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Liu Y, Gong W, Zhao Y, Deng X, Zhang S, Li C. aPRBind: protein-RNA interface prediction by combining sequence and I-TASSER model-based structural features learned with convolutional neural networks. Bioinformatics 2021; 37:937-942. [PMID: 32821925 DOI: 10.1093/bioinformatics/btaa747] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Revised: 07/26/2020] [Accepted: 08/17/2020] [Indexed: 12/13/2022] Open
Abstract
MOTIVATION Protein-RNA interactions play a critical role in various biological processes. The accurate prediction of RNA-binding residues in proteins has been one of the most challenging and intriguing problems in the field of computational biology. The existing methods still have a relatively low accuracy especially for the sequence-based ab-initio methods. RESULTS In this work, we propose an approach aPRBind, a convolutional neural network-based ab-initio method for RNA-binding residue prediction. aPRBind is trained with sequence features and structural ones (particularly including residue dynamics information and residue-nucleotide propensity developed by us) that are extracted from the predicted structures by I-TASSER. The analysis of feature contributions indicates the sequence features are most important, followed by dynamics information, and the sequence and structural features are complementary in binding site prediction. The performance comparison of our method with other peer ones on benchmark dataset shows that aPRBind outperforms some state-of-the-art ab-initio methods. Additionally, aPRBind can give a better prediction for the modeled structures with TM-score≥0.5, and meanwhile since the structural features are not very sensitive to the refined 3D structures, aPRBind has only a marginal dependence on the accuracy of the structure model, which allows aPRBind to be applied to the RNA-binding site prediction for the modeled or unbound structures. AVAILABILITY AND IMPLEMENTATION The source code is available at https://github.com/ChunhuaLiLab/aPRbind. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yang Liu
- Department of Biomedical Engineering, Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Weikang Gong
- Department of Biomedical Engineering, Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Yanpeng Zhao
- Department of Biomedical Engineering, Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Xueqing Deng
- Department of Biomedical Engineering, Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Shan Zhang
- Department of Biomedical Engineering, Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Chunhua Li
- Department of Biomedical Engineering, Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
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Lee JY, Krieger J, Herguedas B, García-Nafría J, Dutta A, Shaikh SA, Greger IH, Bahar I. Druggability Simulations and X-Ray Crystallography Reveal a Ligand-Binding Site in the GluA3 AMPA Receptor N-Terminal Domain. Structure 2018; 27:241-252.e3. [PMID: 30528594 DOI: 10.1016/j.str.2018.10.017] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Revised: 07/25/2018] [Accepted: 10/18/2018] [Indexed: 11/19/2022]
Abstract
Ionotropic glutamate receptors (iGluRs) mediate the majority of excitatory neurotransmission in the brain. Their dysfunction is implicated in many neurological disorders, rendering iGluRs potential drug targets. Here, we performed a systematic analysis of the druggability of two major iGluR subfamilies, using molecular dynamics simulations in the presence of drug-like molecules. We demonstrate the applicability of druggability simulations by faithfully identifying known agonist and modulator sites on AMPA receptors (AMPARs) and NMDA receptors. Simulations produced the expected allosteric changes of the AMPAR ligand-binding domain in response to agonist. We also identified a novel ligand-binding site specific to the GluA3 AMPAR N-terminal domain (NTD), resulting from its unique conformational flexibility that we explored further with crystal structures trapped in vastly different states. In addition to providing an in-depth analysis into iGluR NTD dynamics, our approach identifies druggable sites and permits the determination of pharmacophoric features toward novel iGluR modulators.
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Affiliation(s)
- Ji Young Lee
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Avenue, Suite 3064 BST3, Pittsburgh, PA 15260, USA
| | - James Krieger
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Avenue, Suite 3064 BST3, Pittsburgh, PA 15260, USA
| | - Beatriz Herguedas
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Francis Crick Avenue, Cambridge CB2 0QH, UK
| | - Javier García-Nafría
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Francis Crick Avenue, Cambridge CB2 0QH, UK
| | - Anindita Dutta
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Avenue, Suite 3064 BST3, Pittsburgh, PA 15260, USA
| | - Saher A Shaikh
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Francis Crick Avenue, Cambridge CB2 0QH, UK
| | - Ingo H Greger
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Francis Crick Avenue, Cambridge CB2 0QH, UK.
| | - Ivet Bahar
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Avenue, Suite 3064 BST3, Pittsburgh, PA 15260, USA.
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Tiwari SP, Reuter N. Conservation of intrinsic dynamics in proteins — what have computational models taught us? Curr Opin Struct Biol 2018; 50:75-81. [DOI: 10.1016/j.sbi.2017.12.001] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Revised: 11/24/2017] [Accepted: 12/08/2017] [Indexed: 12/12/2022]
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Arikawa K. Theoretical framework for analyzing structural compliance properties of proteins. Biophys Physicobiol 2018; 15:58-74. [PMID: 29607281 PMCID: PMC5873042 DOI: 10.2142/biophysico.15.0_58] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Accepted: 12/27/2017] [Indexed: 01/29/2023] Open
Abstract
We propose methods for directly analyzing structural compliance (SC) properties of elastic network models of proteins, and we also propose methods for extracting information about motion properties from the SC properties. The analysis of SC properties involves describing the relationships between the applied forces and the deformations. When decomposing the motion according to the magnitude of SC (SC mode decomposition), we can obtain information about the motion properties under the assumption that the lower SC mode motions or the softer motions occur easily. For practical applications, the methods are formulated in a general form. The parts where forces are applied and those where deformations are evaluated are separated from each other for enabling the analyses of allosteric interactions between the specified parts. The parts are specified not only by the points but also by the groups of points (the groups are treated as flexible bodies). In addition, we propose methods for quantitatively evaluating the properties based on the screw theory and the considerations of the algebraic structures of the basic equations expressing the SC properties. These methods enable quantitative discussions about the relationships between the SC mode motions and the motions estimated from two different conformations; they also help identify the key parts that play important roles for the motions by comparing the SC properties with those of partially constrained models. As application examples, lactoferrin and ATCase are analyzed. The results show that we can understand their motion properties through their lower SC mode motions or the softer motions.
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Affiliation(s)
- Keisuke Arikawa
- Department of Mechanical Engineering Kanagawa Institute of Technology, Atsugi, Kanagawa 243-0292, Japan
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Boehr DD, D'Amico RN, O'Rourke KF. Engineered control of enzyme structural dynamics and function. Protein Sci 2018; 27:825-838. [PMID: 29380452 DOI: 10.1002/pro.3379] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2017] [Revised: 01/20/2018] [Accepted: 01/24/2018] [Indexed: 12/20/2022]
Abstract
Enzymes undergo a range of internal motions from local, active site fluctuations to large-scale, global conformational changes. These motions are often important for enzyme function, including in ligand binding and dissociation and even preparing the active site for chemical catalysis. Protein engineering efforts have been directed towards manipulating enzyme structural dynamics and conformational changes, including targeting specific amino acid interactions and creation of chimeric enzymes with new regulatory functions. Post-translational covalent modification can provide an additional level of enzyme control. These studies have not only provided insights into the functional role of protein motions, but they offer opportunities to create stimulus-responsive enzymes. These enzymes can be engineered to respond to a number of external stimuli, including light, pH, and the presence of novel allosteric modulators. Altogether, the ability to engineer and control enzyme structural dynamics can provide new tools for biotechnology and medicine.
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Affiliation(s)
- David D Boehr
- Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania, 16802, USA
| | - Rebecca N D'Amico
- Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania, 16802, USA
| | - Kathleen F O'Rourke
- Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania, 16802, USA
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Song K, Liu X, Huang W, Lu S, Shen Q, Zhang L, Zhang J. Improved Method for the Identification and Validation of Allosteric Sites. J Chem Inf Model 2017; 57:2358-2363. [DOI: 10.1021/acs.jcim.7b00014] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Kun Song
- Department of Pathophysiology, Key Laboratory
of Cell Differentiation and Apoptosis of National Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai, 200025, China
| | - Xinyi Liu
- Department of Pathophysiology, Key Laboratory
of Cell Differentiation and Apoptosis of National Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai, 200025, China
| | - Wenkang Huang
- Department of Pathophysiology, Key Laboratory
of Cell Differentiation and Apoptosis of National Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai, 200025, China
| | - Shaoyong Lu
- Department of Pathophysiology, Key Laboratory
of Cell Differentiation and Apoptosis of National Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai, 200025, China
| | - Qiancheng Shen
- Department of Pathophysiology, Key Laboratory
of Cell Differentiation and Apoptosis of National Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai, 200025, China
| | - Lu Zhang
- Department of Pathophysiology, Key Laboratory
of Cell Differentiation and Apoptosis of National Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai, 200025, China
| | - Jian Zhang
- Department of Pathophysiology, Key Laboratory
of Cell Differentiation and Apoptosis of National Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai, 200025, China
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Yura K. Preface of Special Issue "Protein-Ligand Interactions". Biophys Physicobiol 2016; 13:85-86. [PMID: 27924261 PMCID: PMC5042156 DOI: 10.2142/biophysico.13.0_85] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Affiliation(s)
- Kei Yura
- Graduate School of Humanities and Sciences, Ochanomizu University, 2-1-1 Otsuka, Bunkyo, Tokyo 112-8610, Japan; Center for Informational Biology, Ochanomizu University, 2-1-1 Otsuka, Bunkyo, Tokyo 112-8610, Japan; National Institute of Genetics, 1111 Yata, Mishima, Shizuoka 411-8540, Japan
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