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Computational Prediction of Mutational Effects on SARS-CoV-2 Binding by Relative Free Energy Calculations
Author(s) -
Junjie Zou,
Jian Yin,
Lei Fang,
Mingjun Yang,
Tianyuan Wang,
Weikun Wu,
Michael A. Bellucci,
Peiyu Zhang
Publication year - 2020
Publication title -
journal of chemical information and modeling
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.24
H-Index - 160
eISSN - 1549-960X
pISSN - 1549-9596
DOI - 10.1021/acs.jcim.0c00679
Subject(s) - alanine scanning , covid-19 , plasma protein binding , docking (animal) , angiotensin converting enzyme 2 , mutagenesis , computational biology , alanine , biology , binding site , virology , chemistry , mutation , genetics , microbiology and biotechnology , gene , infectious disease (medical specialty) , medicine , disease , amino acid , nursing , pathology , outbreak
The ability of coronaviruses to infect humans is invariably associated with their binding strengths to human receptor proteins. Both SARS-CoV-2, initially named 2019-nCoV, and SARS-CoV were reported to utilize angiotensin-converting enzyme 2 (ACE2) as an entry receptor in human cells. To better understand the interplay between SARS-CoV-2 and ACE2, we performed computational alanine scanning mutagenesis on the "hotspot" residues at protein-protein interfaces using relative free energy calculations. Our data suggest that the mutations in SARS-CoV-2 lead to a greater binding affinity relative to SARS-CoV. In addition, our free energy calculations provide insight into the infectious ability of viruses on a physical basis and also provide useful information for the design of antiviral drugs.

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