Evidence map›Paper›PMID 38792173›Full record

ArticleMolecules (Basel, Switzerland)2024

Structure-Based Virtual Screening for Methyltransferase Inhibitors of SARS-CoV-2 nsp14 and nsp16.

Kejue Wu, Yinfeng Guo, Tiefeng Xu, Weifeng Huang, Deyin Guo, Liu Cao, Jinping Lei

Abstract read
In one paragraph

Article in Molecules (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

7 authors.

Kejue WuGuangdong Key Laboratory of Chiral Molecule and Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou 510006, China.
Yinfeng GuoGuangdong Key Laboratory of Chiral Molecule and Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou 510006, China.
Tiefeng XuCentre for Infection and Immunity Studies (CIIS), School of Medicine, Sun Yat-Sen University, Shenzhen 518107, China.
Weifeng HuangGuangdong Key Laboratory of Chiral Molecule and Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou 510006, China.ORCID 0009-0006-9335-3877
Deyin GuoCentre for Infection and Immunity Studies (CIIS), School of Medicine, Sun Yat-Sen University, Shenzhen 518107, China.
Liu CaoCentre for Infection and Immunity Studies (CIIS), School of Medicine, Sun Yat-Sen University, Shenzhen 518107, China.
Jinping LeiGuangdong Key Laboratory of Chiral Molecule and Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou 510006, China.

Funding

Guangdong-Hong Kong Technology Cooperation Funding Scheme 2023A0505010015the National Key Research and Development Program of China 2023YFF1204900, 2023YFF1204902
6 · The paper itself

Abstract

The ongoing COVID-19 pandemic still threatens human health around the world. The methyltransferases (MTases) of SARS-CoV-2, specifically nsp14 and nsp16, play crucial roles in the methylation of the N7 and 2'-O positions of viral RNA, making them promising targets for the development of antiviral drugs. In this work, we performed structure-based virtual screening for nsp14 and nsp16 using the screening workflow (HTVS, SP, XP) of Schrödinger 2019 software, and we carried out biochemical assays and molecular dynamics simulation for the identification of potential MTase inhibitors. For nsp14, we screened 239,000 molecules, leading to the identification of three hits A1-A3 showing N7-MTase inhibition rates greater than 60% under a concentration of 50 µM. For the SAM binding and nsp10-16 interface sites of nsp16, the screening of 210,000 and 237,000 molecules, respectively, from ZINC15 led to the discovery of three hit compounds B1-B3 exhibiting more than 45% of 2'-O-MTase inhibition under 50 µM. These six compounds with moderate MTase inhibitory activities could be used as novel candidates for the further development of anti-SARS-CoV-2 drugs.

Indexed as

Antiviral AgentsEnzyme InhibitorsMethyltransferasesMolecular Dynamics SimulationSARS-CoV-2Viral Nonstructural ProteinsBinding SitesCOVID-19COVID-19 Drug TreatmentDrug Evaluation, PreclinicalExoribonucleasesHumansMolecular Docking SimulationAntiviral AgentsEnzyme InhibitorsExoribonucleasesMethyltransferasesNSP14 protein, SARS-CoV-2NSP16 protein, SARS-CoV-2Viral Nonstructural ProteinsMTase inhibitorsnsp14nsp16SARS-CoV-2structure-based virtual screening

Identifiers

PMID38792173
PMCPMC11124212

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.