Goat milk is traditionally known for its nutritional value, greater digestibility, and cultural significance. More recently, goat milk has been explored as a promising natural source of novel bioactive peptides (BAPs) with significant biological activity. BAPs derived from goat milk have emerged as high potential candidates for their therapeutic and health-promoting attributes owing to their diverse functionality as natural antioxidants, antihypertensives, antimicrobials, antidiabetics, anti-hypercholesterolemic, anticancer, as well as immunomodulatory therapeutic aids. The integration of powerful computational biology approaches, such as machine learning and artificial intelligence, with the research in the domains of bioinformatics and food sciences has greatly accelerated and improved the discovery pipelines for milk protein-derived BAPs. This, in turn, has enabled rapid and cost-effective in silico identification, high-throughput screening, and functional prediction of these BAPs prior to being subjected to experimental validation and testing through conventional in vitro/in vivo methods. The utilization of various in silico techniques, such as bioactivity relationship modeling tools, molecular docking, and design of experiment (DoE) approaches for optimizing production, identification, and performance in biological systems, as well as future potential are investigated in the current review.
Hassan et al. (Tue,) studied this question.