Evidence map›Paper›PMID 34607208›Full record

ArticleJournal of molecular graphics & modelling2021

In silico identification of SARS-CoV-2 cell entry inhibitors from selected natural antivirals.

Yusuf Şimşek, Sahra Setenay Baran, Belma Aslım

Abstract read
In one paragraph

Article in Journal of molecular graphics & modelling, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
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

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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
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

3 authors.

Yusuf ŞimşekVocational School of Health Services, Gazi University, Ankara, Turkey. Electronic address: yusufsimsek@gazi.edu.tr.
Sahra Setenay BaranDepartment of Biology, Gazi University, Ankara, Turkey.
Belma AslımDepartment of Biology, Gazi University, Ankara, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of this study is to identify potential drug-like molecules against SARS-CoV-2 virus among the natural antiviral compounds published in the Encyclopedia of Traditional Chinese Medicine. To test inhibition capability of these compounds first, we docked them with Spike protein, angiotensin-converting enzyme 2 (ACE2) (PDB ID: 6M0J) and neuropilin 1 (NRP1) (PDB ID: 7JJC) receptors, and found significant docking scores with extra precision up to -11 kcal/mol. Then, their stability in the binding pockets were further evaluated with molecular dynamics simulation. Eight natural antiviral compounds were identified as potential inhibitors against SARS-CoV-2 cell entry after 200 ns molecular dynamics simulations. We found CMP-3, CMP-4, CMP-5, CMP-6 and CMP-8 are strong binders for the spike protein, CMP-1, CMP-2, CMP-4, CMP-5 and CMP-7 are strong binders for the neuropilin receptor, and CMP-5 is a strong binder for the ACE2. Quercetin derivatives (CMP-4, CMP-5, CMP-6 and CMP-7) were found highly stable in the active domain of NRP1, ACE2 and Spike protein. Especially, CMP-5 showed an inhibitory activity for all targets. These natural antivirals may be potential drug candidates for the prevention of SARS-CoV-2 infection.

Indexed as

COVID-19SARS-CoV-2Antiviral AgentsHumansMolecular Dynamics SimulationSpike Glycoprotein, CoronavirusVirus InternalizationAntiviral AgentsSpike Glycoprotein, CoronavirusACE2AntiviralsMolecular dockingMolecular dynamics simulationNeuropilinSARS-CoV-2Spike protein

Identifiers

PMID34607208
PMCPMC8479391

What OpenQuestion holds

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Registered trials

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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.