Development of anticancer peptides-based Echinococcus granulosus by in silico approaches

Cancer is the second leading cause of death globally, with traditional treatment approaches often facing challenges such as severe side effects and tumor resistance. Thus, developing novel anticancer therapies is crucial. This study explores the potential of anticancer peptides (ACPs) derived from the Echinococcus (E). granulosus proteins’ P29 and EG95 using bioinformatics methods. The amino acid sequences of P29 and EG95 were retrieved from the NCBI database. Peptides ranging from 5 to 25 amino acids in length were computationally generated from these sequences. These peptides were then subjected to a comprehensive protein scan using the AntiCP 2.0 server. Subsequently, potential ACPs from EG95 and P29 templates underwent systematic mutations and were re-evaluated using AntiCP 2.0 to determine whether their anticancer properties had improved. Toxicity, allergenicity, and antigenicity of the ACPs were assessed using TOXINPRED2, ALGPRED2, and VAXIJEN2 tools, respectively. Primary datasets of known ACPs and non-ACPs were compiled, and motif analysis was conducted using the MERCI software to identify specific patterns associated with anticancer activity. This revealed diverse motif lengths, particularly those ranging from 12 to 13 amino acids. Among P29 peptides, 902 were identified as allergenic and 55 as toxic, while among the EG95 peptides, 264 were allergenic and 15 were toxic. Peptides demonstrating desirable properties were further evaluated using the TOPSIS decision model, which ranked them based on multiple attributes. The top 10 peptides with the highest scores were identified as promising ACP candidates. Our study highlights the potential of using bioinformatic methods to design ACPs from E. granulosus proteins, namely P29 and EG95, paving the way for the development of targeted and effective anticancer therapies. This study uniquely leverages E. granulosus proteins, capitalizing on their evolutionary refinement at the host-pathogen interface, to design ACPs with enhanced biocompatibility and multi-modal targeting. Our approach integrates motif-driven discovery with systematic mutation and TOPSIS-based multi-criteria optimization, offering a novel pipeline distinct from conventional AMP-to-ACP conversion strategies.