In silico modelling and docking insight of bacterial peptide as anti-tubercular and anticancer agents

This study was investigated to predict the anti-tubercular and anticancer traits of bacterial peptide SMANF2 using computational tools. Initially, the structure of peptide SMANF2 was modelled and its stereo-chemical, physiochemical, and functional parameters were determined using various computational approaches. Further, in silico molecular docking between peptide and targeted proteins of Mycobacterium tuberculosis and cancer cells (lung cancer – A540 cell line; colon cancer – HT-29 cell line) were predicted using Hex 8.0.0 docking software. Results predicted good stereo-chemical, physiochemical, and functional features of peptide SMANF2. The peptide exhibited the highest negative energy value (E-value) of -747.99 KJ/mol with LysA, followed by -735.43, -631.07, -549.28, and -285.06 KJ/mol with ribonucleotide reductase, alanine racemase, DNA gyrase, and isocitrate lyase, respectively of M. tuberculosis. Among targeted proteins of A540 cell line, peptide SMANF2 revealed the highest docking score of -539.12 KJ/mol with Bcl-2. On the other hand, the peptide showed highest negative E-value of -422.70 KJ/mol with Bcl-xL among targeted HT-29 cell line. In a nutshell, this in silico study predicted the anti-tubercular and anticancer traits of peptide SMANF2 that can be used as auspicious therapeutic agent in future.