Novel Computational Approaches in the Discovery and Identification of Bioactive Peptides: A Bioinformatics Perspective
Mengjiao Li, Mengting Chen, Yizhen Sun, Liu Shi, Xiaojia Guo, Sheng Chen, Lang Chen, Guangquan Xiong, Weiqing Sun, Ruichang Gao, Ke Liang, Lan Wang, Wenjin Wu
Abstract
Bioactive peptides are protein molecules known for their specific biological functions, offering promising applications across various fields including medicine, food, and cosmetics. Traditional approaches to the investigation of bioactive peptides typically encompass extraction, separation, purification, identification, and experimental evaluation. However, these methodologies are frequently subject to human-related variables, which consequently lead to reduced efficiency and compromised accuracy. Bioinformatics techniques, including computer simulation screening, quantitative structure-activity relationship (QSAR) analysis, and machine learning, have emerged as powerful tools in the field of bioactive peptide research. These advanced methodologies not only enhance the efficiency of bioactive peptide screening but also provide valuable insights into the underlying mechanisms of action of these peptides. This review discusses the identification, analysis, and evaluation of bioactive peptides through innovative bioinformatics technology while also highlighting traditional techniques that have been developed and improved. This review provides robust theoretical support and valuable references for future research and applications involving bioactive peptides.