Uncovering the taste features: Applying machine learning and molecular docking approaches to predict umami taste intensity of peptides

Abstract

The umami taste, often described as the fifth basic taste, plays a pivotal role in the culinary world, significantly contributing to the overall flavor profile and consumer satisfaction of food products. Precise prediction and enhancement of umami taste intensity using the identification of umami peptides can lead to groundbreaking advancements in the food industry. This study presents a novel approach to combine machine learning and molecular docking techniques. The machine learning algorithm is based on a cascade algorithm combining CatBoost and BERT models to predict the umami taste intensity of peptides accurately. Our research reveals that combinations of specific amino acids, such as aspartic acid with alanine or glycine and lysine with glycine or histidine, have been identified to enhance the umami taste in foods. The model is available as a user-friendly web server at https://taste.infochemistry.ru. This study contributes to the scientific understanding of taste perception and provides a valuable tool for the food industry to innovate and improve product quality by optimizing umami taste profiles.