouri, Columbia, Missouri, 65211
| | - Dina Schneidman-Duhovny
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, 94158
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, 94158
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, 94158
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, 94158
- California Institute for Quantitative Biosciences (QB3), University of California San Francisco, San Francisco, California, 94158
| | - Joan Segura
- GN7 of the National Institute for Bioinformatics (INB) and Biocomputing Unit, National Center of Biotechnology (CSIC), Madrid, 28049, Spain
| | - Narcis Fernandez-Fuentes
- Institute of Biological, Environmental and Rural Sciences (IBERS), Aberystwyth University, Aberystwyth, SY233FG, United Kingdom
| | - Shruthi Viswanath
- Department of Computer Science, University of Texas at Austin, Austin, Texas, 78712
- Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas, 78712
| | - Ron Elber
- Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas, 78712
- Department of Chemistry, University of Texas at Austin, Austin, Texas, 78712
| | - Sergei Grudinin
- LJK, University Grenoble Alpes, CNRS, Grenoble, 38000, France
- INRIA, Grenoble, 38000, France
| | - Petr Popov
- LJK, University Grenoble Alpes, CNRS, Grenoble, 38000, France
- INRIA, Grenoble, 38000, France
- Moscow Institute of Physics and Technology, Dolgoprudniy, Russia
| | - Emilie Neveu
- LJK, University Grenoble Alpes, CNRS, Grenoble, 38000, France
- INRIA, Grenoble, 38000, France
| | - Hasup Lee
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea
| | - Minkyung Baek
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea
| | - Sangwoo Park
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea
| | - Lim Heo
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea
| | - Gyu Rie Lee
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea
| | - Sanbo Qin
- Department of Physics and Institute of Molecular Biophysics, Florida State University, Tallahassee, Florida, 32306, USA
| | - Huan-Xiang Zhou
- Department of Physics and Institute of Molecular Biophysics, Florida State University, Tallahassee, Florida, 32306, USA
| | | | - Bernard Maigret
- CNRS, LORIA, Campus Scientifique, BP 239, Vandœuvre-lès-Nancy, 54506, France
| | | | - Anisah Ghoorah
- Department of Computer Science and Engineering, University of Mauritius, Reduit, Mauritius
| | - Mieczyslaw Torchala
- Biomolecular Modelling Laboratory, the Francis Crick Institute, Lincoln's Inn Fields Laboratory, London, WC2A 3LY, United Kingdom
| | - Raphaël A G Chaleil
- Biomolecular Modelling Laboratory, the Francis Crick Institute, Lincoln's Inn Fields Laboratory, London, WC2A 3LY, United Kingdom
| | - Paul A Bates
- Biomolecular Modelling Laboratory, the Francis Crick Institute, Lincoln's Inn Fields Laboratory, London, WC2A 3LY, United Kingdom
| | - Efrat Ben-Zeev
- G-INCPM, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Miriam Eisenstein
- Department of Chemical Research Support, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Surendra S Negi
- Sealy Center for Structural Biology and Molecular Biophysics, University of Texas Medical Branch, 301 University Boulevard, Galveston, Texas, 77555-0857
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts, 01605
| | - Thom Vreven
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts, 01605
| | - Brian G Pierce
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts, 01605
| | - Tyler M Borrman
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts, 01605
| | - Jinchao Yu
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, University Paris-Saclay, CEA-Saclay, Gif-sur-Yvette, 91191, France
| | - Françoise Ochsenbein
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, University Paris-Saclay, CEA-Saclay, Gif-sur-Yvette, 91191, France
| | - Raphaël Guerois
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, University Paris-Saclay, CEA-Saclay, Gif-sur-Yvette, 91191, France
| | - Anna Vangone
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - João P G L M Rodrigues
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Gydo van Zundert
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Mehdi Nellen
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Li Xue
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Ezgi Karaca
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Adrien S J Melquiond
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Koen Visscher
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Panagiotis L Kastritis
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Alexandre M J J Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Xianjin Xu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, 65211
| | - Liming Qiu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, 65211
| | - Chengfei Yan
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, 65211
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri, 65211
| | - Jilong Li
- Department of Computer Science, University of Missouri, Columbia, Missouri, 65211
| | - Zhiwei Ma
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, 65211
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri, 65211
| | - Jianlin Cheng
- Department of Computer Science, University of Missouri, Columbia, Missouri, 65211
- Informatics Institute, University of Missouri, Columbia, Missouri, 65211
| | - Xiaoqin Zou
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, 65211
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri, 65211
- Informatics Institute, University of Missouri, Columbia, Missouri, 65211
- Department of Biochemistry, University of Missouri, Columbia, Missouri, 65211
| | - Yang Shen
- Toyota Technological Institute at Chicago, 6045 S Kenwood Avenue, Chicago, Illinois, 60637
| | - Lenna X Peterson
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907
| | - Hyung-Rae Kim
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907
| | - Amit Roy
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907
- Bioinformatics and Computational Biosciences Branch, Rocky Mountain Laboratories, National Institutes of Health, Hamilton, Montano 59840
| | - Xusi Han
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907
| | | | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907
- Department of Computer Science, Purdue University, West Lafayette, IN, USA, 47907
| | - Xiaofeng Yu
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Neil J Bruce
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Jonathan C Fuller
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Rebecca C Wade
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
- Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, Heidelberg, Germany
- Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
| | - Ivan Anishchenko
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66047
| | - Petras J Kundrotas
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66047
| | - Ilya A Vakser
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66047
- Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, 66047
| | - Kenichiro Imai
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Koto-Ku, Japan
| | - Kazunori Yamada
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Koto-Ku, Japan
| | - Toshiyuki Oda
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Koto-Ku, Japan
| | - Tsukasa Nakamura
- Graduate School of Frontier Sciences, the University of Tokyo, Kashiwa, Japan
| | - Kentaro Tomii
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Koto-Ku, Japan
- Graduate School of Frontier Sciences, the University of Tokyo, Kashiwa, Japan
| | - Chiara Pallara
- Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, C/Jordi Girona 29, Barcelona, 08034, Spain
| | - Miguel Romero-Durana
- Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, C/Jordi Girona 29, Barcelona, 08034, Spain
| | - Brian Jiménez-García
- Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, C/Jordi Girona 29, Barcelona, 08034, Spain
| | - Iain H Moal
- Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, C/Jordi Girona 29, Barcelona, 08034, Spain
| | - Juan Férnandez-Recio
- Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, C/Jordi Girona 29, Barcelona, 08034, Spain
| | - Jong Young Joung
- Center for in-Silico Protein Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea
| | - Jong Yun Kim
- Center for in-Silico Protein Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea
| | - Keehyoung Joo
- Center for in-Silico Protein Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea
- Center for Advanced Computation, Korea Institute for Advanced Study, Seoul, 130-722, Korea
| | - Jooyoung Lee
- Center for in-Silico Protein Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea
- School of Computational Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea
| | - Dima Kozakov
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
- Department of Chemistry, Boston University, Boston, Massachusetts
| | - Scott Mottarella
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - David R Hall
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Artem Mamonov
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Bing Xia
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Tanggis Bohnuud
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Carlos A Del Carpio
- Institute of Biological Diversity, International Pacific Institute of Indiana, Bloomington, Indiana, 47401
- Drosophila Genetic Resource Center, Kyoto Institute of Technology, Ukyo-Ku, 616-8354, Japan
| | - Eichiro Ichiishi
- International University of Health and Welfare Hospital (IUHW Hospital), Asushiobara-City, Tochigi Prefecture, 329-2763, Japan
| | - Nicholas Marze
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, 21218
| | - Daisuke Kuroda
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, 21218
| | - Shourya S Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, 21218
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, 21218
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland, 21218
| | - Edrisse Chermak
- King Abdullah University of Science and Technology, Saudi Arabia
| | - Luigi Cavallo
- King Abdullah University of Science and Technology, Saudi Arabia
| | - Romina Oliva
- University of Naples "Parthenope", Napoli, Italy
| | - Andrey Tovchigrechko
- J. Craig Venter Institute, 9704 Medical Center Drive, Rockville, Maryland, 20850
| | - Shoshana J Wodak
- Departments of Biochemistry and Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
- VIB Structural Biology Research Center, VUB Pleinlaan 2, Brussels, 1050, Belgium.
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Im W, Liang J, Olson A, Zhou HX, Vajda S, Vakser IA. Challenges in structural approaches to cell modeling. J Mol Biol 2016; 428:2943-64. [PMID: 27255863 PMCID: PMC4976022 DOI: 10.1016/j.jmb.2016.05.024] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2016] [Revised: 05/19/2016] [Accepted: 05/24/2016] [Indexed: 11/17/2022]
Abstract
Computational modeling is essential for structural characterization of biomolecular mechanisms across the broad spectrum of scales. Adequate understanding of biomolecular mechanisms inherently involves our ability to model them. Structural modeling of individual biomolecules and their interactions has been rapidly progressing. However, in terms of the broader picture, the focus is shifting toward larger systems, up to the level of a cell. Such modeling involves a more dynamic and realistic representation of the interactomes in vivo, in a crowded cellular environment, as well as membranes and membrane proteins, and other cellular components. Structural modeling of a cell complements computational approaches to cellular mechanisms based on differential equations, graph models, and other techniques to model biological networks, imaging data, etc. Structural modeling along with other computational and experimental approaches will provide a fundamental understanding of life at the molecular level and lead to important applications to biology and medicine. A cross section of diverse approaches presented in this review illustrates the developing shift from the structural modeling of individual molecules to that of cell biology. Studies in several related areas are covered: biological networks; automated construction of three-dimensional cell models using experimental data; modeling of protein complexes; prediction of non-specific and transient protein interactions; thermodynamic and kinetic effects of crowding; cellular membrane modeling; and modeling of chromosomes. The review presents an expert opinion on the current state-of-the-art in these various aspects of structural modeling in cellular biology, and the prospects of future developments in this emerging field.
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Affiliation(s)
- Wonpil Im
- Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, KS 66047, United States.
| | - Jie Liang
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, United States.
| | - Arthur Olson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, United States.
| | - Huan-Xiang Zhou
- Department of Physics and Institute of Molecular Biophysics, Florida State University, Tallahassee, FL 32306, United States.
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, United States.
| | - Ilya A Vakser
- Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, KS 66047, United States.
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Anishchenko I, Badal V, Dauzhenka T, Das M, Tuzikov AV, Kundrotas PJ, Vakser IA. Genome-Wide Structural Modeling of Protein-Protein Interactions. Bioinformatics Research and Applications 2016. [DOI: 10.1007/978-3-319-38782-6_8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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Abstract
The rapidly growing amount of publicly available information from biomedical research is readily accessible on the Internet, providing a powerful resource for predictive biomolecular modeling. The accumulated data on experimentally determined structures transformed structure prediction of proteins and protein complexes. Instead of exploring the enormous search space, predictive tools can simply proceed to the solution based on similarity to the existing, previously determined structures. A similar major paradigm shift is emerging due to the rapidly expanding amount of information, other than experimentally determined structures, which still can be used as constraints in biomolecular structure prediction. Automated text mining has been widely used in recreating protein interaction networks, as well as in detecting small ligand binding sites on protein structures. Combining and expanding these two well-developed areas of research, we applied the text mining to structural modeling of protein-protein complexes (protein docking). Protein docking can be significantly improved when constraints on the docking mode are available. We developed a procedure that retrieves published abstracts on a specific protein-protein interaction and extracts information relevant to docking. The procedure was assessed on protein complexes from Dockground (http://dockground.compbio.ku.edu). The results show that correct information on binding residues can be extracted for about half of the complexes. The amount of irrelevant information was reduced by conceptual analysis of a subset of the retrieved abstracts, based on the bag-of-words (features) approach. Support Vector Machine models were trained and validated on the subset. The remaining abstracts were filtered by the best-performing models, which decreased the irrelevant information for ~ 25% complexes in the dataset. The extracted constraints were incorporated in the docking protocol and tested on the Dockground unbound benchmark set, significantly increasing the docking success rate. Protein interactions are central for many cellular processes. Physical characterization of these interactions is essential for understanding of life processes and applications in biology and medicine. Because of the inherent limitations of experimental techniques and rapid development of computational power and methodology, computer modeling is a tool of choice in many studies. Publicly available information from biomedical research is readily accessible on the Internet, providing a powerful resource for modeling of proteins and protein complexes. A major paradigm shift in modeling of protein complexes is emerging due to the rapidly expanding amount of such information, which can be used as modeling constraints. Text mining has been widely used in recreating networks of protein interactions, as well as in detecting small molecule binding sites on proteins. Combining and expanding these two well-developed areas of research, we applied the text mining to physical modeling of protein complexes (protein docking). Our procedure retrieves published abstracts on a protein-protein interaction and extracts the relevant information. The results show that correct information on binding can be obtained for about half of protein complexes. The extracted constraints were incorporated in a modeling procedure, significantly improving its performance.
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Affiliation(s)
- Varsha D. Badal
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, United States of America
| | - Petras J. Kundrotas
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, United States of America
- * E-mail: (IAV); (PJK)
| | - Ilya A. Vakser
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, United States of America
- Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, United States of America
- * E-mail: (IAV); (PJK)
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Kirys T, Ruvinsky AM, Singla D, Tuzikov AV, Kundrotas PJ, Vakser IA. Simulated unbound structures for benchmarking of protein docking in the DOCKGROUND resource. BMC Bioinformatics 2015; 16:243. [PMID: 26227548 PMCID: PMC4521349 DOI: 10.1186/s12859-015-0672-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2015] [Accepted: 07/10/2015] [Indexed: 11/10/2022] Open
Abstract
Background Proteins play an important role in biological processes in living organisms. Many protein functions are based on interaction with other proteins. The structural information is important for adequate description of these interactions. Sets of protein structures determined in both bound and unbound states are essential for benchmarking of the docking procedures. However, the number of such proteins in PDB is relatively small. A radical expansion of such sets is possible if the unbound structures are computationally simulated. Results The Dockground public resource provides data to improve our understanding of protein–protein interactions and to assist in the development of better tools for structural modeling of protein complexes, such as docking algorithms and scoring functions. A large set of simulated unbound protein structures was generated from the bound structures. The modeling protocol was based on 1 ns Langevin dynamics simulation. The simulated structures were validated on the ensemble of experimentally determined unbound and bound structures. The set is intended for large scale benchmarking of docking algorithms and scoring functions. Conclusions A radical expansion of the unbound protein docking benchmark set was achieved by simulating the unbound structures. The simulated unbound structures were selected according to criteria from systematic comparison of experimentally determined bound and unbound structures. The set is publicly available at http://dockground.compbio.ku.edu.
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Affiliation(s)
- Tatsiana Kirys
- Center for Computational Biology, The University of Kansas, Lawrence, KS, 66047, USA. .,United Institute of Informatics Problems, National Academy of Sciences, 220012, Minsk, Belarus.
| | - Anatoly M Ruvinsky
- Center for Computational Biology, The University of Kansas, Lawrence, KS, 66047, USA. .,Schrödinger, Inc., Cambridge, MA, 02142, USA.
| | - Deepak Singla
- Center for Computational Biology, The University of Kansas, Lawrence, KS, 66047, USA.
| | - Alexander V Tuzikov
- United Institute of Informatics Problems, National Academy of Sciences, 220012, Minsk, Belarus.
| | - Petras J Kundrotas
- Center for Computational Biology, The University of Kansas, Lawrence, KS, 66047, USA.
| | - Ilya A Vakser
- Center for Computational Biology, The University of Kansas, Lawrence, KS, 66047, USA. .,Department of Molecular Biosciences, The University of Kansas, Lawrence, KS, 66045, USA.
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Vakser IA. Protein-protein docking: from interaction to interactome. Biophys J 2015; 107:1785-1793. [PMID: 25418159 DOI: 10.1016/j.bpj.2014.08.033] [Citation(s) in RCA: 177] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2014] [Revised: 08/17/2014] [Accepted: 08/27/2014] [Indexed: 12/29/2022] Open
Abstract
The protein-protein docking problem is one of the focal points of activity in computational biophysics and structural biology. The three-dimensional structure of a protein-protein complex, generally, is more difficult to determine experimentally than the structure of an individual protein. Adequate computational techniques to model protein interactions are important because of the growing number of known protein structures, particularly in the context of structural genomics. Docking offers tools for fundamental studies of protein interactions and provides a structural basis for drug design. Protein-protein docking is the prediction of the structure of the complex, given the structures of the individual proteins. In the heart of the docking methodology is the notion of steric and physicochemical complementarity at the protein-protein interface. Originally, mostly high-resolution, experimentally determined (primarily by x-ray crystallography) protein structures were considered for docking. However, more recently, the focus has been shifting toward lower-resolution modeled structures. Docking approaches have to deal with the conformational changes between unbound and bound structures, as well as the inaccuracies of the interacting modeled structures, often in a high-throughput mode needed for modeling of large networks of protein interactions. The growing number of docking developers is engaged in the community-wide assessments of predictive methodologies. The development of more powerful and adequate docking approaches is facilitated by rapidly expanding information and data resources, growing computational capabilities, and a deeper understanding of the fundamental principles of protein interactions.
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Affiliation(s)
- Ilya A Vakser
- Center for Bioinformatics and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas.
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Anishchenko I, Kundrotas PJ, Tuzikov AV, Vakser IA. Structural templates for comparative protein docking. Proteins 2015; 83:1563-70. [PMID: 25488330 DOI: 10.1002/prot.24736] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2014] [Revised: 11/15/2014] [Accepted: 11/26/2014] [Indexed: 11/07/2022]
Abstract
Structural characterization of protein-protein interactions is important for understanding life processes. Because of the inherent limitations of experimental techniques, such characterization requires computational approaches. Along with the traditional protein-protein docking (free search for a match between two proteins), comparative (template-based) modeling of protein-protein complexes has been gaining popularity. Its development puts an emphasis on full and partial structural similarity between the target protein monomers and the protein-protein complexes previously determined by experimental techniques (templates). The template-based docking relies on the quality and diversity of the template set. We present a carefully curated, nonredundant library of templates containing 4950 full structures of binary complexes and 5936 protein-protein interfaces extracted from the full structures at 12 Å distance cut-off. Redundancy in the libraries was removed by clustering the PDB structures based on structural similarity. The value of the clustering threshold was determined from the analysis of the clusters and the docking performance on a benchmark set. High structural quality of the interfaces in the template and validation sets was achieved by automated procedures and manual curation. The library is included in the Dockground resource for molecular recognition studies at http://dockground.bioinformatics.ku.edu.
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Affiliation(s)
- Ivan Anishchenko
- Center for Bioinformatics, The University of Kansas, Lawrence, Kansas, 66047.,United Institute of Informatics Problems, National Academy of Sciences, Minsk, 220012, Belarus
| | - Petras J Kundrotas
- Center for Bioinformatics, The University of Kansas, Lawrence, Kansas, 66047
| | - Alexander V Tuzikov
- United Institute of Informatics Problems, National Academy of Sciences, Minsk, 220012, Belarus
| | - Ilya A Vakser
- Center for Bioinformatics, The University of Kansas, Lawrence, Kansas, 66047.,Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, 66045
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36
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Anishchenko I, Kundrotas PJ, Tuzikov AV, Vakser IA. Protein models docking benchmark 2. Proteins 2015; 83:891-7. [PMID: 25712716 DOI: 10.1002/prot.24784] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2014] [Revised: 01/30/2015] [Accepted: 02/14/2015] [Indexed: 12/28/2022]
Abstract
Structural characterization of protein-protein interactions is essential for our ability to understand life processes. However, only a fraction of known proteins have experimentally determined structures. Such structures provide templates for modeling of a large part of the proteome, where individual proteins can be docked by template-free or template-based techniques. Still, the sensitivity of the docking methods to the inherent inaccuracies of protein models, as opposed to the experimentally determined high-resolution structures, remains largely untested, primarily due to the absence of appropriate benchmark set(s). Structures in such a set should have predefined inaccuracy levels and, at the same time, resemble actual protein models in terms of structural motifs/packing. The set should also be large enough to ensure statistical reliability of the benchmarking results. We present a major update of the previously developed benchmark set of protein models. For each interactor, six models were generated with the model-to-native C(α) RMSD in the 1 to 6 Å range. The models in the set were generated by a new approach, which corresponds to the actual modeling of new protein structures in the "real case scenario," as opposed to the previous set, where a significant number of structures were model-like only. In addition, the larger number of complexes (165 vs. 63 in the previous set) increases the statistical reliability of the benchmarking. We estimated the highest accuracy of the predicted complexes (according to CAPRI criteria), which can be attained using the benchmark structures. The set is available at http://dockground.bioinformatics.ku.edu.
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Affiliation(s)
- Ivan Anishchenko
- Center for Bioinformatics, The University of Kansas, Lawrence, Kansas, 66047; United Institute of Informatics Problems, National Academy of Sciences, Minsk, 220012, Belarus
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Ruvinsky AM, Vakser IA, Rivera M. Local packing modulates diversity of iron pathways and cooperative behavior in eukaryotic and prokaryotic ferritins. J Chem Phys 2014; 140:115104. [PMID: 24655206 DOI: 10.1063/1.4868229] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Ferritin-like molecules show a remarkable combination of the evolutionary conserved activity of iron uptake and release that engage different pores in the conserved ferritin shell. It was hypothesized that pore selection and iron traffic depend on dynamic allostery with no conformational changes in the backbone. In this study, we detect the allosteric networks in Pseudomonas aeruginosa bacterioferritin (BfrB), bacterial ferritin (FtnA), and bullfrog M and L ferritins (Ftns) by a network-weaving algorithm (NWA) that passes threads of an allosteric network through highly correlated residues using hierarchical clustering. The residue-residue correlations are calculated in the packing-on elastic network model that introduces atom packing into the common packing-off model. Applying NWA revealed that each of the molecules has an extended allosteric network mostly buried inside the ferritin shell. The structure of the networks is consistent with experimental observations of iron transport: The allosteric networks in BfrB and FtnA connect the ferroxidase center with the 4-fold pores and B-pores, leaving the 3-fold pores unengaged. In contrast, the allosteric network directly links the 3-fold pores with the 4-fold pores in M and L Ftns. The majority of the network residues are either on the inner surface or buried inside the subunit fold or at the subunit interfaces. We hypothesize that the ferritin structures evolved in a way to limit the influence of functionally unrelated events in the cytoplasm on the allosteric network to maintain stability of the translocation mechanisms. We showed that the residue-residue correlations and the resultant long-range cooperativity depend on the ferritin shell packing, which, in turn, depends on protein sequence composition. Switching from the packing-on to the packing-off model reduces correlations by 35%-38% so that no allosteric network can be found. The influence of the side-chain packing on the allosteric networks explains the diversity in mechanisms of iron traffic suggested by experimental approaches.
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Affiliation(s)
- Anatoly M Ruvinsky
- Infection Innovative Medicine, AstraZeneca R&D Boston, 35 Gatehouse Drive, Waltham, Massachusetts 02451, USA
| | - Ilya A Vakser
- Center for Bioinformatics, The University of Kansas, Lawrence, Kansas 66047, USA
| | - Mario Rivera
- Department of Chemistry, The University of Kansas, Lawrence, Kansas 66047, USA
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Lensink MF, Moal IH, Bates PA, Kastritis PL, Melquiond ASJ, Karaca E, Schmitz C, van Dijk M, Bonvin AMJJ, Eisenstein M, Jiménez-García B, Grosdidier S, Solernou A, Pérez-Cano L, Pallara C, Fernández-Recio J, Xu J, Muthu P, Praneeth Kilambi K, Gray JJ, Grudinin S, Derevyanko G, Mitchell JC, Wieting J, Kanamori E, Tsuchiya Y, Murakami Y, Sarmiento J, Standley DM, Shirota M, Kinoshita K, Nakamura H, Chavent M, Ritchie DW, Park H, Ko J, Lee H, Seok C, Shen Y, Kozakov D, Vajda S, Kundrotas PJ, Vakser IA, Pierce BG, Hwang H, Vreven T, Weng Z, Buch I, Farkash E, Wolfson HJ, Zacharias M, Qin S, Zhou HX, Huang SY, Zou X, Wojdyla JA, Kleanthous C, Wodak SJ. Blind prediction of interfacial water positions in CAPRI. Proteins 2014; 82:620-32. [PMID: 24155158 PMCID: PMC4582081 DOI: 10.1002/prot.24439] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2013] [Revised: 09/16/2013] [Accepted: 09/26/2013] [Indexed: 12/30/2022]
Abstract
We report the first assessment of blind predictions of water positions at protein-protein interfaces, performed as part of the critical assessment of predicted interactions (CAPRI) community-wide experiment. Groups submitting docking predictions for the complex of the DNase domain of colicin E2 and Im2 immunity protein (CAPRI Target 47), were invited to predict the positions of interfacial water molecules using the method of their choice. The predictions-20 groups submitted a total of 195 models-were assessed by measuring the recall fraction of water-mediated protein contacts. Of the 176 high- or medium-quality docking models-a very good docking performance per se-only 44% had a recall fraction above 0.3, and a mere 6% above 0.5. The actual water positions were in general predicted to an accuracy level no better than 1.5 Å, and even in good models about half of the contacts represented false positives. This notwithstanding, three hotspot interface water positions were quite well predicted, and so was one of the water positions that is believed to stabilize the loop that confers specificity in these complexes. Overall the best interface water predictions was achieved by groups that also produced high-quality docking models, indicating that accurate modelling of the protein portion is a determinant factor. The use of established molecular mechanics force fields, coupled to sampling and optimization procedures also seemed to confer an advantage. Insights gained from this analysis should help improve the prediction of protein-water interactions and their role in stabilizing protein complexes.
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Affiliation(s)
- Marc F Lensink
- Interdisciplinary Research Institute USR3078 CNRS, University Lille North of France, Villeneuve d'Ascq, France
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Kundrotas PJ, Vakser IA. Global and local structural similarity in protein-protein complexes: implications for template-based docking. Proteins 2013; 81:2137-42. [PMID: 23946125 DOI: 10.1002/prot.24392] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2013] [Revised: 07/23/2013] [Accepted: 08/02/2013] [Indexed: 02/02/2023]
Abstract
The increasing amount of structural information on protein-protein interactions makes it possible to predict the structure of protein-protein complexes by comparison/alignment of the interacting proteins to the ones in cocrystallized complexes. In the predictions based on structure similarity, the template search is performed by structural alignment of the target interactors with the entire structures or with the interface only of the subunits in cocrystallized complexes. This study investigates the scope of the structural similarity that facilitates the detection of a broad range of templates significantly divergent from the targets. The analysis of the target-template similarity is based on models of protein-protein complexes in a large representative set of heterodimers. The similarity of the biological and crystal packing interfaces, dissimilar interface structural motifs in overall similar structures, interface similarity to the full structure, and local similarity away from the interface were analyzed. The structural similarity at the protein-protein interfaces only was observed in ~25% of target-template pairs with sequence identity <20% and primarily homodimeric templates. For ~50% of the target-template pairs, the similarity at the interface was accompanied by the similarity of the whole structure. However, the structural similarity at the interfaces was still stronger than that of the noninterface parts. The study provides insights into structural and functional diversity of protein-protein complexes, and relative performance of the interface and full structure alignment in docking.
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Anishchenko I, Kundrotas PJ, Tuzikov AV, Vakser IA. Protein models: the Grand Challenge of protein docking. Proteins 2013; 82:278-87. [PMID: 23934791 DOI: 10.1002/prot.24385] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2013] [Revised: 07/16/2013] [Accepted: 07/26/2013] [Indexed: 12/28/2022]
Abstract
Characterization of life processes at the molecular level requires structural details of protein-protein interactions (PPIs). The number of experimentally determined protein structures accounts only for a fraction of known proteins. This gap has to be bridged by modeling, typically using experimentally determined structures as templates to model related proteins. The fraction of experimentally determined PPI structures is even smaller than that for the individual proteins, due to a larger number of interactions than the number of individual proteins, and a greater difficulty of crystallizing protein-protein complexes. The approaches to structural modeling of PPI (docking) often have to rely on modeled structures of the interactors, especially in the case of large PPI networks. Structures of modeled proteins are typically less accurate than the ones determined by X-ray crystallography or nuclear magnetic resonance. Thus the utility of approaches to dock these structures should be assessed by thorough benchmarking, specifically designed for protein models. To be credible, such benchmarking has to be based on carefully curated sets of structures with levels of distortion typical for modeled proteins. This article presents such a suite of models built for the benchmark set of the X-ray structures from the Dockground resource (http://dockground.bioinformatics.ku.edu) by a combination of homology modeling and Nudged Elastic Band method. For each monomer, six models were generated with predefined C(α) root mean square deviation from the native structure (1, 2, …, 6 Å). The sets and the accompanying data provide a comprehensive resource for the development of docking methodology for modeled proteins.
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Affiliation(s)
- Ivan Anishchenko
- Center for Bioinformatics, The University of Kansas, Lawrence, Kansas, 66047; United Institute of Informatics Problems, National Academy of Sciences, 220012, Minsk, Belarus
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Kundrotas PJ, Vakser IA, Janin J. Structural templates for modeling homodimers. Protein Sci 2013; 22:1655-63. [PMID: 23996787 DOI: 10.1002/pro.2361] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2013] [Revised: 08/23/2013] [Accepted: 08/23/2013] [Indexed: 12/17/2022]
Abstract
Oligomeric proteins are more abundant in nature than monomeric proteins, and involved in all biological processes. In the absence of an experimental structure, their subunits can be modeled from their sequence like monomeric proteins, but reliable procedures to build the oligomeric assembly are scarce. Template-based methods, which start from known protein structures, are commonly applied to model subunits. We present a method to model homodimers that relies on a structural alignment of the subunits, and test it on a set of 511 target structures recently released by the Protein Data Bank, taking as templates the earlier released structures of 3108 homodimeric proteins (H-set), and 2691 monomeric proteins that form dimer-like assemblies in crystals (M-set). The structural alignment identifies a H-set template for 97% of the targets, and in half of the cases, it yields a correct model of the dimer geometry and residue-residue contacts in the target. It also identifies a M-set template for most of the targets, and some of the crystal dimers are very similar to the target homodimers. The procedure efficiently detects homology at low levels of sequence identities, and points to erroneous quaternary structures in the Protein Data Bank. The high coverage of the target set suggests that the content of the Protein Data Bank already approaches the structural diversity of protein assemblies in nature, and that template-based methods should become the choice method for modeling oligomeric as well as monomeric proteins.
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Affiliation(s)
- Petras J Kundrotas
- Center for Bioinformatics, The University of Kansas, 2030 Becker Dr., Lawrence, Kansas, 66047
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Kundrotas PJ, Vakser IA. Protein-protein alternative binding modes do not overlap. Protein Sci 2013; 22:1141-5. [PMID: 23775945 DOI: 10.1002/pro.2295] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2013] [Revised: 06/01/2013] [Accepted: 06/03/2013] [Indexed: 11/09/2022]
Abstract
Proteins often bind other proteins in more than one way. Thus alternative binding modes is an essential feature of protein interactions. Such binding modes may be detected by X-ray crystallography and thus reflected in Protein Data Bank. The alternative binding is often observed not for the protein itself but for its structural homolog. The results of this study based on the analysis of a comprehensive set of co-crystallized protein-protein complexes show that the alternative binding modes generally do not overlap, but are spatially separated. This effect is based on molecular recognition characteristics of the protein structures. The results are also in excellent agreement with the intermolecular energy funnel size estimates obtained previously by an independent methodology. The results provide an important insight into the principles of protein association, as well as potential guidelines for modeling of protein complexes and the design of protein interfaces.
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Affiliation(s)
- Petras J Kundrotas
- Center for Bioinformatics and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas 66047, USA
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Ruvinsky AM, Kirys T, Tuzikov AV, Vakser IA. Ensemble-based characterization of unbound and bound states on protein energy landscape. Protein Sci 2013; 22:734-44. [PMID: 23526684 DOI: 10.1002/pro.2256] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2012] [Revised: 02/02/2013] [Accepted: 03/15/2013] [Indexed: 11/07/2022]
Abstract
Physicochemical description of numerous cell processes is fundamentally based on the energy landscapes of protein molecules involved. Although the whole energy landscape is difficult to reconstruct, increased attention to particular targets has provided enough structures for mapping functionally important subspaces associated with the unbound and bound protein structures. The subspace mapping produces a discrete representation of the landscape, further called energy spectrum. We compiled and characterized ensembles of bound and unbound conformations of six small proteins and explored their spectra in implicit solvent. First, the analysis of the unbound-to-bound changes points to conformational selection as the binding mechanism for four proteins. Second, results show that bound and unbound spectra often significantly overlap. Moreover, the larger the overlap the smaller the root mean square deviation (RMSD) between the bound and unbound conformational ensembles. Third, the center of the unbound spectrum has a higher energy than the center of the corresponding bound spectrum of the dimeric and multimeric states for most of the proteins. This suggests that the unbound states often have larger entropy than the bound states. Fourth, the exhaustively long minimization, making small intrarotamer adjustments (all-atom RMSD ≤ 0.7 Å), dramatically reduces the distance between the centers of the bound and unbound spectra as well as the spectra extent. It condenses unbound and bound energy levels into a thin layer at the bottom of the energy landscape with the energy spacing that varies between 0.8-4.6 and 3.5-10.5 kcal/mol for the unbound and bound states correspondingly. Finally, the analysis of protein energy fluctuations showed that protein vibrations itself can excite the interstate transitions, including the unbound-to-bound ones.
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Affiliation(s)
- Anatoly M Ruvinsky
- Center for Bioinformatics, The University of Kansas, Lawrence, Kansas 66047, USA.
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Abstract
Structure fluctuations and conformational changes accompany all biological processes involving macromolecules. The paper presents a classification of protein residues based on the normalized equilibrium fluctuations of the residue centers of mass in proteins and a statistical analysis of conformation changes in the side-chains upon binding. Normal mode analysis and an elastic network model were applied to a set of protein complexes to calculate the residue fluctuations and develop the residue classification. Comparison with a classification based on normalized B-factors suggests that the B-factors may underestimate protein flexibility in solvent. Our classification shows that protein loops and disordered fragments are enriched with highly fluctuating residues and depleted with weakly fluctuating residues. Strategies for engineering thermostable proteins are discussed. To calculate the dihedral angles distribution functions, the configuration space was divided into cells by a cubic grid. The effect of protein association on the distribution functions depends on the amino acid type and a grid step in the dihedral angles space. The changes in the dihedral angles increase from the near-backbone dihedral angle to the most distant one, for most residues. On average, one fifth of the interface residues change the rotamer state upon binding, whereas the rest of the interface residues undergo local readjustments within the same rotamer.
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Affiliation(s)
- Anatoly M Ruvinsky
- Center for Bioinformatics, University of Kansas, Lawrence, KS 66047, USA.
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Kirys T, Ruvinsky AM, Tuzikov AV, Vakser IA. Correlation analysis of the side-chains conformational distribution in bound and unbound proteins. BMC Bioinformatics 2012; 13:236. [PMID: 22984947 PMCID: PMC3479416 DOI: 10.1186/1471-2105-13-236] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2012] [Accepted: 09/11/2012] [Indexed: 01/30/2023] Open
Abstract
BACKGROUND Protein interactions play a key role in life processes. Characterization of conformational properties of protein-protein interactions is important for understanding the mechanisms of protein association. The rapidly increasing amount of experimentally determined structures of proteins and protein-protein complexes provides foundation for research on protein interactions and complex formation. The knowledge of the conformations of the surface side chains is essential for modeling of protein complexes. The purpose of this study was to analyze and compare dihedral angle distribution functions of the side chains at the interface and non-interface areas in bound and unbound proteins. RESULTS To calculate the dihedral angle distribution functions, the configuration space was divided into grid cells. Statistical analysis showed that the similarity between bound and unbound interface and non-interface surface depends on the amino acid type and the grid resolution. The correlation coefficients between the distribution functions increased with the grid spacing increase for all amino acid types. The Manhattan distance showing the degree of dissimilarity between the distribution functions decreased accordingly. Short residues with one or two dihedral angles had higher correlations and smaller Manhattan distances than the longer residues. Met and Arg had the slowest growth of the correlation coefficient with the grid spacing increase. The correlations between the interface and non-interface distribution functions had a similar dependence on the grid resolution in both bound and unbound states. The interface and non-interface differences between bound and unbound distribution functions, caused by biological protein-protein interactions or crystal contacts, disappeared at the 70° grid spacing for interfaces and 30° for non-interface surface, which agrees with an average span of the side-chain rotamers. CONCLUSIONS The two-fold difference in the critical grid spacing indicates larger conformational changes upon binding at the interface than at the rest of the surface. At the same time, transitions between rotamers induced by interactions across the interface or the crystal packing are rare, with most side chains having local readjustments that do not change the rotameric state. The analysis is important for better understanding of protein interactions and development of flexible docking approaches.
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Affiliation(s)
- Tatsiana Kirys
- Center for Bioinformatics, The University of Kansas, Lawrence, KS 66047, USA
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Yao H, Wang Y, Lovell S, Kumar R, Ruvinsky AM, Battaile KP, Vakser IA, Rivera M. The structure of the BfrB-Bfd complex reveals protein-protein interactions enabling iron release from bacterioferritin. J Am Chem Soc 2012; 134:13470-81. [PMID: 22812654 PMCID: PMC3428730 DOI: 10.1021/ja305180n] [Citation(s) in RCA: 59] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Ferritin-like molecules are unique to cellular iron homeostasis because they can store iron at concentrations much higher than those dictated by the solubility of Fe(3+). Very little is known about the protein interactions that deliver iron for storage or promote the mobilization of stored iron from ferritin-like molecules. Here, we report the X-ray crystal structure of Pseudomonas aeruginosa bacterioferritin (Pa-BfrB) in complex with bacterioferritin-associated ferredoxin (Pa-Bfd) at 2.0 Å resolution. As the first example of a ferritin-like molecule in complex with a cognate partner, the structure provides unprecedented insight into the complementary interface that enables the [2Fe-2S] cluster of Pa-Bfd to promote heme-mediated electron transfer through the BfrB protein dielectric (~18 Å), a process that is necessary to reduce the core ferric mineral and facilitate mobilization of Fe(2+). The Pa-BfrB-Bfd complex also revealed the first structure of a Bfd, thus providing a first view to what appears to be a versatile metal binding domain ubiquitous to the large Fer2_BFD family of proteins and enzymes with diverse functions. Residues at the Pa-BfrB-Bfd interface are highly conserved in Bfr and Bfd sequences from a number of pathogenic bacteria, suggesting that the specific recognition between Pa-BfrB and Pa-Bfd is of widespread significance to the understanding of bacterial iron homeostasis.
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Affiliation(s)
- Huili Yao
- Department of Chemistry, University of Kansas, Multidisciplinary Research Building, 2030 Becker Dr., Lawrence, KS 66047
| | - Yan Wang
- Department of Chemistry, University of Kansas, Multidisciplinary Research Building, 2030 Becker Dr., Lawrence, KS 66047
| | - Scott Lovell
- Del Shankel Structural Biology Center, University of Kansas, 2034 Becker Dr., Lawrence, KS 66047
| | - Ritesh Kumar
- Center for Bioinformatics, University of Kansas, 2030 Becker Dr., Lawrence, KS 66047
| | - Anatoly M. Ruvinsky
- Center for Bioinformatics, University of Kansas, 2030 Becker Dr., Lawrence, KS 66047
| | - Kevin P. Battaile
- IMCA-CAT, Hauptman Woodward Medical Research Institute, 9700 S. Cass Avenue, Bldg. 435A, Argonne, IL 60439
| | - Ilya A. Vakser
- Center for Bioinformatics, University of Kansas, 2030 Becker Dr., Lawrence, KS 66047
| | - Mario Rivera
- Department of Chemistry, University of Kansas, Multidisciplinary Research Building, 2030 Becker Dr., Lawrence, KS 66047
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Kundrotas PJ, Zhu Z, Vakser IA. GWIDD: a comprehensive resource for genome-wide structural modeling of protein-protein interactions. Hum Genomics 2012; 6:7. [PMID: 23245398 PMCID: PMC3500202 DOI: 10.1186/1479-7364-6-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2012] [Accepted: 07/11/2012] [Indexed: 11/10/2022] Open
Abstract
Protein-protein interactions are a key component of life processes. The knowledge of the three-dimensional structure of these interactions is important for understanding protein function. Genome-Wide Docking Database (http://gwidd.bioinformatics.ku.edu) offers an extensive source of data for structural studies of protein-protein complexes on genome scale. The current release of the database combines the available experimental data on the structure and characteristics of protein interactions with structural modeling of protein complexes for 771 organisms spanned over the entire universe of life from viruses to humans. The interactions are stored in a relational database with user-friendly interface that includes various search options. The search results can be interactively previewed; the structures, downloaded, along with the interaction characteristics.
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Kirys T, Ruvinsky AM, Tuzikov AV, Vakser IA. Rotamer libraries and probabilities of transition between rotamers for the side chains in protein-protein binding. Proteins 2012; 80:2089-98. [PMID: 22544766 DOI: 10.1002/prot.24103] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2012] [Revised: 04/12/2012] [Accepted: 04/17/2012] [Indexed: 01/26/2023]
Abstract
Conformational changes in the side chains are essential for protein-protein binding. Rotameric states and unbound- to-bound conformational changes in the surface residues were systematically studied on a representative set of protein complexes. The side-chain conformations were mapped onto dihedral angles space. The variable threshold algorithm was developed to cluster the dihedral angle distributions and to derive rotamers, defined as the most probable conformation in a cluster. Six rotamer libraries were generated: full surface, surface noninterface, and surface interface-each for bound and unbound states. The libraries were used to calculate the probabilities of the rotamer transitions upon binding. The stability of amino acids was quantified based on the transition maps. The noninterface residues' stability was higher than that of the interface. Long side chains with three or four dihedral angles were less stable than the shorter ones. The transitions between the rotamers at the interface occurred more frequently than on the noninterface surface. Most side chains changed conformation within the same rotamer or moved to an adjacent rotamer. The highest percentage of the transitions was observed primarily between the two most occupied rotamers. The probability of the transition between rotamers increased with the decrease of the rotamer stability. The analysis revealed characteristics of the surface side-chain conformational transitions that can be utilized in flexible docking protocols.
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Affiliation(s)
- Tatsiana Kirys
- Center for Bioinformatics, The University of Kansas, Lawrence, Kansas 66047, USA
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49
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Abstract
The increasing availability of co-crystallized protein-protein complexes provides an opportunity to use template-based modeling for protein-protein docking. Structure alignment techniques are useful in detection of remote target-template similarities. The size of the structure involved in the alignment is important for the success in modeling. This paper describes a systematic large-scale study to find the optimal definition/size of the interfaces for the structure alignment-based docking applications. The results showed that structural areas corresponding to the cutoff values <12 Å across the interface inadequately represent structural details of the interfaces. With the increase of the cutoff beyond 12 Å, the success rate for the benchmark set of 99 protein complexes, did not increase significantly for higher accuracy models, and decreased for lower-accuracy models. The 12 Å cutoff was optimal in our interface alignment-based docking, and a likely best choice for the large-scale (e.g., on the scale of the entire genome) applications to protein interaction networks. The results provide guidelines for the docking approaches, including high-throughput applications to modeled structures.
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Affiliation(s)
- Rohita Sinha
- Center for Bioinformatics, The University of Kansas, Lawrence, Kansas, United States of America
| | - Petras J. Kundrotas
- Center for Bioinformatics, The University of Kansas, Lawrence, Kansas, United States of America
- * E-mail: (PJK); (IAV)
| | - Ilya A. Vakser
- Center for Bioinformatics, The University of Kansas, Lawrence, Kansas, United States of America
- Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, United States of America
- * E-mail: (PJK); (IAV)
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50
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Liu S, Vakser IA. DECK: Distance and environment-dependent, coarse-grained, knowledge-based potentials for protein-protein docking. BMC Bioinformatics 2011; 12:280. [PMID: 21745398 PMCID: PMC3145612 DOI: 10.1186/1471-2105-12-280] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2011] [Accepted: 07/11/2011] [Indexed: 11/13/2022] Open
Abstract
Background Computational approaches to protein-protein docking typically include scoring aimed at improving the rank of the near-native structure relative to the false-positive matches. Knowledge-based potentials improve modeling of protein complexes by taking advantage of the rapidly increasing amount of experimentally derived information on protein-protein association. An essential element of knowledge-based potentials is defining the reference state for an optimal description of the residue-residue (or atom-atom) pairs in the non-interaction state. Results The study presents a new Distance- and Environment-dependent, Coarse-grained, Knowledge-based (DECK) potential for scoring of protein-protein docking predictions. Training sets of protein-protein matches were generated based on bound and unbound forms of proteins taken from the DOCKGROUND resource. Each residue was represented by a pseudo-atom in the geometric center of the side chain. To capture the long-range and the multi-body interactions, residues in different secondary structure elements at protein-protein interfaces were considered as different residue types. Five reference states for the potentials were defined and tested. The optimal reference state was selected and the cutoff effect on the distance-dependent potentials investigated. The potentials were validated on the docking decoys sets, showing better performance than the existing potentials used in scoring of protein-protein docking results. Conclusions A novel residue-based statistical potential for protein-protein docking was developed and validated on docking decoy sets. The results show that the scoring function DECK can successfully identify near-native protein-protein matches and thus is useful in protein docking. In addition to the practical application of the potentials, the study provides insights into the relative utility of the reference states, the scope of the distance dependence, and the coarse-graining of the potentials.
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Affiliation(s)
- Shiyong Liu
- Biomolecular Physics and Modeling Group, Department of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
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