Investigation of Anticancer Peptides Derived from Arca Species Using In Silico Analysis - Jixu Wu; Xiuhua Zhang; Yuting Jin; Man Zhang; Rongmin Yu; Liyan Song; Fei Liu; Jianhua Zhu (Molecules)
This study employed an integrated in silico approach to identify and characterize anticancer peptides (ACPs) derived from Arca species. Using a comprehensive bioinformatics pipeline (BIOPEP, ToxinPred, ProtParam, ChemDraw, SwissTargetPrediction, and I-TASSER), we screened hydrolyzed bioactive peptides from Arca species, identifying seventeen novel peptide candidates. Subsequent in vitro validation revealed three peptides (KW, WQIWYK, KGKWQIWYKSL) with significant anticancer activity, demonstrating both high biosafety and clinical potential. Our findings highlight Arca species proteins as a valuable source of therapeutic ACPs and establish bioinformatics as an efficient strategy for rapid discovery of bioactive peptides. This approach combines computational prediction with experimental validation, offering a robust framework for developing novel peptide-based therapeutics.