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Trends in Bioinformatics
  Year: 2020 | Volume: 13 | Issue: 1 | Page No.: 1-9
DOI: 10.3923/tb.2020.1.9
 
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In silico Modelling and Molecular Docking Insight of Bacterial Peptide for Anti-tubercular and Anticancer Drug Designing
Ameer Khusro , Chirom Aarti and Paul Agastian

Abstract:
Background and Objective: At present, millions of mortalities caused by tuberculosis and cancer are of colossal concern worldwide. The severe adverse effects of existing drugs have emphasized the researchers to identify ideal therapeutic agents. In view of this, the present study was aimed to assess the anti-tubercular and anticancer properties of bacterial peptide using in silico docking tool. Materials and Methods: The 3D structure of peptide NMANF2 was modelled using online software PEP-FOLD and iCn3D. The 3D structures of M. tuberculosis, lung cancer (A540) and colon cancer (HT-29) cells’ target receptors were retrieved from RCSB PDB. In silico molecular docking between ligands and targeted proteins of M. tuberculosis and cancer cell lines (A540 and HT-29) were analyzed and visualized using Hex 8.0.0 docking software. Results: The peptide exhibited highest negative energy value (E-value) with DNA gyrase, followed by ribonucleotide reductase, LysA, alanine racemase and isocitrate lyase of M. tuberculosis. The peptide revealed highest docking score with Bcl-2 of A540 cell line. On the other hand, the peptide showed highest negative E-value with CDK4 among targeted proteins of HT-29 cell line. Conclusion: Based on this in silico results, peptide NMANF2 may be used as potent agent for designing anti-tubercular and anticancer drugs in future.
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How to cite this article:

Ameer Khusro, Chirom Aarti and Paul Agastian, 2020. In silico Modelling and Molecular Docking Insight of Bacterial Peptide for Anti-tubercular and Anticancer Drug Designing. Trends in Bioinformatics, 13: 1-9.

DOI: 10.3923/tb.2020.1.9

URL: https://scialert.net/abstract/?doi=tb.2020.1.9

 
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