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Gong W, Liu Y, Zhao Y, Wang S, Han Z, Li C. Equally Weighted Multiscale Elastic Network Model and Its Comparison with Traditional and Parameter-Free Models. J Chem Inf Model 2021; 61:921-937. [PMID: 33496590 DOI: 10.1021/acs.jcim.0c01178] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Dynamical properties of proteins play an essential role in their function exertion. The elastic network model (ENM) is an effective and efficient tool in characterizing the intrinsic dynamical properties encoded in biomacromolecule structures. The Gaussian network model (GNM) and anisotropic network model (ANM) are the two often-used ENM models. Here, we introduce an equally weighted multiscale ENM (equally weighted mENM) based on the original mENM (denoted as mENM), in which fitting weights of Kirchhoff/Hessian matrixes in mENM are removed since they neglect the details of pairwise interactions. Then, we perform its comparison with the mENM, traditional ENM, and parameter-free ENM (pfENM) in reproducing dynamical properties for the six representative proteins whose molecular dynamics (MD) trajectories are available in http://mmb.pcb.ub.es/MoDEL/. In the results, for B-factor prediction, mENM performs best, while the equally weighted mENM performs also well, better than the traditional ENM and pfENM models. As to the dynamical cross-correlation map calculation, mENM performs worst, while the results produced from the equally weighted mENM and pfENM models are close to those from MD trajectories with the latter a little better than the former. Furthermore, encouragingly, the equally weighted mANM displays the best performance in capturing the functional motional modes, followed by pfANM and traditional ANM models, while the mANM fails in all the cases. This work is helpful for strengthening the understanding of the elastic network model and provides a valuable guide for researchers to utilize the model to explore protein dynamics.
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Affiliation(s)
- Weikang Gong
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China.,Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing University of Technology, Beijing 100124, China
| | - Yang Liu
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China.,Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing University of Technology, Beijing 100124, China
| | - Yanpeng Zhao
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China.,Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing University of Technology, Beijing 100124, China
| | - Shihao Wang
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China.,Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing University of Technology, Beijing 100124, China
| | - Zhongjie Han
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China.,Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing University of Technology, Beijing 100124, China
| | - Chunhua Li
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China.,Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing University of Technology, Beijing 100124, China
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Gaussian network model can be enhanced by combining solvent accessibility in proteins. Sci Rep 2017; 7:7486. [PMID: 28790346 PMCID: PMC5548781 DOI: 10.1038/s41598-017-07677-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Accepted: 06/29/2017] [Indexed: 01/03/2023] Open
Abstract
Gaussian network model (GNM), regarded as the simplest and most representative coarse-grained model, has been widely adopted to analyze and reveal protein dynamics and functions. Designing a variation of the classical GNM, by defining a new Kirchhoff matrix, is the way to improve the residue flexibility modeling. We combined information arising from local relative solvent accessibility (RSA) between two residues into the Kirchhoff matrix of the parameter-free GNM. The undetermined parameters in the new Kirchhoff matrix were estimated by using particle swarm optimization. The usage of RSA was motivated by the fact that our previous work using RSA based linear regression model resulted out higher prediction quality of the residue flexibility when compared with the classical GNM and the parameter free GNM. Computational experiments, conducted based on one training dataset, two independent datasets and one additional small set derived by molecular dynamics simulations, demonstrated that the average correlation coefficients of the proposed RSA based parameter-free GNM, called RpfGNM, were significantly increased when compared with the parameter-free GNM. Our empirical results indicated that a variation of the classical GNMs by combining other protein structural properties is an attractive way to improve the quality of flexibility modeling.
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Narayanan C, Bafna K, Roux LD, Agarwal PK, Doucet N. Applications of NMR and computational methodologies to study protein dynamics. Arch Biochem Biophys 2017; 628:71-80. [PMID: 28483383 DOI: 10.1016/j.abb.2017.05.002] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2017] [Revised: 05/03/2017] [Accepted: 05/04/2017] [Indexed: 02/07/2023]
Abstract
Overwhelming evidence now illustrates the defining role of atomic-scale protein flexibility in biological events such as allostery, cell signaling, and enzyme catalysis. Over the years, spin relaxation nuclear magnetic resonance (NMR) has provided significant insights on the structural motions occurring on multiple time frames over the course of a protein life span. The present review article aims to illustrate to the broader community how this technique continues to shape many areas of protein science and engineering, in addition to being an indispensable tool for studying atomic-scale motions and functional characterization. Continuing developments in underlying NMR technology alongside software and hardware developments for complementary computational approaches now enable methodologies to routinely provide spatial directionality and structural representations traditionally harder to achieve solely using NMR spectroscopy. In addition to its well-established role in structural elucidation, we present recent examples that illustrate the combined power of selective isotope labeling, relaxation dispersion experiments, chemical shift analyses, and computational approaches for the characterization of conformational sub-states in proteins and enzymes.
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Affiliation(s)
- Chitra Narayanan
- INRS-Institut Armand-Frappier, Université du Québec, 531 Boul. des Prairies, Laval, QC H7V 1B7, Canada
| | - Khushboo Bafna
- Genome Science and Technology, University of Tennessee, Knoxville, TN 37996, USA
| | - Louise D Roux
- INRS-Institut Armand-Frappier, Université du Québec, 531 Boul. des Prairies, Laval, QC H7V 1B7, Canada
| | - Pratul K Agarwal
- Department of Biochemistry, Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA; Computational Biology Institute and Computer Science and Mathematics Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN 37830, USA
| | - Nicolas Doucet
- INRS-Institut Armand-Frappier, Université du Québec, 531 Boul. des Prairies, Laval, QC H7V 1B7, Canada; PROTEO, The Quebec Network for Research on Protein Function, Structure, and Engineering, 1045 Avenue de la Médecine, Université Laval, Québec, QC G1V 0A6, Canada; GRASP, The Groupe de Recherche Axé sur la Structure des Protéines, 3649 Promenade Sir William Osler, McGill University, Montréal, QC H3G 0B1, Canada.
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Identification of Hot Spots in Protein Structures Using Gaussian Network Model and Gaussian Naive Bayes. BIOMED RESEARCH INTERNATIONAL 2016; 2016:4354901. [PMID: 27882325 PMCID: PMC5110947 DOI: 10.1155/2016/4354901] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2016] [Revised: 10/02/2016] [Accepted: 10/11/2016] [Indexed: 01/21/2023]
Abstract
Residue fluctuations in protein structures have been shown to be highly associated with various protein functions. Gaussian network model (GNM), a simple representative coarse-grained model, was widely adopted to reveal function-related protein dynamics. We directly utilized the high frequency modes generated by GNM and further performed Gaussian Naive Bayes (GNB) to identify hot spot residues. Two coding schemes about the feature vectors were implemented with varying distance cutoffs for GNM and sliding window sizes for GNB based on tenfold cross validations: one by using only a single high mode and the other by combining multiple modes with the highest frequency. Our proposed methods outperformed the previous work that did not directly utilize the high frequency modes generated by GNM, with regard to overall performance evaluated using F1 measure. Moreover, we found that inclusion of more high frequency modes for a GNB classifier can significantly improve the sensitivity. The present study provided additional valuable insights into the relation between the hot spots and the residue fluctuations.
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