Evidence map›Paper›PMID 35262828›Full record

ArticleJournal, genetic engineering & biotechnology2022

In silico discovery of 3 novel quercetin derivatives against papain-like protease, spike protein, and 3C-like protease of SARS-CoV-2.

Kunal Bhattacharya, Ripunjoy Bordoloi, Nongmaithem Randhoni Chanu, Ramen Kalita, Bhargab Jyoti Sahariah, Atanu Bhattacharjee

Open access · diamondAbstract read
In one paragraph

Article in Journal, genetic engineering & biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
3.9field-weighted citation impact, top 6% 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

6 citing papers in PubMed, 27 citations in OpenAlex.

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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

6 authors at 2 institutions in 1 country.

Kunal BhattacharyaNETES Institute of Pharmaceutical Science, Mirza, Guwahati, Assam, 781125, India. kunal22101994@gmail.com.ORCID http://orcid.org/0000-0002-5546-1504
Ripunjoy BordoloiNETES Institute of Pharmaceutical Science, Mirza, Guwahati, Assam, 781125, India.
Nongmaithem Randhoni ChanuFaculty of Pharmaceutical Science, Assam Downtown University, Guwahati, Assam, India.
Ramen KalitaNETES Institute of Pharmaceutical Science, Mirza, Guwahati, Assam, 781125, India.
Bhargab Jyoti SahariahNETES Institute of Pharmaceutical Science, Mirza, Guwahati, Assam, 781125, India.
Atanu BhattacharjeeRoyal School of Pharmacy, Royal Global University, Guwahati, Assam, India.
National Institute of Pharmaceutical Education and Research · INAssam Down Town University · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe derivatives of quercetin is known for their immune-modulating antiviral, anti-blood clotting, antioxidant, and also for its anti-inflammatory efficacy. The current study was therefore conducted to examine the noted novel derivatives of quercetin present in plant sources as an immune modulator and as an antiviral molecule in the COVID-19 disease and also to study their affinity of binding with potential three targets reported for coronavirus, i.e., papain-like protease, spike protein receptor-binding domain, and 3C-like protease. Based on the high-positive drug-likeness score, the reported derivatives of quercetin obtained from an open-source database were further filtered. Compounds with positive and high drug-likeness scores were further predicted for their potential targets using DIGEP-Pred software, and STRING was used to evaluate the interaction between modulated proteins. The associated pathways were recorded based on the Kyoto Encyclopedia of Genes and Genomes pathway database. Docking was performed finally using PyRx having AutoDock Vina to identify the efficacy of binding between quercetin derivatives with papain-like protease, spike protein receptor-binding domain, and 3C-like protease. The ligand that scored minimum binding energy was chosen to visualize the interaction between protein and ligand. Normal mode analysis in internal coordinates was done with normal mode analysis to evaluate the physical movement and stability of the best protein-ligand complexes using the iMODS server.

resultsForty bioactive compounds with the highest positive drug-likeness scores were identified. These 40 bioactives were responsible for regulating different pathways associated with antiviral activity and modulation of immunity. Finally, three lead molecules were identified based on the molecular docking and dynamics simulation studies with the highest anti-COVID-19 and immunomodulatory potentials. Standard antiviral drug remdesivir on docking showed a binding affinity of - 5.8 kcal/mol with PLpro, - 6.4 kcal/mol with 3CLpro, and - 8.6 kcal/mol with spike protein receptor-binding domain of SARS-CoV-2, the discovered hit molecules quercetin 3-O-arabinoside 7-O-rhamnoside showed binding affinity of - 8.2 kcal/mol with PLpro, whereas quercetin 3-[rhamnosyl-(1- > 2)-alpha-L-arabinopyranoside] and quercetin-3-neohesperidoside-7-rhamnoside was predicted to have a binding affinity of - 8.5 kcal/mol and - 8.8 kcal/mol with spike protein receptor-binding domain and 3CLpro respectively

conclusionDocking study revealed quercetin 3-O-arabinoside 7-O-rhamnoside to possess the highest binding affinity with papain-like protease, quercetin 3-[rhamnosyl-(1- > 2)-alpha-L-arabinopyranoside] with spike protein receptor-binding domain, and quercetin-3-neohesperidoside-7-rhamnoside with 3C-like protease and all the protein-ligand complexes were found to be stable after performing the normal mode analysis of the complexes in internal coordinates.

Indexed as

3CLproPLproQuercetin derivativesSARS-CoV-2Spike protein

Identifiers

PMID35262828
PMCPMC8905286
OpenAlexW4220828492

What OpenQuestion holds

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LicenceCC BY
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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.