Evidence map›Paper›PMID 34121824›Full record

ArticleJournal of proteins and proteomics2021

Screening of FDA-approved compound library identifies potential small-molecule inhibitors of SARS-CoV-2 non-structural proteins NSP1, NSP4, NSP6 and NSP13: molecular modeling and molecular dynamics studies.

Shobana Sundar, Lokesh Thangamani, Shanmughavel Piramanayagam, Chandrasekar Narayanan Rahul, Natarajan Aiswarya, Kanagaraj Sekar, Jeyakumar Natarajan

Open access · bronzeAbstract read
In one paragraph

Article in Journal of proteins and proteomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.4field-weighted citation impact, top 10% 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

10 citing papers in PubMed, 17 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. SARS-CoV-2-Specific Immune Response and the Pathogenesis of COVID-19.International journal of molecular sciences · 2022
    Review
  8. Structural biology of SARS-CoV-2: open the door for novel therapies.Signal transduction and targeted therapy · 2022
    Review
  9. Article
  10. Article
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

7 authors at 2 institutions in 1 country.

Shobana SundarComputational Biology Lab, Department of Bioinformatics, Bharathiar University, Coimbatore, India.
Lokesh ThangamaniComputational Biology Lab, Department of Bioinformatics, Bharathiar University, Coimbatore, India.
Shanmughavel PiramanayagamComputational Biology Lab, Department of Bioinformatics, Bharathiar University, Coimbatore, India.
Chandrasekar Narayanan RahulDepartment of Computational and Data Sciences, Indian Institute of Science, Bangalore, Karnataka India.
Natarajan AiswaryaDepartment of Computational and Data Sciences, Indian Institute of Science, Bangalore, Karnataka India.
Kanagaraj SekarDepartment of Computational and Data Sciences, Indian Institute of Science, Bangalore, Karnataka India.
Jeyakumar NatarajanData Mining and Text Mining Laboratory, Department of Bioinformatics, Bharathiar University, Coimbatore, Tamil Nadu India.
Bharathiar University · INIndian Institute of Science Bangalore · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19, the current global pandemic has caused immense damage to human lives and the global economy. It is instigated by the SARS-CoV-2 virus and there is an immediate need for the identification of effective drugs against this deadly virus. SARS-CoV-2 genome codes for four structural proteins, sixteen non-structural proteins (NSPs) and several accessory proteins for its survival inside the host cells. In the present study, through in silico approaches, we aim to identify compounds that are effective against the four NSPs namely, NSP1, NSP4, NSP6 and NSP13 of SARS-CoV-2. The selection criteria of these four NSP proteins are they are least explored and potential targets. First, we have modeled the 3D structures of these proteins using homology modeling methods. Further, through molecular docking studies, we have screened the FDA-approved compounds against these modeled proteins and reported their docking scores. To gain dynamic insights, molecular dynamics studies have also been carried out for the best scored ligand against the NSPs. This study can further pave way for exposing more number of compounds against these proteins and enhance COVID-19 treatment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42485-021-00067-w.

Indexed as

COVID-19FDA-approved compoundsMolecular dockingMolecular dynamicsNSPs

Identifiers

PMID34121824
PMCPMC8188161
OpenAlexW3166104600

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.