Evidence map›Paper›PMID 32967116›Full record

ArticleBiomolecules2020

Recognition of Potential COVID-19 Drug Treatments through the Study of Existing Protein-Drug and Protein-Protein Structures: An Analysis of Kinetically Active Residues.

Ognjen Perišić

Abstract readComparative Study
In one paragraph

Article in Biomolecules, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Targeting protein-protein interaction interfaces in COVID-19 drug discovery.Computational and structural biotechnology journal · 2021
    Review
  8. Article
  9. 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

1 author.

Ognjen PerišićBig Blue Genomics, Vojvode Brane 32, 11000 Belgrade, Serbia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We report the results of our in silico study of approved drugs as potential treatments for COVID-19. The study is based on the analysis of normal modes of proteins. The drugs studied include chloroquine, ivermectin, remdesivir, sofosbuvir, boceprevir, and α-difluoromethylornithine (DMFO). We applied the tools we developed and standard tools used in the structural biology community. Our results indicate that small molecules selectively bind to stable, kinetically active residues and residues adjoining them on the surface of proteins and inside protein pockets, and that some prefer hydrophobic sites over other active sites. Our approach is not restricted to viruses and can facilitate rational drug design, as well as improve our understanding of molecular interactions, in general.

Indexed as

PandemicsAdenosine MonophosphateAlanineAngiotensin-Converting Enzyme 2Antibodies, ViralAntigen-Antibody ReactionsAntiviral AgentsBetacoronavirusBinding SitesChloroquineCoronavirus InfectionsCOVID-19COVID-19 Drug TreatmentDrug RepositioningEflornithineHumansACE2 protein, humanAdenosine MonophosphateAlanineAngiotensin-Converting Enzyme 2Antibodies, ViralAntiviral AgentsChloroquineEflornithineglycine receptor alpha3 subunitIvermectinL-Lactate DehydrogenaseN-(3-amino-1-(cyclobutylmethyl)-2,3-dioxopropyl)-3-(2-((((1,1-dimethylethyl)amino)carbonyl)amino)-3,3-dimethyl-1-oxobutyl)-6,6-dimethyl-3-azabicyclo(3.1.0)hexan-2-carboxamidePeptidyl-Dipeptidase AProlineReceptors, GlycineremdesivirSaposinsSofosbuvirSpike Glycoprotein, Coronavirusspike protein, SARS-CoV-2boceprevirchloroquineCOVID-19ivermectinnormal-modesprotein–drug interactionsproteinsremdesivirsofosbuvirspike glycoproteinα-difluoromethylornithine (DMFO)

Identifiers

PMID32967116
PMCPMC7565175

What OpenQuestion holds

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LicenceCC BY
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.