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Theoretical study of the adiponectin receptors: binding site characterization and molecular dynamics of possible ligands for drug design. SN APPLIED SCIENCES 2020. [DOI: 10.1007/s42452-020-2333-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
AbstractThe two adiponectin receptors (AdipoR1 and AdipoR2) have been implicated in glucose and lipid regulation involved in several metabolic pathologies including type II diabetes. Their exact biochemical functions and mechanisms remain poorly understood. Moreover, these receptors do not yet have data on possible co-crystallized active ligands. In this study, we applied different computational methodologies to address three main unanswered questions: first, the localization and validation of possible binding sites; second, the generation of novel ligands with amenable characteristics to target the receptors; and third, the determination of important chemical interactions between the ligands and the receptors. Computational analysis of the binding site reveals that the residues triad R267, F271, and Y310 could be responsible for changes in the spatial arrangement and geometry of the binding pocket in AdipoR1. Molecular docking results in high docking scores of − 13.6 and − 16.5 kcal/mol for the top best ligands in AdipoR1 and AdipoR2 respectively. Finally, molecular dynamics suggests that hydrolytic activity may be possible with these compounds and that this reaction could be mediated by aspartic acid residues. The two adiponectin receptors have an endogenous protein ligand, adiponectin. However the synthesis is expensive and technically challenging. Although some debatable agonists have been proposed investigations of suitable synthetic ligands are indeed, very much needed for targeting these receptors and their associate pathologies and metabolic pathways. Furthermore, these findings provide a framework for further biochemical investigations of amenable compounds for drug discovery in order to target these receptors and their associated pathologies.
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Huang BC, Yang LW. Molecular dynamics simulations and linear response theories jointly describe biphasic responses of myoglobin relaxation and reveal evolutionarily conserved frequent communicators. Biophys Physicobiol 2020; 16:473-484. [PMID: 31984199 PMCID: PMC6975898 DOI: 10.2142/biophysico.16.0_473] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Accepted: 09/20/2019] [Indexed: 12/01/2022] Open
Abstract
In this study, we provide a time-dependent mechanical model, taking advantage of molecular dynamics simulations, quasiharmonic analysis of molecular dynamics trajectories, and time-dependent linear response theories to describe vibrational energy redistribution within the protein matrix. The theoretical description explained the observed biphasic responses of specific residues in myoglobin to CO-photolysis and photoexcitation on heme. The fast responses were found to be triggered by impulsive forces and propagated mainly by principal modes <40 cm−1. The predicted fast responses for individual atoms were then used to study signal propagation within the protein matrix and signals were found to propagate ~8 times faster across helices (4076 m/s) than within the helices, suggesting the importance of tertiary packing in the sensitivity of proteins to external perturbations. We further developed a method to integrate multiple intramolecular signal pathways and discover frequent “communicators”. These communicators were found to be evolutionarily conserved including those distant from the heme.
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Affiliation(s)
- Bang-Chieh Huang
- Institute of Bioinformatics and Structural Biology, National Tsing-Hua University, Hsinchu 30013, Taiwan
| | - Lee-Wei Yang
- Institute of Bioinformatics and Structural Biology, National Tsing-Hua University, Hsinchu 30013, Taiwan.,Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Sciences, Academia Sinica, Taipei 11529, Taiwan.,Department of Life Science, National Tsing Hua University, Hsinchu 30013, Taiwan.,Physics Division, National Center for Theoretical Sciences, Hsinchu 30013, Taiwan
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Chan J, Takemura K, Lin HR, Chang KC, Chang YY, Joti Y, Kitao A, Yang LW. An Efficient Timer and Sizer of Biomacromolecular Motions. Structure 2019; 28:259-269.e8. [PMID: 31780433 DOI: 10.1016/j.str.2019.10.020] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2019] [Revised: 08/22/2019] [Accepted: 10/30/2019] [Indexed: 11/28/2022]
Abstract
Life ticks as fast as how proteins move. Computationally expensive molecular dynamics simulation has been the only theoretical tool to gauge the time and sizes of these motions, though barely to their slowest ends. Here, we convert a computationally cheap elastic network model (ENM) into a molecular timer and sizer to gauge the slowest functional motions of structured biomolecules. Quasi-harmonic analysis, fluctuation profile matching, and the Wiener-Khintchine theorem are used to define the "time periods," t, for anharmonic principal components (PCs), which are validated by nuclear magnetic resonance (NMR) order parameters. The PCs with their respective "time periods" are mapped to the eigenvalues (λENM) of the corresponding ENM modes. Thus, the power laws t(ns) = 56.1λENM-1.6 and σ2(Å2) = 32.7λENM-3.0 can be established allowing the characterization of the timescales of NMR-resolved conformers, crystallographic anisotropic displacement parameters, and important ribosomal motions, as well as motional sizes of the latter.
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Affiliation(s)
- Justin Chan
- Institute of Bioinformatics and Structural Biology, National Tsing Hua Univ., No. 101, Section 2, Kuang-Fu Road, Hsinchu 30013, Taiwan; Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Kazuhiro Takemura
- School of Life Science and Technology, Tokyo Institute of Technology, M6-13, 2-12-1 Ookayama, Meguro, Tokyo 152-8550, Japan
| | - Hong-Rui Lin
- Institute of Bioinformatics and Structural Biology, National Tsing Hua Univ., No. 101, Section 2, Kuang-Fu Road, Hsinchu 30013, Taiwan
| | - Kai-Chun Chang
- Institute of Molecular and Cellular Biology, National Taiwan University, Taipei, Taiwan
| | - Yuan-Yu Chang
- Institute of Bioinformatics and Structural Biology, National Tsing Hua Univ., No. 101, Section 2, Kuang-Fu Road, Hsinchu 30013, Taiwan
| | - Yasumasa Joti
- XFEL Utilization Division, Japan Synchrotron Radiation Research Institute, 1-1-1 Kouto, Sayo-cho, Sayo-gun, Hyogo 679-5198, Japan
| | - Akio Kitao
- School of Life Science and Technology, Tokyo Institute of Technology, M6-13, 2-12-1 Ookayama, Meguro, Tokyo 152-8550, Japan.
| | - Lee-Wei Yang
- Institute of Bioinformatics and Structural Biology, National Tsing Hua Univ., No. 101, Section 2, Kuang-Fu Road, Hsinchu 30013, Taiwan; Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan; Physics Division, National Center for Theoretical Sciences, Hsinchu, Taiwan.
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Li H, Chang YY, Lee JY, Bahar I, Yang LW. DynOmics: dynamics of structural proteome and beyond. Nucleic Acids Res 2019; 45:W374-W380. [PMID: 28472330 PMCID: PMC5793847 DOI: 10.1093/nar/gkx385] [Citation(s) in RCA: 99] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2017] [Accepted: 04/25/2017] [Indexed: 01/07/2023] Open
Abstract
DynOmics (dynomics.pitt.edu) is a portal developed to leverage rapidly growing structural proteomics data by efficiently and accurately evaluating the dynamics of structurally resolved systems, from individual molecules to large complexes and assemblies, in the context of their physiological environment. At the core of the portal is a newly developed server, ENM 1.0, which permits users to efficiently generate information on the collective dynamics of any structure in PDB format, user-uploaded or database-retrieved. ENM 1.0 integrates two widely used elastic network models (ENMs)—the Gaussian Network Model (GNM) and the Anisotropic Network Model (ANM), extended to take account of molecular environment. It enables users to assess potentially functional sites, signal transduction or allosteric communication mechanisms, and protein–protein and protein–DNA interaction poses, in addition to delivering ensembles of accessible conformers reconstructed at atomic details based on the global modes of motions predicted by the ANM. The ‘environment’ is defined in a flexible manner, from lipid bilayer and crystal contacts, to substrate or ligands bound to a protein, or surrounding subunits in a multimeric structure or assembly. User-friendly interactive features permit users to easily visualize how the environment alter the intrinsic dynamics of the query systems. ENM 1.0 can be accessed at http://enm.pitt.edu/ or http://dyn.life.nthu.edu.tw/oENM/.
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Affiliation(s)
- Hongchun Li
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh PA, 15213, USA
| | - Yuan-Yu Chang
- Institute of Bioinformatics and Structural Biology, National Tsing-Hua University, Taiwan
| | - Ji Young Lee
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh PA, 15213, USA
| | - Ivet Bahar
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh PA, 15213, USA
| | - Lee-Wei Yang
- Institute of Bioinformatics and Structural Biology, National Tsing-Hua University, Taiwan
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Corona RI, Sudarshan S, Aluru S, Guo JT. An SVM-based method for assessment of transcription factor-DNA complex models. BMC Bioinformatics 2018; 19:506. [PMID: 30577740 PMCID: PMC6302363 DOI: 10.1186/s12859-018-2538-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Background Atomic details of protein-DNA complexes can provide insightful information for better understanding of the function and binding specificity of DNA binding proteins. In addition to experimental methods for solving protein-DNA complex structures, protein-DNA docking can be used to predict native or near-native complex models. A docking program typically generates a large number of complex conformations and predicts the complex model(s) based on interaction energies between protein and DNA. However, the prediction accuracy is hampered by current approaches to model assessment, especially when docking simulations fail to produce any near-native models. Results We present here a Support Vector Machine (SVM)-based approach for quality assessment of the predicted transcription factor (TF)-DNA complex models. Besides a knowledge-based protein-DNA interaction potential DDNA3, we applied several structural features that have been shown to play important roles in binding specificity between transcription factors and DNA molecules to quality assessment of complex models. To address the issue of unbalanced positive and negative cases in the training dataset, we applied hard-negative mining, an iterative training process that selects an initial training dataset by combining all of the positive cases and a random sample from the negative cases. Results show that the SVM model greatly improves prediction accuracy (84.2%) over two knowledge-based protein-DNA interaction potentials, orientation potential (60.8%) and DDNA3 (68.4%). The improvement is achieved through reducing the number of false positive predictions, especially for the hard docking cases, in which a docking algorithm fails to produce any near-native complex models. Conclusions A learning-based SVM scoring model with structural features for specific protein-DNA binding and an atomic-level protein-DNA interaction potential DDNA3 significantly improves prediction accuracy of complex models by successfully identifying cases without near-native structural models. Electronic supplementary material The online version of this article (10.1186/s12859-018-2538-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Rosario I Corona
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA
| | - Sanjana Sudarshan
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA
| | - Srinivas Aluru
- School of Computational Science and Engineering, Georgia Institute of Technology, 266 Ferst Drive, Atlanta, GA, 30332, USA
| | - Jun-Tao Guo
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA.
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