Evidence map›Paper›PMID 32658735›Full record

ArticleComputers in biology and medicine2020

Identification of potential inhibitors of three key enzymes of SARS-CoV2 using computational approach.

Hafsa Iftikhar, Hafiza Nayyer Ali, Sadia Farooq, Hammad Naveed, Syed Shahzad-Ul-Hussan

Open access · bronzeAbstract read
In one paragraph

Article in Computers in biology and medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
7.1field-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

33 citing papers in PubMed, 54 citations in OpenAlex.

  1. Article
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  6. Arabian journal of chemistry · 2023
    Article
  7. Article
  8. Review
  9. Review
  10. Article
  11. Article
  12. Review
  13. Novel Drug Design for Treatment of COVID-19: A Systematic Review of Preclinical Studies.The Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale · 2022
    Review
  14. Article
  15. Review
  16. Screening of Severe Acute Respiratory Syndrome Coronavirus 2 RNA-Dependent RNA Polymerase Inhibitors Using Computational Approach.Journal of computational biology : a journal of computational molecular cell biology · 2021
    Article
  17. Article
  18. Article
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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

5 authors at 2 institutions in 1 country.

Hafsa IftikharDepartment of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan.
Hafiza Nayyer AliDepartment of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan.
Sadia FarooqDepartment of Computer Science, National University of Computer & Emerging Sciences, Islamabad, Pakistan.
Hammad NaveedDepartment of Computer Science, National University of Computer & Emerging Sciences, Islamabad, Pakistan. Electronic address: hammad.naveed@nu.edu.pk.
Syed Shahzad-Ul-HussanDepartment of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan. Electronic address: shahzad.hussan@lums.edu.pk.
Lahore University of Management Sciences · PKNational University of Computer and Emerging Sciences · PK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The recent outbreak of coronavirus disease-19 (COVID-19) continues to drastically affect healthcare throughout the world. To date, no approved treatment regimen or vaccine is available to effectively attenuate or prevent the infection. Therefore, collective and multidisciplinary efforts are needed to identify new therapeutics or to explore effectiveness of existing drugs and drug-like small molecules against SARS-CoV-2 for lead identification and repurposing prospects. This study addresses the identification of small molecules that specifically bind to any of the three essential proteins (RdRp, 3CL-protease and helicase) of SARS-CoV-2. By applying computational approaches we screened a library of 4574 compounds also containing FDA-approved drugs against these viral proteins. Shortlisted hits from initial screening were subjected to iterative docking with the respective proteins. Ranking score on the basis of binding energy, clustering score, shape complementarity and functional significance of the binding pocket was applied to identify the binding compounds. Finally, to minimize chances of false positives, we performed docking of the identified molecules with 100 irrelevant proteins of diverse classes thereby ruling out the non-specific binding. Three FDA-approved drugs showed binding to 3CL-protease either at the catalytic pocket or at an allosteric site related to functionally important dimer formation. A drug-like molecule showed binding to RdRp in its catalytic pocket blocking the key catalytic residues. Two other drug-like molecules showed specific interactions with helicase at a key domain involved in catalysis. This study provides lead drugs or drug-like molecules for further in vitro and clinical investigation for drug repurposing and new drug development prospects.

Indexed as

Drug RepositioningAmidesBetacoronavirusCarbamatesCatalytic DomainComputer SimulationCoronavirus InfectionsCOVID-19COVID-19 Drug TreatmentCyclopropanesDimerizationDrug DesignHumansLactams, MacrocyclicMolecular Docking SimulationPandemicsAmidesCarbamatesCyclopropanesgrazoprevirLactams, MacrocyclicProtease InhibitorsQuinoxalinesRimantadineSulfonamidesViral ProteinsDrug repurposingGrazoprevirHelicaseRdRpRimantadineSARS-CoV-2

Identifiers

PMID32658735
PMCPMC7282781
OpenAlexW3033671083

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.