BACKGROUND AND OBJECTIVESCancer is one of the life-threatening diseases in which biological factors, such as bacteria are involved in its development. In recent years, antimicrobial peptides have received much attention for treatment of cancer or microbial disease. Therefore, identification of engineered peptides that can effectively suppress cancer markers, such as epidermal growth factor receptor (EGFR), is a suitable model for introducing anticancer drugs. This study aims to use the amino acid sequence related to a bacteriocin of Streptococcus gallolyticus, Gallocin, to design newly engineered peptide(s) with an anticancer activity using in silico or bioinformatics methods.MATERIALS AND METHODSThe anti-cancer sequences with a length of ۱۰ amino acids were predicted from the amino acid sequence of Gallocin with the help of support vector machine (SVM) algorithm web-based tools. Then, four sequences with a high SVM score and the best physiochemical properties, including positive net charge and amphipathicity, were selected for further analysis. The docking studies of receptor-ligand interactions were carried out by Molegro Virtual Docker software and also ClusPro and HDOCK servers. Finally, the pharmacological properties of predicted peptides, including absorption, distribution, metabolism, excretion, and toxicity (ADMET), were evaluated using ADMETlab web tool.RESULTS AND DISCUSSIONBased on Docking results, all peptides showed approximately similar affinity to the
EGFR receptor. However,
Peptide ۴ had the lowest energy (-۸۱۶.۹) and the highest binding affinity to the
EGFR receptor. In addition,
Peptide ۱,
Peptide ۳, and
Peptide ۴ formed many reasonable Hydrogen bond interactions and consequently a strong attachment to the
EGFR receptor. Also, according to ADMET features, these peptides showed reasonable toxicity and pharmacological properties.CONCLUSIONOverall, some peptides were designed by time and cost-effective in silico methods which can be candidates for further experimental anti-cancer research.