Evidence map›Paper›PMID 36478400›Full record

ArticleJournal of computational chemistry2023

Large scale peptide screening against main protease of SARS CoV-2.

Md Jaish Uddin, Hasina Akhter, Urmi Chowdhury, Jannatul Mawah, Sanzida Tul Karim, Mohammad Jomel, Md Sirajul Islam, Mohammad Raqibul Islam, Latifa Afrin Bhuiyan Onin, Md Ackas Ali and 2 more

Open access · greenAbstract read
In one paragraph

Article in Journal of computational chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.2field-weighted citation impact, top 23% 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

4 citing papers in PubMed, 10 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

12 authors at 2 institutions in 2 countries.

Md Jaish UddinDivision of Infectious Disease and Division of Computer-Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
Hasina AkhterDivision of Infectious Disease and Division of Computer-Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
Urmi ChowdhuryDivision of Infectious Disease and Division of Computer-Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
Jannatul MawahDivision of Infectious Disease and Division of Computer-Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
Sanzida Tul KarimDivision of Infectious Disease and Division of Computer-Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
Mohammad JomelDivision of Infectious Disease and Division of Computer-Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
Md Sirajul IslamDivision of Infectious Disease and Division of Computer-Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
Mohammad Raqibul IslamDivision of Infectious Disease and Division of Computer-Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
Latifa Afrin Bhuiyan OninDivision of Infectious Disease and Division of Computer-Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
Md Ackas AliDivision of Infectious Disease and Division of Computer-Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
Faiyaz Md EfazDivision of Infectious Disease and Division of Computer-Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
Mohammad A HalimDepartment of Chemistry and Biochemistry, Kennesaw State University, Kennesaw, Georgia, USA.ORCID 0000-0002-1698-7044
Green University of Bangladesh · BDKennesaw State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic has been a public health emergency, with deadly forms constantly emerging around the world, highlighting the dire need for highly effective antiviral therapeutics. Peptide therapeutics show significant potential for this viral disease due to their efficiency, safety, and specificity. Here, two thousand seven hundred eight antibacterial peptides were screened computationally targeting the Main protease (Mpro) of SARS CoV-2. Six top-ranked peptides according to their binding scores, binding pose were investigated by molecular dynamics to explore the interaction and binding behavior of peptide-Mpro complexes. The structural and energetic characteristics of Mpro-DRAMP01760 and Mpro-DRAMP01808 complexes fluctuated less during a 250 ns MD simulation. In addition, three peptides (DRAMP01760, DRAMP01808, and DRAMP01342) bind strongly to Mpro protein, according to the free energy landscape and principal component analysis. Peptide helicity and secondary structure analysis are in agreement with our findings. Interaction analysis of protein-peptide complexes demonstrated that Mpro's residue CYS145, HIS41, PRO168, GLU166, GLN189, ASN142, MET49, and THR26 play significant contributions in peptide-protein attachment. Binding free energy analysis (MM-PBSA) demonstrated the energy profile of interacting residues of Mpro in peptide-Mpro complexes. To summarize, the peptides DRAMP01808 and DRAMP01760 may be highly Mpro specific, resulting disruption in a viral replication and transcription. The results of this research are expected to assist future research toward the development of antiviral peptide-based therapeutics for Covid-19 treatment.

Indexed as

COVID-19SARS-CoV-2Antiviral AgentsCOVID-19 Drug TreatmentHumansMolecular Docking SimulationMolecular Dynamics SimulationPandemicsPeptide HydrolasesPeptidesAntiviral AgentsPeptide HydrolasesPeptidesantibacterial peptidefree energy landscapehelicitymain proteaseMM-PBSASARS CoV-2

Identifiers

PMID36478400
PMCPMC9877796
OpenAlexW4310948337

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.