Wu Jixu, Zhang Xiuhua, Jin Yuting, Zhang Man, Yu Rongmin, Song Liyan, Liu Fei, Zhu Jianhua
Biotechnological Institute of Chinese Materia Medica, Jinan University, Guangzhou 510632, China.
Shandong Engineering Research Center for Efficient Preparation and Application of Sugar and Sugar Complex, Shandong Academy of Pharmaceutical Science, Jinan 250101, China.
Molecules. 2025 Apr 7;30(7):1640. doi: 10.3390/molecules30071640.
This study employed an integrated in silico approach to identify and characterize anticancer peptides (ACPs) derived from species. Using a comprehensive bioinformatics pipeline (BIOPEP, ToxinPred, ProtParam, ChemDraw, SwissTargetPrediction, and I-TASSER), we screened hydrolyzed bioactive peptides from 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 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.
本研究采用综合的计算机模拟方法来鉴定和表征源自物种的抗癌肽（ACP）。通过全面的生物信息学流程（BIOPEP、ToxinPred、ProtParam、ChemDraw、SwissTargetPrediction和I-TASSER），我们从物种中筛选水解生物活性肽，鉴定出17种新型肽候选物。随后的体外验证揭示了三种具有显著抗癌活性的肽（KW、WQIWYK、KGKWQIWYKSL），证明了其高生物安全性和临床潜力。我们的研究结果突出了物种蛋白质作为治疗性ACP的宝贵来源，并确立了生物信息学作为快速发现生物活性肽的有效策略。这种方法将计算预测与实验验证相结合，为开发新型基于肽的疗法提供了一个强大的框架。
