In Silico Design of a novel peptide inhibitor derivative of Pediocin CP2 affecting breast cancer

Introduction & Aim: In the recent years, Use of therapeutic peptides in cancer therapy has been receiving significant consideration. Anticancer peptides are small (5–40 amino acids) peptides, often origin from antimicrobial peptides. In silico studies are a hopeful method to design new peptide drug having the anticancer potential. Thus, aim of the present study is develop the approach for the identification of possible peptide drug molecule for breast cancer targeting through bioinformatics analysis. Methods: Pediocin CP2 sequence submit to AntiCP server for predict and design of newly anticancer peptides. The 2D and 3D structures of these peptides were drawn with Protean (DNAstar software), Phyre2 and I-TASSER respectively.The 3D structures of peptides were docked with the HIF-1α using docking software Hex 6.1 and the peptide with the maximum binding energy value was identified. As well as, anticancer activity for each peptide estimated with iACP software. Results: AntiCP server predicts > 250 peptide with anticancer property. Anticancer activity of new design peptides showed 98-99% anticancer specificity. Result of this study showed that among 250 peptides were virtually screened 15 peptides showed high anticancer property and among these peptides KHSCSVDWGKATTC identified as potential sequence for HIF-1α ligand. Conclusion: Our data indicated that the probability of successful production of new anticancer peptide derived of antimicrobial peptide. These findings based on bioinformatics analysis may be used in vaccine design and cancer therapy. Therefore, our designed peptide can be also used as potential anticancer drugs in a low dose for a better effect against breast cancer.

کلیدواژه ها:

Anticancer peptide ، Pediocin CP2 ، breast cancer ، in silico