A Computational Pipeline to Identify and Characterize Binding Sites and Interacting Chemotypes in SARS-CoV-2-Reference-Cited by-同舟云学术

A Computational Pipeline to Identify and Characterize Binding Sites and Interacting Chemotypes in SARS-CoV-2

Published:2023-06-06 Issue:24 Volume:8 Page:21871-21884
ISSN:2470-1343
Container-title:ACS Omega
language:en
Short-container-title:ACS Omega

Author:

Sandholtz Sarah H.¹^ORCID,Drocco Jeffrey A.¹,Zemla Adam T.²^ORCID,Torres Marisa W.²,Silva Mary S.²,Allen Jonathan E.²^ORCID

Affiliation:

1. Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States of America

2. Global Security Computing Applications Division, Computing Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States of America

Funder

U.S. Department of Energy

U.S. Department of Defense

Publisher

American Chemical Society (ACS)

Subject

General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acsomega.3c01621

Reference54 articles.

1. Supervised molecular dynamics for exploring the druggability of the SARS-CoV-2 spike protein

2. Class A G Protein-Coupled Receptor Antagonist Famotidine as a Therapeutic Alternative against SARS-CoV2: An In Silico Analysis

3. Structural elucidation of SARS-CoV-2 vital proteins: Computational methods reveal potential drug candidates against main protease, Nsp12 polymerase and Nsp13 helicase

4. In silico identification of potential inhibitors of key SARS-CoV-2 3CL hydrolase (Mpro) via molecular docking, MMGBSA predictive binding energy calculations, and molecular dynamics simulation

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