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

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.

Medienart:

E-Artikel

Erscheinungsjahr:

2021

Erschienen:

2021

Enthalten in:

Zur Gesamtaufnahme - volume:12

Enthalten in:

The journal of physical chemistry letters - 12(2021), 23 vom: 17. Juni, Seite 5494-5502

Sprache:

Englisch

Beteiligte Personen:

Pavlova, Anna [VerfasserIn]
Zhang, Zijian [VerfasserIn]
Acharya, Atanu [VerfasserIn]
Lynch, Diane L [VerfasserIn]
Pang, Yui Tik [VerfasserIn]
Mou, Zhongyu [VerfasserIn]
Parks, Jerry M [VerfasserIn]
Chipot, Chris [VerfasserIn]
Gumbart, James C [VerfasserIn]

Links:

Volltext

Themen:

ACE2 protein, human
Angiotensin-Converting Enzyme 2
EC 3.4.17.23
Journal Article
Spike Glycoprotein, Coronavirus
Spike protein, SARS-CoV-2

Anmerkungen:

Date Completed 28.06.2021

Date Revised 29.08.2023

published: Print-Electronic

Citation Status MEDLINE

doi:

10.1021/acs.jpclett.1c01494

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM326347399