Evidence map›Paper›PMID 33386025›Full record

ArticleBriefings in bioinformatics2021

Identifying the natural polyphenol catechin as a multi-targeted agent against SARS-CoV-2 for the plausible therapy of COVID-19: an integrated computational approach.

Chandra Bhushan Mishra, Preeti Pandey, Ravi Datta Sharma, Md Zubbair Malik, Raj Kumar Mongre, Andrew M Lynn, Rajendra Prasad, Raok Jeon, Amresh Prakash

Open access · bronzeAbstract read
In one paragraph

Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed, 1 pooled it
8.9field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

49 citing papers in PubMed, 1 synthesis or guideline pooled it, 99 citations in OpenAlex.

  1. Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 4 institutions in 4 countries.

Chandra Bhushan MishraCollege of Pharmacy, Sookmyung Women's University, Seoul, South Korea.
Preeti PandeyDepartment of Chemistry & Biochemistry, University of Oklahoma, OK, USA.
Ravi Datta SharmaAmity Institute of Technology, Amity University, Haryana.
Md Zubbair MalikSchool of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India.
Raj Kumar MongreCollege of Pharmacy, Sookmyung Women's University, Seoul, South Korea.
Andrew M LynnSchool of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India.
Rajendra PrasadAmity Institute of Biotechnology and is the dean of Faculty of Science Engineering and Technology, Amity University Haryana, Haryana 122413, India.
Raok JeonCollege of Pharmacy, Sookmyung Women's University, Seoul, South Korea.
Amresh PrakashAmity Institute of Integrative Sciences and Health, Amity Institute of Integrative Sciences and Health, Amity University, Haryana.
Sookmyung Women's University · KRJawaharlal Nehru University · INAmity University · AEUniversity of Oklahoma · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The global pandemic crisis, coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has claimed the lives of millions of people across the world. Development and testing of anti-SARS-CoV-2 drugs or vaccines have not turned to be realistic within the timeframe needed to combat this pandemic. Here, we report a comprehensive computational approach to identify the multi-targeted drug molecules against the SARS-CoV-2 proteins, whichare crucially involved in the viral-host interaction, replication of the virus inside the host, disease progression and transmission of coronavirus infection. Virtual screening of 75 FDA-approved potential antiviral drugs against the target proteins, spike (S) glycoprotein, human angiotensin-converting enzyme 2 (hACE2), 3-chymotrypsin-like cysteine protease (3CLpro), cathepsin L (CTSL), nucleocapsid protein, RNA-dependent RNA polymerase (RdRp) and non-structural protein 6 (NSP6), resulted in the selection of seven drugs which preferentially bind to the target proteins. Further, the molecular interactions determined by molecular dynamics simulation revealed that among the 75 drug molecules, catechin can effectively bind to 3CLpro, CTSL, RBD of S protein, NSP6 and nucleocapsid protein. It is more conveniently involved in key molecular interactions, showing binding free energy (ΔGbind) in the range of -5.09 kcal/mol (CTSL) to -26.09 kcal/mol (NSP6). At the binding pocket, catechin is majorly stabilized by the hydrophobic interactions, displays ΔEvdW values: -7.59 to -37.39 kcal/mol. Thus, the structural insights of better binding affinity and favorable molecular interaction of catechin toward multiple target proteins signify that catechin can be potentially explored as a multi-targeted agent against COVID-19.

Indexed as

COVID-19 Drug TreatmentCatechinCOVID-19HumansMolecular Docking SimulationMolecular Dynamics SimulationPolyphenolsSARS-CoV-2CatechinPolyphenolscatechinCOVID-19free energy landscapemulti-targeted drugSARS-CoV-2

Identifiers

PMID33386025
PMCPMC7799228
OpenAlexW3114019386

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.