Evidence map›Paper›PMID 37033295›Full record

ArticlePhytomedicine plus : international journal of phytotherapy and phytopharmacology2023

Identification of natural peptides from "PlantPepDB" database as anti-SARS-CoV-2 agents: A protein-protein docking approach.

Priyanka Bhandu, Himanshu Verma, Baddipadige Raju, Gera Narendra, Shalki Choudhary, Manmeet Singh, Pankaj Kumar Singh, Om Silakari

Open access · goldAbstract read
In one paragraph

Article in Phytomedicine plus : international journal of phytotherapy and phytopharmacology, 2023. 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
0.6field-weighted citation impact, top 31% 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

3 citing papers in PubMed, 4 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

8 authors at 2 institutions in 2 countries.

Priyanka BhanduMolecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, 147002, India.
Himanshu VermaMolecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, 147002, India.
Baddipadige RajuMolecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, 147002, India.
Gera NarendraMolecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, 147002, India.
Shalki ChoudharyMolecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, 147002, India.
Manmeet SinghMolecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, 147002, India.
Pankaj Kumar SinghFaculty of Medicine, Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, Turku, FI-20014, Finland.
Om SilakariMolecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, 147002, India.
Punjabi University · INUniversity of Turku · FI

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: A global pandemic owing to COVID-19 infection has created havoc in the entire world. The etiological agent responsible for this viral outbreak is classified as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Still, there's no specific drug or preventive medication to treat SARS-CoV-2. This study was designed to demonstrate the efficacy of some anti-viral peptides obtained from a plant database i.e., PlantPepDB as potential ACE-2-Spike (S) protein complex neutralizers using a structure-based drug designing approach. Method: A total of 83 anti-viral plant peptides were screened from a peptide database i.e. PlantPepDB based on their reported anti-viral activities against various viral strains. In order to screen peptides that may potentially interfere with ACE-2 and S complex formation, molecular docking studies were conducted using the flare module of Cresset software and subsequently, analysed the crucial interactions between the peptides and S complexes and ACE-2/S complex. Herein, the interactions and docking scores obtained for ACE-2/S complex were considered as references. The S-peptides complexes which displayed superior interactions and docking scores than reference complex i.e., ACE2-S were considered as final hits. The Molecular dynamics studies were conducted for a period of 30 ns for each of the final hit/S complex to understand the interaction stability and binding mechanism of designed peptides. Results: The molecular docking results revealed that five peptides including Cycloviolacin Y3, Cycloviolacin Y1, White cloud bean defensin, Putative defensin 3.1, and Defensin D1 showed superior docking scores (i.e. -1372.5 kJ/mol to -1232.6 kJ/mol) when docked at the ACE2 binding site of S-protein than score obtained for the complex of ACE-2 and S protein i.e. -1183.4 kJ/mol. Moreover, these top five peptides manifested key interactions required to prevent the binding of S protein with ACE2. The molecular dynamics simulation study revealed that two of these five peptides i.e. Cycloviolacin Y3 and Cycloviolacin Y1 displayed minimal RMSD fluctuations. Conclusions: The current structure-based drug-designing approach shows the possible role of anti-viral plant peptides as potential molecules to be explored at the initial stage of viral pathogenesis.

Indexed as

Plant peptides, Anti-viral peptidesProtein-protein dockingSARS-CoV-2

Identifiers

PMID37033295
PMCPMC10065049
OpenAlexW4362469184

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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.