In silico and molecular docking approaches in food-derived bioactive peptide discovery: Trends, challenges, and prospects

Abstract

The 2024 Nobel Prize awarded for protein structure prediction has strengthened the reliability of in silico approaches for protein and peptide research. In recent years, food-derived bioactive peptides (BAPs), which are small amino acid chains produced from food proteins, have garnered increasing interest owing to their promising health benefits. However, traditional approaches to BAPs production are often time-consuming, expensive, and unpredictable. The emergence of in silico methods has transformed BAPs research by enabling high-throughput screening and strategic utilization in food and pharmaceuticals. Molecular docking approaches have considerably expedited BAPs research by predicting the binding affinities and molecular interactions with target proteins. This review explores recent developments in computational approaches to BAPs discovery, highlighting their viability and sustainability. It provides a broad overview of advances in in silico BAPs production and molecular docking methods, delves into the appraisal of bioactivity, toxicity, and allergenicity, and discusses ligand and receptor preparation and molecular simulations. Future research should focus on improving docking algorithms, integrating multiscale modeling techniques, and incorporating high-throughput experimental screening for better validation. By addressing these challenges, the in silico techniques can play an important role in the efficient identification of novel food-derived BAPs with therapeutic potential.