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Biochemical features and mutations of key proteins in SARS-CoV-2 and their impacts on RNA therapeutics

Authors
  • Zeng, Li1
  • Li, Dongying2
  • Tong, Weida2
  • Shi, Tieliu1
  • Ning, Baitang2
  • 1 Changde Research Centre for Artificial Intelligence and Biomedicine, College of Life and Environmental Sciences, Hunan University of Arts and Science, Changde, Hunan 415000, China
  • 2 National Center for Toxicological Research (NCTR), U.S. Food and Drug Administration (FDA), Jefferson, AR, 72079, United States
Type
Published Article
Journal
Biochemical pharmacology
Publisher
New York, NY : Elsevier Science Inc
Publication Date
Jan 19, 2021
Volume
189
Pages
114424–114424
Identifiers
DOI: 10.1016/j.bcp.2021.114424
PMID: 33482149
PMCID: PMC7816569
Source
PubMed Central
Keywords
Disciplines
  • Review
License
Unknown

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic. Three viral proteins, the spike protein (S) for attachment of virus to host cells, 3-chymotrypsin-like cysteine protease (Mpro) for digestion of viral polyproteins to functional proteins, and RNA-dependent-RNA-polymerase (RdRp) for RNA synthesis are the most critical proteins for virus infection and replication, rendering them the most important drug targets for both antibody and chemical drugs. Due to its low-fidelity polymerase, the virus is subject to frequent mutations. To date, the sequence data from tens of thousands of virus isolates have revealed hundreds of mutations. Although most mutations have a minimum consequence, a small number of non-synonymous mutations may alter the virulence and antigenicity of the mutants. To evaluate the effects of viral mutations on drug safety and efficacy, we reviewed the biochemical features of the three main proteins and their potentials as drug targets, and analyzed the mutation profiles and their impacts on RNA therapeutics. We believe that monitoring and predicting mutation-introduced protein conformational changes in the three key viral proteins and evaluating their binding affinities and enzymatic activities with the U.S. Food and Drug Administration (FDA) regulated drugs by using computational modeling and machine learning processes can provide valuable information for the consideration of drug efficacy and drug safety for drug developers and drug reviewers. Finally, we propose an interactive database for drug developers and reviewers to use in evaluating the safety and efficacy of U.S. FDA regulated drugs with regard to viral mutations.

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