151
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Peterson LX, Togawa Y, Esquivel-Rodriguez J, Terashi G, Christoffer C, Roy A, Shin WH, Kihara D. Modeling the assembly order of multimeric heteroprotein complexes. PLoS Comput Biol 2018; 14:e1005937. [PMID: 29329283 PMCID: PMC5785014 DOI: 10.1371/journal.pcbi.1005937] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 01/25/2018] [Accepted: 12/19/2017] [Indexed: 12/31/2022] Open
Abstract
Protein-protein interactions are the cornerstone of numerous biological processes. Although an increasing number of protein complex structures have been determined using experimental methods, relatively fewer studies have been performed to determine the assembly order of complexes. In addition to the insights into the molecular mechanisms of biological function provided by the structure of a complex, knowing the assembly order is important for understanding the process of complex formation. Assembly order is also practically useful for constructing subcomplexes as a step toward solving the entire complex experimentally, designing artificial protein complexes, and developing drugs that interrupt a critical step in the complex assembly. There are several experimental methods for determining the assembly order of complexes; however, these techniques are resource-intensive. Here, we present a computational method that predicts the assembly order of protein complexes by building the complex structure. The method, named Path-LzerD, uses a multimeric protein docking algorithm that assembles a protein complex structure from individual subunit structures and predicts assembly order by observing the simulated assembly process of the complex. Benchmarked on a dataset of complexes with experimental evidence of assembly order, Path-LZerD was successful in predicting the assembly pathway for the majority of the cases. Moreover, when compared with a simple approach that infers the assembly path from the buried surface area of subunits in the native complex, Path-LZerD has the strong advantage that it can be used for cases where the complex structure is not known. The path prediction accuracy decreased when starting from unbound monomers, particularly for larger complexes of five or more subunits, for which only a part of the assembly path was correctly identified. As the first method of its kind, Path-LZerD opens a new area of computational protein structure modeling and will be an indispensable approach for studying protein complexes.
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Affiliation(s)
- Lenna X. Peterson
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America
| | - Yoichiro Togawa
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America
| | - Juan Esquivel-Rodriguez
- Department of Computer Science, Purdue University, West Lafayette, Indiana, United States of America
| | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America
| | - Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, Indiana, United States of America
| | - Amitava Roy
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, Indiana, United States of America
- Bioinformatics and Computational Biosciences Branch, Rocky Mountain Laboratories, NIAID, National Institutes of Health, Hamilton, Montana, United States of America
| | - Woong-Hee Shin
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America
- Department of Computer Science, Purdue University, West Lafayette, Indiana, United States of America
- * E-mail:
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152
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Tramontano A. The computational prediction of protein assemblies. Curr Opin Struct Biol 2017; 46:170-175. [PMID: 29102305 DOI: 10.1016/j.sbi.2017.10.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2017] [Revised: 10/04/2017] [Accepted: 10/05/2017] [Indexed: 10/18/2022]
Abstract
The function of proteins in the cell is almost always mediated by their interaction with different partners, including other proteins, nucleic acids or small organic molecules. The ability of identifying all of them is an essential step in our quest for understanding life at the molecular level. The inference of the protein complex composition and of its molecular details can also provide relevant clues for the development and the design of drugs. In this short review, I will discuss the computational aspects of the analysis and prediction of protein-protein assemblies and discuss some of the most recent developments as seen in the last Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiment.
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Affiliation(s)
- Anna Tramontano
- Physics Department, Sapienza University of Rome, Piazzale Aldo Moro, 5 I-00185 Roma, Italy; Istituto Pasteur - Fondazione Cenci Bolognetti, Sapienza University of Rome, Piazzale Aldo Moro, 5 I-00185 Roma, Italy
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153
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154
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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: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [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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155
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Ciemny MP, Kurcinski M, Blaszczyk M, Kolinski A, Kmiecik S. Modeling EphB4-EphrinB2 protein-protein interaction using flexible docking of a short linear motif. Biomed Eng Online 2017; 16:71. [PMID: 28830442 PMCID: PMC5568603 DOI: 10.1186/s12938-017-0362-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Background Many protein–protein interactions are mediated by a short linear motif. Usually, amino acid sequences of those motifs are known or can be predicted. It is much harder to experimentally characterize or predict their structure in the bound form. In this work, we test a possibility of using flexible docking of a short linear motif to predict the interaction interface of the EphB4-EphrinB2 complex (a system extensively studied for its significance in tumor progression). Methods In the modeling, we only use knowledge about the motif sequence and experimental structures of EphB4-EphrinB2 complex partners. The proposed protocol enables efficient modeling of significant conformational changes in the short linear motif fragment during molecular docking simulation. For the docking simulations, we use the CABS-dock method for docking fully flexible peptides to flexible protein receptors (available as a server at http://biocomp.chem.uw.edu.pl/CABSdock/). Based on the docking result, the protein–protein complex is reconstructed and refined. Results Using this novel protocol, we obtained an accurate EphB4-EphrinB2 interaction model. Conclusions The results show that the CABS-dock method may be useful as the primary docking tool in specific protein–protein docking cases similar to EphB4-EphrinB2 complex—that is, where a short linear motif fragment can be identified.
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Affiliation(s)
- Maciej Pawel Ciemny
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Żwirki i Wigury 101, 02-089, Warsaw, Poland.,Faculty of Physics, University of Warsaw, Pasteura 5, Warsaw, Poland
| | - Mateusz Kurcinski
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Żwirki i Wigury 101, 02-089, Warsaw, Poland
| | - Maciej Blaszczyk
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Żwirki i Wigury 101, 02-089, Warsaw, Poland
| | - Andrzej Kolinski
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Żwirki i Wigury 101, 02-089, Warsaw, Poland
| | - Sebastian Kmiecik
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Żwirki i Wigury 101, 02-089, Warsaw, Poland.
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156
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Rini J, Anbalagan M. IGF2BP1: a novel binding protein of p38 MAPK. Mol Cell Biochem 2017; 435:133-140. [PMID: 28497370 DOI: 10.1007/s11010-017-3062-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2017] [Accepted: 05/04/2017] [Indexed: 02/07/2023]
Abstract
Signal transduction pathways control various biological processes in cells leading to distinct cellular functions. Protein-protein interactions and post-translational modifications are the physiological events that occur in signaling pathway. p38 MAPK are known to be involved in regulating wide range of cellular processes by interacting and activating relevant signaling molecules by means of phosphorylation. Deregulation of p38 MAPK is associated with various pathological conditions. In order to get an insight into the role played by p38 MAPK in cellular signaling, studies were carried out to identify proteins that interact with p38 MAPK. Mass spectrometry was used to identify the proteins present in p38 MAPK complex obtained by co-immunoprecipitation. Based on mass spectrometry data, here we report insulin-like growth factor-II binding protein 1 (IGF2BP1) as a novel interacting partner of p38 MAPK. IGF2BP1 is a RNA-binding protein predominantly known to be involved in tumor progression. To reconfirm the mass spectrometry data, in silico analysis was carried out. Based on different models predicted in silico, we report the possible interaction domains of p38MAPK and IGF2BP1. Considering the involvement of p38MAPK and IGF2BP1 in cancer, our study opens up the possibility of p38MAPK regulating IGF2BP1 function, and the possibility of targeting this novel interaction for developing cancer-treating drugs is discussed.
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Affiliation(s)
- Jacob Rini
- School of Biosciences and Technology, VIT University, Vellore, Tamil Nadu, 632014, India
| | - Moorthy Anbalagan
- School of Biosciences and Technology, VIT University, Vellore, Tamil Nadu, 632014, India.
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157
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Peterson LX, Roy A, Christoffer C, Terashi G, Kihara D. Modeling disordered protein interactions from biophysical principles. PLoS Comput Biol 2017; 13:e1005485. [PMID: 28394890 PMCID: PMC5402988 DOI: 10.1371/journal.pcbi.1005485] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2016] [Revised: 04/24/2017] [Accepted: 03/29/2017] [Indexed: 12/12/2022] Open
Abstract
Disordered protein-protein interactions (PPIs), those involving a folded protein and an intrinsically disordered protein (IDP), are prevalent in the cell, including important signaling and regulatory pathways. IDPs do not adopt a single dominant structure in isolation but often become ordered upon binding. To aid understanding of the molecular mechanisms of disordered PPIs, it is crucial to obtain the tertiary structure of the PPIs. However, experimental methods have difficulty in solving disordered PPIs and existing protein-protein and protein-peptide docking methods are not able to model them. Here we present a novel computational method, IDP-LZerD, which models the conformation of a disordered PPI by considering the biophysical binding mechanism of an IDP to a structured protein, whereby a local segment of the IDP initiates the interaction and subsequently the remaining IDP regions explore and coalesce around the initial binding site. On a dataset of 22 disordered PPIs with IDPs up to 69 amino acids, successful predictions were made for 21 bound and 18 unbound receptors. The successful modeling provides additional support for biophysical principles. Moreover, the new technique significantly expands the capability of protein structure modeling and provides crucial insights into the molecular mechanisms of disordered PPIs.
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Affiliation(s)
- Lenna X. Peterson
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America
| | - Amitava Roy
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, Indiana, United States of America
- Bioinformatics and Computational Biosciences Branch, Rocky Mountain Laboratories, NIAID, National Institutes of Health, Hamilton, Montana, United States of America
| | - Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, Indiana, United States of America
| | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America
- School of Pharmacy, Kitasato University, Tokyo, Japan
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America
- Department of Computer Science, Purdue University, West Lafayette, Indiana, United States of America
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158
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Anishchenko I, Kundrotas PJ, Vakser IA. Modeling complexes of modeled proteins. Proteins 2017; 85:470-478. [PMID: 27701777 PMCID: PMC5313347 DOI: 10.1002/prot.25183] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [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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159
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Peterson LX, Kim H, Esquivel-Rodriguez J, Roy A, Han X, Shin WH, Zhang J, Terashi G, Lee M, Kihara D. Human and server docking prediction for CAPRI round 30-35 using LZerD with combined scoring functions. Proteins 2017; 85:513-527. [PMID: 27654025 PMCID: PMC5313330 DOI: 10.1002/prot.25165] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2016] [Revised: 09/09/2016] [Accepted: 09/15/2016] [Indexed: 12/12/2022]
Abstract
We report the performance of protein-protein docking predictions by our group for recent rounds of the Critical Assessment of Prediction of Interactions (CAPRI), a community-wide assessment of state-of-the-art docking methods. Our prediction procedure uses a protein-protein docking program named LZerD developed in our group. LZerD represents a protein surface with 3D Zernike descriptors (3DZD), which are based on a mathematical series expansion of a 3D function. The appropriate soft representation of protein surface with 3DZD makes the method more tolerant to conformational change of proteins upon docking, which adds an advantage for unbound docking. Docking was guided by interface residue prediction performed with BindML and cons-PPISP as well as literature information when available. The generated docking models were ranked by a combination of scoring functions, including PRESCO, which evaluates the native-likeness of residues' spatial environments in structure models. First, we discuss the overall performance of our group in the CAPRI prediction rounds and investigate the reasons for unsuccessful cases. Then, we examine the performance of several knowledge-based scoring functions and their combinations for ranking docking models. It was found that the quality of a pool of docking models generated by LZerD, that is whether or not the pool includes near-native models, can be predicted by the correlation of multiple scores. Although the current analysis used docking models generated by LZerD, findings on scoring functions are expected to be universally applicable to other docking methods. Proteins 2017; 85:513-527. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Lenna X. Peterson
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Hyungrae Kim
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | | | - Amitava Roy
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, IN, 47907, USA
- Bioinformatics and Computational Biosciences Branch, Rocky Mountain Laboratories, NIAID, National Institutes of Health, Hamilton, Montana 59840, USA
| | - Xusi Han
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Woong-Hee Shin
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Jian Zhang
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
- School of Pharmacy, Kitasato University, Minato-Ku, Tokyo, 108-8641, Japan
| | - Matt Lee
- Lilly Biotechnology Center San Diego, 10300 Campus Point Drive, San Diego, CA, 92121, USA
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
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160
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Zhang Z, Ehmann U, Zacharias M. Monte Carlo replica-exchange based ensemble docking of protein conformations. Proteins 2017; 85:924-937. [DOI: 10.1002/prot.25262] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Revised: 01/11/2017] [Accepted: 01/19/2017] [Indexed: 12/14/2022]
Affiliation(s)
- Zhe Zhang
- Physik-Department T38; Technische Universität München; Garching 85748 Germany
- Department Chemie; Technische Universität München, Biomolecular NMR and Munich Center for Integrated Protein Science; Garching 85747 Germany
- College of Life and Health Sciences; Northeast University; Shenyang P.R. China
| | - Uwe Ehmann
- Physik-Department T38; Technische Universität München; Garching 85748 Germany
| | - Martin Zacharias
- Physik-Department T38; Technische Universität München; Garching 85748 Germany
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161
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Launay G, Ceres N, Martin J. Non-interacting proteins may resemble interacting proteins: prevalence and implications. Sci Rep 2017; 7:40419. [PMID: 28084410 PMCID: PMC5289270 DOI: 10.1038/srep40419] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2016] [Accepted: 12/07/2016] [Indexed: 12/13/2022] Open
Abstract
The vast majority of proteins do not form functional interactions in physiological conditions. We have considered several sets of protein pairs from S. cerevisiae with no functional interaction reported, denoted as non-interacting pairs, and compared their 3D structures to available experimental complexes. We identified some non-interacting pairs with significant structural similarity with experimental complexes, indicating that, even though they do not form functional interactions, they have compatible structures. We estimate that up to 8.7% of non-interacting protein pairs could have compatible structures. This number of interactions exceeds the number of functional interactions (around 0.2% of the total interactions) by a factor 40. Network analysis suggests that the interactions formed by non-interacting pairs with compatible structures could be particularly hazardous to the protein-protein interaction network. From a structural point of view, these interactions display no aberrant structural characteristics, and are even predicted as relatively stable and enriched in potential physical interactors, suggesting a major role of regulation to prevent them.
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Affiliation(s)
- Guillaume Launay
- Univ Lyon, CNRS, UMR 5086 MMSB, 7 passage du Vercors F-69367, Lyon, France
| | - Nicoletta Ceres
- Univ Lyon, CNRS, UMR 5086 MMSB, 7 passage du Vercors F-69367, Lyon, France
| | - Juliette Martin
- Univ Lyon, CNRS, UMR 5086 MMSB, 7 passage du Vercors F-69367, Lyon, France
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162
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Hasani HJ, Barakat KH. Protein-Protein Docking. PHARMACEUTICAL SCIENCES 2017. [DOI: 10.4018/978-1-5225-1762-7.ch042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Protein-protein docking algorithms are powerful computational tools, capable of analyzing the protein-protein interactions at the atomic-level. In this chapter, we will review the theoretical concepts behind different protein-protein docking algorithms, highlighting their strengths as well as their limitations and pointing to important case studies for each method. The methods we intend to cover in this chapter include various search strategies and scoring techniques. This includes exhaustive global search, fast Fourier transform search, spherical Fourier transform-based search, direct search in Cartesian space, local shape feature matching, geometric hashing, genetic algorithm, randomized search, and Monte Carlo search. We will also discuss the different ways that have been used to incorporate protein flexibility within the docking procedure and some other future directions in this field, suggesting possible ways to improve the different methods.
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163
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Sarvagalla S, Coumar MS. Protein-Protein Interactions (PPIs) as an Alternative to Targeting the ATP Binding Site of Kinase. PHARMACEUTICAL SCIENCES 2017. [DOI: 10.4018/978-1-5225-1762-7.ch043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Most of the developed kinase inhibitor drugs are ATP competitive and suffer from drawbacks such as off-target kinase activity, development of resistance due to mutation in the ATP binding pocket and unfavorable intellectual property situations. Besides the ATP binding pocket, protein kinases have binding sites that are involved in Protein-Protein Interactions (PPIs); these PPIs directly or indirectly regulate the protein kinase activity. Of recent, small molecule inhibitors of PPIs are emerging as an alternative to ATP competitive agents. Rational design of inhibitors for kinase PPIs could be carried out using molecular modeling techniques. In silico tools available for the prediction of hot spot residues and cavities at the PPI sites and the means to utilize this information for the identification of inhibitors are discussed. Moreover, in silico studies to target the Aurora B-INCENP PPI sites are discussed in context. Overall, this chapter provides detailed in silico strategies that are available to the researchers for carrying out structure-based drug design of PPI inhibitors.
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164
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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] [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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165
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Choong YS, Lee YV, Soong JX, Law CT, Lim YY. Computer-Aided Antibody Design: An Overview. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 1053:221-243. [PMID: 29549642 DOI: 10.1007/978-3-319-72077-7_11] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The use of monoclonal antibody as the next generation protein therapeutics with remarkable success has surged the development of antibody engineering to design molecules for optimizing affinity, better efficacy, greater safety and therapeutic function. Therefore, computational methods have become increasingly important to generate hypotheses, interpret and guide experimental works. In this chapter, we discussed the overall antibody design by computational approches.
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Affiliation(s)
- Yee Siew Choong
- Institute for Research in Molecular Medicine, Universiti Sains Malaysia, Minden, Penang, Malaysia.
| | - Yie Vern Lee
- Institute for Research in Molecular Medicine, Universiti Sains Malaysia, Minden, Penang, Malaysia
| | - Jia Xin Soong
- Institute for Research in Molecular Medicine, Universiti Sains Malaysia, Minden, Penang, Malaysia
| | - Cheh Tat Law
- Institute for Research in Molecular Medicine, Universiti Sains Malaysia, Minden, Penang, Malaysia
| | - Yee Ying Lim
- Institute for Research in Molecular Medicine, Universiti Sains Malaysia, Minden, Penang, Malaysia
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166
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Bai F, Morcos F, Cheng RR, Jiang H, Onuchic JN. Elucidating the druggable interface of protein-protein interactions using fragment docking and coevolutionary analysis. Proc Natl Acad Sci U S A 2016; 113:E8051-E8058. [PMID: 27911825 PMCID: PMC5167203 DOI: 10.1073/pnas.1615932113] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Protein-protein interactions play a central role in cellular function. Improving the understanding of complex formation has many practical applications, including the rational design of new therapeutic agents and the mechanisms governing signal transduction networks. The generally large, flat, and relatively featureless binding sites of protein complexes pose many challenges for drug design. Fragment docking and direct coupling analysis are used in an integrated computational method to estimate druggable protein-protein interfaces. (i) This method explores the binding of fragment-sized molecular probes on the protein surface using a molecular docking-based screen. (ii) The energetically favorable binding sites of the probes, called hot spots, are spatially clustered to map out candidate binding sites on the protein surface. (iii) A coevolution-based interface interaction score is used to discriminate between different candidate binding sites, yielding potential interfacial targets for therapeutic drug design. This approach is validated for important, well-studied disease-related proteins with known pharmaceutical targets, and also identifies targets that have yet to be studied. Moreover, therapeutic agents are proposed by chemically connecting the fragments that are strongly bound to the hot spots.
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Affiliation(s)
- Fang Bai
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005
| | - Faruck Morcos
- Department of Biological Sciences, University of Texas at Dallas, Dallas, TX 75080
- Department of Bioengineering, University of Texas at Dallas, Dallas, TX 75080
- Center for Systems Biology, University of Texas at Dallas, Dallas, TX 75080
| | - Ryan R Cheng
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005
| | - Hualiang Jiang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China;
| | - José N Onuchic
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005;
- Department of Physics and Astronomy, Rice University, Houston, TX 77005
- Department of Chemistry, Rice University, Houston, TX 77005
- Department of Biosciences, Rice University, Houston, TX 77005
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167
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Gromiha MM, Yugandhar K, Jemimah S. Protein-protein interactions: scoring schemes and binding affinity. Curr Opin Struct Biol 2016; 44:31-38. [PMID: 27866112 DOI: 10.1016/j.sbi.2016.10.016] [Citation(s) in RCA: 80] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Revised: 09/30/2016] [Accepted: 10/25/2016] [Indexed: 01/16/2023]
Abstract
Protein-protein interactions mediate several cellular functions, which can be understood from the information obtained using the three-dimensional structures of protein-protein complexes and binding affinity data. This review focuses on computational aspects of predicting the best native-like complex structure and binding affinities. The first part covers the prediction of protein-protein complex structures and the advantages of conformational searching and scoring functions in protein-protein docking. The second part is devoted to various aspects of protein-protein interaction thermodynamics, such as databases for binding affinities and other thermodynamic parameters, computational methods to predict the binding affinity using either the three-dimensional structures of complexes or amino acid sequences, and change in binding affinities of the complexes upon mutations. We provide the latest developments on protein-protein docking and binding affinity studies along with a list of available computational resources for understanding protein-protein interactions.
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Affiliation(s)
- M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India.
| | - K Yugandhar
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India
| | - Sherlyn Jemimah
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India
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168
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Joseph JA, Whittleston CS, Wales DJ. Structure, Thermodynamics, and Folding Pathways for a Tryptophan Zipper as a Function of Local Rigidification. J Chem Theory Comput 2016; 12:6109-6117. [DOI: 10.1021/acs.jctc.6b00734] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Affiliation(s)
- Jerelle A. Joseph
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Chris S. Whittleston
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - David J. Wales
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
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169
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Chermak E, De Donato R, Lensink MF, Petta A, Serra L, Scarano V, Cavallo L, Oliva R. Introducing a Clustering Step in a Consensus Approach for the Scoring of Protein-Protein Docking Models. PLoS One 2016; 11:e0166460. [PMID: 27846259 PMCID: PMC5112798 DOI: 10.1371/journal.pone.0166460] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Accepted: 10/28/2016] [Indexed: 12/18/2022] Open
Abstract
Correctly scoring protein-protein docking models to single out native-like ones is an open challenge. It is also an object of assessment in CAPRI (Critical Assessment of PRedicted Interactions), the community-wide blind docking experiment. We introduced in the field the first pure consensus method, CONSRANK, which ranks models based on their ability to match the most conserved contacts in the ensemble they belong to. In CAPRI, scorers are asked to evaluate a set of available models and select the top ten ones, based on their own scoring approach. Scorers’ performance is ranked based on the number of targets/interfaces for which they could provide at least one correct solution. In such terms, blind testing in CAPRI Round 30 (a joint prediction round with CASP11) has shown that critical cases for CONSRANK are represented by targets showing multiple interfaces or for which only a very small number of correct solutions are available. To address these challenging cases, CONSRANK has now been modified to include a contact-based clustering of the models as a preliminary step of the scoring process. We used an agglomerative hierarchical clustering based on the number of common inter-residue contacts within the models. Two criteria, with different thresholds, were explored in the cluster generation, setting either the number of common contacts or of total clusters. For each clustering approach, after selecting the top (most populated) ten clusters, CONSRANK was run on these clusters and the top-ranked model for each cluster was selected, in the limit of 10 models per target. We have applied our modified scoring approach, Clust-CONSRANK, to SCORE_SET, a set of CAPRI scoring models made recently available by CAPRI assessors, and to the subset of homodimeric targets in CAPRI Round 30 for which CONSRANK failed to include a correct solution within the ten selected models. Results show that, for the challenging cases, the clustering step typically enriches the ten top ranked models in native-like solutions. The best performing clustering approaches we tested indeed lead to more than double the number of cases for which at least one correct solution can be included within the top ten ranked models.
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Affiliation(s)
- Edrisse Chermak
- Kaust Catalysis Center, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia
| | - Renato De Donato
- Kaust Catalysis Center, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia
- Dipartimento di Informatica ed Applicazioni, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano (SA), Italy
| | | | - Andrea Petta
- Dipartimento di Informatica ed Applicazioni, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano (SA), Italy
| | - Luigi Serra
- Dipartimento di Informatica ed Applicazioni, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano (SA), Italy
| | - Vittorio Scarano
- Dipartimento di Informatica ed Applicazioni, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano (SA), Italy
| | - Luigi Cavallo
- Kaust Catalysis Center, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia
| | - Romina Oliva
- Department of Sciences and Technologies, University “Parthenope” of Naples, Centro Direzionale Isola C4 80143, Naples, Italy
- * E-mail:
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170
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Zheng J, Kundrotas PJ, Vakser IA, Liu S. Template-Based Modeling of Protein-RNA Interactions. PLoS Comput Biol 2016; 12:e1005120. [PMID: 27662342 PMCID: PMC5035060 DOI: 10.1371/journal.pcbi.1005120] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2016] [Accepted: 08/25/2016] [Indexed: 12/29/2022] Open
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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171
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Yu J, Guerois R. PPI4DOCK: large scale assessment of the use of homology models in free docking over more than 1000 realistic targets. Bioinformatics 2016; 32:3760-3767. [PMID: 27551106 DOI: 10.1093/bioinformatics/btw533] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2016] [Revised: 07/22/2016] [Accepted: 08/10/2016] [Indexed: 12/14/2022] Open
Abstract
MOTIVATION Protein-protein docking methods are of great importance for understanding interactomes at the structural level. It has become increasingly appealing to use not only experimental structures but also homology models of unbound subunits as input for docking simulations. So far we are missing a large scale assessment of the success of rigid-body free docking methods on homology models. RESULTS We explored how we could benefit from comparative modelling of unbound subunits to expand docking benchmark datasets. Starting from a collection of 3157 non-redundant, high X-ray resolution heterodimers, we developed the PPI4DOCK benchmark containing 1417 docking targets based on unbound homology models. Rigid-body docking by Zdock showed that for 1208 cases (85.2%), at least one correct decoy was generated, emphasizing the efficiency of rigid-body docking in generating correct assemblies. Overall, the PPI4DOCK benchmark contains a large set of realistic cases and provides new ground for assessing docking and scoring methodologies. AVAILABILITY AND IMPLEMENTATION Benchmark sets can be downloaded from http://biodev.cea.fr/interevol/ppi4dock/ CONTACT: guerois@cea.frSupplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jinchao Yu
- Institute for Integrative Biology of the Cell (I2BC), IBITECS, CEA, CNRS, Univ Paris-Sud, Université Paris-Saclay, F-91198, Gif-sur-Yvette, France
| | - Raphaël Guerois
- Institute for Integrative Biology of the Cell (I2BC), IBITECS, CEA, CNRS, Univ Paris-Sud, Université Paris-Saclay, F-91198, Gif-sur-Yvette, France
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172
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Almeida RM, Dell'Acqua S, Krippahl L, Moura JJG, Pauleta SR. Predicting Protein-Protein Interactions Using BiGGER: Case Studies. Molecules 2016; 21:E1037. [PMID: 27517887 PMCID: PMC6274584 DOI: 10.3390/molecules21081037] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2016] [Revised: 08/03/2016] [Accepted: 08/04/2016] [Indexed: 11/29/2022] Open
Abstract
The importance of understanding interactomes makes preeminent the study of protein interactions and protein complexes. Traditionally, protein interactions have been elucidated by experimental methods or, with lower impact, by simulation with protein docking algorithms. This article describes features and applications of the BiGGER docking algorithm, which stands at the interface of these two approaches. BiGGER is a user-friendly docking algorithm that was specifically designed to incorporate experimental data at different stages of the simulation, to either guide the search for correct structures or help evaluate the results, in order to combine the reliability of hard data with the convenience of simulations. Herein, the applications of BiGGER are described by illustrative applications divided in three Case Studies: (Case Study A) in which no specific contact data is available; (Case Study B) when different experimental data (e.g., site-directed mutagenesis, properties of the complex, NMR chemical shift perturbation mapping, electron tunneling) on one of the partners is available; and (Case Study C) when experimental data are available for both interacting surfaces, which are used during the search and/or evaluation stage of the docking. This algorithm has been extensively used, evidencing its usefulness in a wide range of different biological research fields.
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Affiliation(s)
- Rui M Almeida
- UCIBIO, REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, NOVA, 2829-516 Caparica, Portugal.
| | - Simone Dell'Acqua
- Department of Chemistry, University of Pavia, Via Taramelli 12, 27100 Pavia, Italy.
| | - Ludwig Krippahl
- CENTRIA, Departamento de Informática, Faculdade de Ciências e Tecnologia, NOVA, 2829-516 Caparica, Portugal.
| | - José J G Moura
- UCIBIO, REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, NOVA, 2829-516 Caparica, Portugal.
| | - Sofia R Pauleta
- UCIBIO, REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, NOVA, 2829-516 Caparica, Portugal.
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173
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Rocheleau AD, Cao TM, Takitani T, King MR. Comparison of human and mouse E-selectin binding to Sialyl-Lewis(x). BMC STRUCTURAL BIOLOGY 2016; 16:10. [PMID: 27368167 PMCID: PMC4930595 DOI: 10.1186/s12900-016-0060-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Accepted: 06/21/2016] [Indexed: 12/22/2022]
Abstract
Background During inflammation, leukocytes are captured by the selectin family of adhesion receptors lining blood vessels to facilitate exit from the bloodstream. E-selectin is upregulated on stimulated endothelial cells and binds to several ligands on the surface of leukocytes. Selectin:ligand interactions are mediated in part by the interaction between the lectin domain and Sialyl-Lewis x (sLex), a tetrasaccharide common to selectin ligands. There is a high degree of homology between selectins of various species: about 72 and 60 % in the lectin and EGF domains, respectively. In this study, molecular dynamics, docking, and steered molecular dynamics simulations were used to compare the binding and dissociation mechanisms of sLex with mouse and human E-selectin. First, a mouse E-selectin homology model was generated using the human E-selectin crystal structure as a template. Results Mouse E-selectin was found to have a greater interdomain angle, which has been previously shown to correlate with stronger binding among selectins. sLex was docked onto human and mouse E-selectin, and the mouse complex was found to have a higher free energy of binding and a lower dissociation constant, suggesting stronger binding. The mouse complex had higher flexibility in a few key residues. Finally, steered molecular dynamics was used to dissociate the complexes at force loading rates of 2000–5000 pm/ps2. The mouse complex took longer to dissociate at every force loading rate and the difference was statistically significant at 3000 pm/ps2. When sLex-coated microspheres were perfused through microtubes coated with human or mouse E-selectin, the particles rolled more slowly on mouse E-selectin. Conclusions Both molecular dynamics simulations and microsphere adhesion experiments show that mouse E-selectin protein binds more strongly to sialyl Lewis x ligand than human E-selectin. This difference was explained by a greater interdomain angle for mouse E-selectin, and greater flexibility in key residues. Future work could introduce similar amino acid substitutions into the human E-selectin sequence to further modulate adhesion behavior.
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Affiliation(s)
- Anne D Rocheleau
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - Thong M Cao
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - Tait Takitani
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - Michael R King
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA.
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174
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Chi PB, Liberles DA. Selection on protein structure, interaction, and sequence. Protein Sci 2016; 25:1168-78. [PMID: 26808055 PMCID: PMC4918422 DOI: 10.1002/pro.2886] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2015] [Revised: 01/18/2016] [Accepted: 01/19/2016] [Indexed: 11/10/2022]
Abstract
Characterizing the probabilities of observing amino acid substitutions at specific sites in a protein over evolutionary time is a major goal in the field of molecular evolution. While purely statistical approaches at different levels of complexity exist, approaches rooted in underlying biological processes are necessary to characterize both the context-dependence of sequence changes (epistasis) and to extrapolate to sequences not observed in biological databases. To develop such approaches, an understanding of the different selective forces that act on amino acid substitution is necessary. Here, an overview of selection on and corresponding modeling of folding stability, folding specificity, binding affinity and specificity for ligands, the evolution of new binding sites on protein surfaces, protein dynamics, intrinsic disorder, and protein aggregation as well as the interplay with protein expression level (concentration) and biased mutational processes are presented.
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Affiliation(s)
- Peter B Chi
- Department of Biology and Center for Computational Genetics and Genomics, Temple University, Philadelphia, Pennsylvania, 19122
- Department of Mathematics and Computer Science, Ursinus College, Collegeville, Pennsylvania, 19426
| | - David A Liberles
- Department of Biology and Center for Computational Genetics and Genomics, Temple University, Philadelphia, Pennsylvania, 19122
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175
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Hou Q, Lensink MF, Heringa J, Feenstra KA. CLUB-MARTINI: Selecting Favourable Interactions amongst Available Candidates, a Coarse-Grained Simulation Approach to Scoring Docking Decoys. PLoS One 2016; 11:e0155251. [PMID: 27166787 PMCID: PMC4864233 DOI: 10.1371/journal.pone.0155251] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2015] [Accepted: 04/26/2016] [Indexed: 01/12/2023] Open
Abstract
Large-scale identification of native binding orientations is crucial for understanding the role of protein-protein interactions in their biological context. Measuring binding free energy is the method of choice to estimate binding strength and reveal the relevance of particular conformations in which proteins interact. In a recent study, we successfully applied coarse-grained molecular dynamics simulations to measure binding free energy for two protein complexes with similar accuracy to full-atomistic simulation, but 500-fold less time consuming. Here, we investigate the efficacy of this approach as a scoring method to identify stable binding conformations from thousands of docking decoys produced by protein docking programs. To test our method, we first applied it to calculate binding free energies of all protein conformations in a CAPRI (Critical Assessment of PRedicted Interactions) benchmark dataset, which included over 19000 protein docking solutions for 15 benchmark targets. Based on the binding free energies, we ranked all docking solutions to select the near-native binding modes under the assumption that the native-solutions have lowest binding free energies. In our top 100 ranked structures, for the ‘easy’ targets that have many near-native conformations, we obtain a strong enrichment of acceptable or better quality structures; for the ‘hard’ targets without near-native decoys, our method is still able to retain structures which have native binding contacts. Moreover, in our top 10 selections, CLUB-MARTINI shows a comparable performance when compared with other state-of-the-art docking scoring functions. As a proof of concept, CLUB-MARTINI performs remarkably well for many targets and is able to pinpoint near-native binding modes in the top selections. To the best of our knowledge, this is the first time interaction free energy calculated from MD simulations have been used to rank docking solutions at a large scale.
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Affiliation(s)
- Qingzhen Hou
- Center for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, De Boelelaan 1081A, 1081 HV Amsterdam, The Netherlands
| | - Marc F. Lensink
- University Lille, CNRS, UMR8576 UGSF - Institute for Structural and Functional Glycobiology, F-59000, Lille, France
| | - Jaap Heringa
- Center for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, De Boelelaan 1081A, 1081 HV Amsterdam, The Netherlands
| | - K. Anton Feenstra
- Center for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, De Boelelaan 1081A, 1081 HV Amsterdam, The Netherlands
- * E-mail:
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176
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Keskin O, Tuncbag N, Gursoy A. Predicting Protein–Protein Interactions from the Molecular to the Proteome Level. Chem Rev 2016; 116:4884-909. [DOI: 10.1021/acs.chemrev.5b00683] [Citation(s) in RCA: 207] [Impact Index Per Article: 25.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
| | - Nurcan Tuncbag
- Graduate
School of Informatics, Department of Health Informatics, Middle East Technical University, 06800 Ankara, Turkey
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177
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Maheshwari S, Brylinski M. Template-based identification of protein–protein interfaces using eFindSitePPI. Methods 2016; 93:64-71. [DOI: 10.1016/j.ymeth.2015.07.017] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Revised: 07/12/2015] [Accepted: 07/29/2015] [Indexed: 11/26/2022] Open
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178
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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] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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179
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Badal VD, Kundrotas PJ, Vakser IA. Text Mining for Protein Docking. PLoS Comput Biol 2015; 11:e1004630. [PMID: 26650466 PMCID: PMC4674139 DOI: 10.1371/journal.pcbi.1004630] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2015] [Accepted: 10/29/2015] [Indexed: 11/18/2022] Open
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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180
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Xue LC, Dobbs D, Bonvin AMJJ, Honavar V. Computational prediction of protein interfaces: A review of data driven methods. FEBS Lett 2015; 589:3516-26. [PMID: 26460190 PMCID: PMC4655202 DOI: 10.1016/j.febslet.2015.10.003] [Citation(s) in RCA: 101] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Revised: 10/01/2015] [Accepted: 10/02/2015] [Indexed: 01/06/2023]
Abstract
Reliably pinpointing which specific amino acid residues form the interface(s) between a protein and its binding partner(s) is critical for understanding the structural and physicochemical determinants of protein recognition and binding affinity, and has wide applications in modeling and validating protein interactions predicted by high-throughput methods, in engineering proteins, and in prioritizing drug targets. Here, we review the basic concepts, principles and recent advances in computational approaches to the analysis and prediction of protein-protein interfaces. We point out caveats for objectively evaluating interface predictors, and discuss various applications of data-driven interface predictors for improving energy model-driven protein-protein docking. Finally, we stress the importance of exploiting binding partner information in reliably predicting interfaces and highlight recent advances in this emerging direction.
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Affiliation(s)
- Li C Xue
- Faculty of Science - Chemistry, Bijvoet Center for Biomolecular Research, Utrecht Univ., Utrecht 3584 CH, The Netherlands.
| | - Drena Dobbs
- Department of Genetics, Development & Cell Biology, Iowa State Univ., Ames, IA 50011, USA; Bioinformatics & Computational Biology Program, Iowa State Univ., Ames, IA 50011, USA
| | - Alexandre M J J Bonvin
- Faculty of Science - Chemistry, Bijvoet Center for Biomolecular Research, Utrecht Univ., Utrecht 3584 CH, The Netherlands
| | - Vasant Honavar
- College of Information Sciences & Technology, Pennsylvania State Univ., University Park, PA 16802, USA; Genomics & Bioinformatics Program, Pennsylvania State Univ., University Park, PA 16802, USA; Neuroscience Program, Pennsylvania State Univ., University Park, PA 16802, USA; The Huck Institutes of the Life Sciences, Pennsylvania State Univ., University Park, PA 16802, USA; Center for Big Data Analytics & Discovery Informatics, Pennsylvania State Univ., University Park, PA 16802, USA; Institute for Cyberscience, Pennsylvania State Univ., University Park, PA 16802, USA
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181
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Muratcioglu S, Guven-Maiorov E, Keskin Ö, Gursoy A. Advances in template-based protein docking by utilizing interfaces towards completing structural interactome. Curr Opin Struct Biol 2015; 35:87-92. [PMID: 26539658 DOI: 10.1016/j.sbi.2015.10.001] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2015] [Revised: 10/09/2015] [Accepted: 10/13/2015] [Indexed: 11/27/2022]
Abstract
The increase in the number of structurally determined protein complexes strengthens template-based docking (TBD) methods for modelling protein-protein interactions (PPIs). These methods utilize the known structures of protein complexes as templates to predict the quaternary structure of the target proteins. The templates may be partial or complete structures. Interface based (partial) methods have recently gained interest due in part to the observation that the interface regions are reusable. We describe how available template interfaces can be used to obtain the structural models of protein interactions. Despite the agreement that a majority of the protein complexes can be modelled using the available Protein Data Bank (PDB) structures, a handful of studies argue that we need more template proteins to increase the structural coverage of PPIs. We also discuss the performance of the interface TBD methods at large scale, and the significance of capturing multiple conformations for improving accuracy.
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Affiliation(s)
- Serena Muratcioglu
- Department of Chemical and Biological Engineering, Koc University, 34450 Istanbul, Turkey; Center for Computational Biology and Bioinformatics, Koc University, 34450 Istanbul, Turkey
| | - Emine Guven-Maiorov
- Department of Chemical and Biological Engineering, Koc University, 34450 Istanbul, Turkey; Center for Computational Biology and Bioinformatics, Koc University, 34450 Istanbul, Turkey
| | - Özlem Keskin
- Department of Chemical and Biological Engineering, Koc University, 34450 Istanbul, Turkey; Center for Computational Biology and Bioinformatics, Koc University, 34450 Istanbul, Turkey
| | - Attila Gursoy
- Department of Computer Engineering, Koc University, 34450 Istanbul, Turkey; Center for Computational Biology and Bioinformatics, Koc University, 34450 Istanbul, Turkey.
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182
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Morris DH, Yip CK, Shi Y, Chait BT, Wang QJ. BECLIN 1-VPS34 COMPLEX ARCHITECTURE: UNDERSTANDING THE NUTS AND BOLTS OF THERAPEUTIC TARGETS. ACTA ACUST UNITED AC 2015; 10:398-426. [PMID: 26692106 DOI: 10.1007/s11515-015-1374-y] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Autophagy is an important lysosomal degradation pathway that aids in the maintenance of cellular homeostasis by breaking down and recycling intracellular contents. Dysregulation of autophagy is linked to a growing number of human diseases. The Beclin 1-Vps34 protein-protein interaction network is critical for autophagy regulation and is therefore essential to cellular integrity. Manipulation of autophagy, in particular via modulation of the action of the Beclin 1-Vps34 complexes, is considered a promising route to combat autophagy-related diseases. Here we summarize recent findings on the core components and structural architecture of the Beclin 1-Vps34 complexes, and how these findings provide valuable insights into the molecular mechanisms that underlie the multiple functions of these complexes and for devising therapeutic strategies.
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Affiliation(s)
- Deanna H Morris
- Department of Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY 40536
| | - Calvin K Yip
- Department of Biochemistry and Molecular Biology, The University of British Columbia, Vancouver, BC, Canada V6T1Z3
| | - Yi Shi
- Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, NY 10065
| | - Brian T Chait
- Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, NY 10065
| | - Qing Jun Wang
- Department of Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY 40536 ; Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, KY 40536, USA ; Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA
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183
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Abstract
SIGNIFICANCE Selenoprotein K (SelK) is an endoplasmic reticulum (ER) membrane protein, and its expression is sensitive to dietary selenium levels. A recently described role for SelK as a cofactor in catalyzing protein palmitoylation reactions provides an important link between low dietary selenium intake and suboptimal cellular functions that depend on this selenoprotein for palmitoylation. RECENT ADVANCES A recent breakthrough provided insight into the contribution of SelK to calcium (Ca(2+)) flux in immune cells. In particular, SelK is required for palmitoylation of the Ca(2+) channel protein, inositol-1,4,5-triphosphate receptor (IP3R) in the ER membrane. Without this post-translational modification, expression and function of the IP3R is impaired. SelK is required for palmitoylation of another transmembrane protein, CD36, and very likely other proteins. SelK serves as a cofactor during protein palmitoylation by binding to the protein acyltransferase, DHHC6, thereby facilitating addition of the palmitate via a thioester bond to the sulfhydryl group of cysteine residues of target proteins. CRITICAL ISSUES The association of DHHC6 and SelK is clearly important for immune cell functions and possibly other cell types. The step in the DHHC6 catalyzed S-acylation reaction on which SelK acts remains unclear and possible mechanisms of how the kinetics of the reaction are impacted by SelK binding to DHHC6 are presented here. FUTURE DIRECTIONS Uncovering the specific role of SelK in promoting DHHC6 catalyzed protein palmitoylation may open a new line of inquiry into other selenoproteins playing similar roles as cofactors for different enzymatic processes.
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Affiliation(s)
- Gregory J Fredericks
- Department of Cell and Molecular Biology, John A. Burns School of Medicine, University of Hawaii , Honolulu, Hawaii
| | - Peter R Hoffmann
- Department of Cell and Molecular Biology, John A. Burns School of Medicine, University of Hawaii , Honolulu, Hawaii
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184
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Popov P, Grudinin S. Knowledge of Native Protein–Protein Interfaces Is Sufficient To Construct Predictive Models for the Selection of Binding Candidates. J Chem Inf Model 2015; 55:2242-55. [DOI: 10.1021/acs.jcim.5b00372] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Petr Popov
- Université Grenoble Alpes, Laboratoire Jean Kuntzmann (LJK), F-38000 Grenoble, France
- CNRS, LJK, F-38000 Grenoble, France
- Inria, F-38000 Grenoble, France
- Moscow Institute
of Physics and Technology, 141700 Dolgoprudny, Russia
| | - Sergei Grudinin
- Université Grenoble Alpes, Laboratoire Jean Kuntzmann (LJK), F-38000 Grenoble, France
- CNRS, LJK, F-38000 Grenoble, France
- Inria, F-38000 Grenoble, France
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185
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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] [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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186
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Wang N, McCammon JA. Substrate channeling between the human dihydrofolate reductase and thymidylate synthase. Protein Sci 2015; 25:79-86. [PMID: 26096018 DOI: 10.1002/pro.2720] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2015] [Revised: 06/02/2015] [Accepted: 06/05/2015] [Indexed: 12/17/2022]
Abstract
In vivo, as an advanced catalytic strategy, transient non-covalently bound multi-enzyme complexes can be formed to facilitate the relay of substrates, i. e. substrate channeling, between sequential enzymatic reactions and to enhance the throughput of multi-step enzymatic pathways. The human thymidylate synthase and dihydrofolate reductase catalyze two consecutive reactions in the folate metabolism pathway, and experiments have shown that they are very likely to bind in the same multi-enzyme complex in vivo. While reports on the protozoa thymidylate synthase-dihydrofolate reductase bifunctional enzyme give substantial evidences of substrate channeling along a surface "electrostatic highway," attention has not been paid to whether the human thymidylate synthase and dihydrofolate reductase, if they are in contact with each other in the multi-enzyme complex, are capable of substrate channeling employing surface electrostatics. This work utilizes protein-protein docking, electrostatics calculations, and Brownian dynamics to explore the existence and mechanism of the substrate channeling between the human thymidylate synthase and dihydrofolate reductase. The results show that the bound human thymidylate synthase and dihydrofolate reductase are capable of substrate channeling and the formation of the surface "electrostatic highway." The substrate channeling efficiency between the two can be reasonably high and comparable to that of the protozoa.
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Affiliation(s)
- Nuo Wang
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California, 92037
| | - J Andrew McCammon
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California, 92037.,Department of Pharmacology, University of California San Diego, La Jolla, California, 92037.,Howard Hughes Medical Institute, University of California San Diego, La Jolla, California, 92037
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187
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Krull F, Korff G, Elghobashi-Meinhardt N, Knapp EW. ProPairs: A Data Set for Protein–Protein Docking. J Chem Inf Model 2015; 55:1495-507. [DOI: 10.1021/acs.jcim.5b00082] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Florian Krull
- Institute of Chemistry and
Biochemistry, Freie Universität Berlin, Fabeckstrasse 36a, 14195 Berlin, Germany
| | - Gerrit Korff
- Institute of Chemistry and
Biochemistry, Freie Universität Berlin, Fabeckstrasse 36a, 14195 Berlin, Germany
| | - Nadia Elghobashi-Meinhardt
- Institute of Chemistry and
Biochemistry, Freie Universität Berlin, Fabeckstrasse 36a, 14195 Berlin, Germany
| | - Ernst-Walter Knapp
- Institute of Chemistry and
Biochemistry, Freie Universität Berlin, Fabeckstrasse 36a, 14195 Berlin, Germany
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188
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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] [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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189
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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] [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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