Machine Learning Reveals the Critical Interactions for SARS-CoV-2 Spike Protein Binding to ACE2

被引：37

作者：

Pavlova, Anna ^{[1
]}

Zhang, Zijian ^{[1
]}

Acharya, Atanu ^{[1
]}

Lynch, Diane L. ^{[1
]}

Pang, Yui Tik ^{[1
]}

Mou, Zhongyu ^{[2
]}

Parks, Jerry M. ^{[2
]}

Chipot, Chris ^{[3
,4
]}

Gumbart, James C. ^{[1
]}

机构：

[1] Georgia Inst Technol, Sch Phys, Atlanta, GA 30332 USA

[2] Oak Ridge Natl Lab, Biosci Div, UT ORNL Ctr Mol Biophys, POB 2009, Oak Ridge, TN 37831 USA

[3] Lab Int Associe CNRS, F-54506 Vandoeuvre Les Nancy, France

[4] Univ Illinois, Dept Phys, Urbana, IL 61801 USA

来源：

JOURNAL OF PHYSICAL CHEMISTRY LETTERS | 2021年 / 12卷 / 23期

基金：

美国国家科学基金会;

关键词：

SARS-CORONAVIRUS; RECEPTOR-BINDING; DYNAMICS; ADAPTATION;

D O I：

10.1021/acs.jpclett.1c01494

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

SARS-CoV and SARS-CoV-2 bind to the human ACE2 receptor in practically identical conformations, although several residues of the receptor-binding domain (RBD) differ between them. Herein, we have used molecular dynamics (MD) simulations, machine learning (ML), and free-energy perturbation (FEP) calculations to elucidate the differences in binding by the two viruses. Although only subtle differences were observed from the initial MD simulations of the two RBD-ACE2 complexes, ML identified the individual residues with the most distinctive ACE2 interactions, many of which have been highlighted in previous experimental studies. FEP calculations quantified the corresponding differences in binding free energies to ACE2, and examination of MD trajectories provided structural explanations for these differences. Lastly, the energetics of emerging SARS-CoV-2 mutations were studied, showing that the affinity of the RBD for ACE2 is increased by N501Y and E484K mutations but is slightly decreased by K417N.

引用

页码：5494 / 5502

页数：9