气候变化领域集成服务门户(CCPortal): Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike RBD and human ACE2

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DOI	10.1073/pnas.2106480118
	Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike RBD and human ACE2
	Chen C.; Boorla V.S.; Banerjee D.; Chowdhury R.; Cavener V.S.; Nissly R.H.; Gontu A.; Boyle N.R.; Vandegrift K.; Nair M.S.; Kuchipudi S.V.; Maranas C.D.
发表日期	2021
ISSN	0027-8424
卷号	118 期号:42
英文摘要	The association of the receptor binding domain (RBD) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein with human angiotensin-converting enzyme 2 (hACE2) represents the first required step for cellular entry. SARS-CoV-2 has continued to evolve with the emergence of several novel variants, and amino acid changes in the RBD have been implicated with increased fitness and potential for immune evasion. Reliably predicting the effect of amino acid changes on the ability of the RBD to interact more strongly with the hACE2 can help assess the implications for public health and the potential for spillover and adaptation into other animals. Here, we introduce a two-step framework that first relies on 48 independent 4-ns molecular dynamics (MD) trajectories of RBD−hACE2 variants to collect binding energy terms decomposed into Coulombic, covalent, van der Waals, lipophilic, generalized Born solvation, hydrogen bonding, π−π packing, and self-contact correction terms. The second step implements a neural network to classify and quantitatively predict binding affinity changes using the decomposed energy terms as descriptors. The computational base achieves a validation accuracy of 82.8% for classifying single–amino acid substitution variants of the RBD as worsening or improving binding affinity for hACE2 and a correlation coefficient of 0.73 between predicted and experimentally calculated changes in binding affinities. Both metrics are calculated using a fivefold cross-validation test. Our method thus sets up a framework for screening binding affinity changes caused by unknown single–and multiple–amino acid changes offering a valuable tool to predict host adaptation of SARS-CoV-2 variants toward tighter hACE2 binding. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Binding affinity; Human ACE2; MM-GBSA; Neural network; SARS-CoV-2
语种	英语
scopus关键词	angiotensin converting enzyme 2; ACE2 protein, human; coronavirus spike glycoprotein; spike protein, SARS-CoV-2; amino acid metabolism; amino acid substitution; Article; artificial neural network; binding affinity; computer model; coronavirus disease 2019; cross validation; human; hydrogen bond; lipophilicity; molecular dynamics; prediction; receptor binding; Severe acute respiratory syndrome coronavirus 2; solvation; binding site; genetics; host pathogen interaction; metabolism; Amino Acid Substitution; Angiotensin-Converting Enzyme 2; Binding Sites; Host-Pathogen Interactions; Humans; Molecular Dynamics Simulation; Neural Networks, Computer; SARS-CoV-2; Spike Glycoprotein, Coronavirus
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/238779
作者单位	Department of Chemical Engineering, Pennsylvania State University, University Park, PA 16802, United States; Bioinformatics and Genomics Program, Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA 16802, United States; Department of Veterinary and Biomedical Sciences, Pennsylvania State University, University Park, PA 16802, United States; Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, Pennsylvania State University, University Park, PA 16802, United States; Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA 16802, United States
推荐引用方式 GB/T 7714	Chen C.,Boorla V.S.,Banerjee D.,et al. Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike RBD and human ACE2[J],2021,118(42).
APA	Chen C..,Boorla V.S..,Banerjee D..,Chowdhury R..,Cavener V.S..,...&Maranas C.D..(2021).Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike RBD and human ACE2.Proceedings of the National Academy of Sciences of the United States of America,118(42).
MLA	Chen C.,et al."Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike RBD and human ACE2".Proceedings of the National Academy of Sciences of the United States of America 118.42(2021).