Cribado virtual de péptidos dulces de proteínas de la leche y análisis del mecanismo de simulación de dinámica...

Bioactive peptides derived from milk proteins have attracted increasing interest due to their potential as natural sweet-tasting compounds. In this study, an integrated in silico strategy was developed to identify sweet peptides from milk proteins. The approach combined machine learning models capable of predicting both sweet and bitter taste properties to improve the accuracy of peptide selection. Peptides generated from virtual enzymatic hydrolysis were screened using 3 machine learning models. Candidates predicted to be sweet and nonbitter were virtually screened to evaluate their binding affinity to the human sweet taste receptor T1R2/T1R3. Peptides with favorable docking scores were further evaluated for their pharmacokinetic properties using computational prediction tools. Based on the combined results of docking and an absorption, distribution, metabolism, excretion, and toxicity assessment, 5 peptides (MDG, MKG, TSG, CDSS, and DSTT) were selected for further analysis. Molecular docking interaction analysis revealed that hydrogen bonding and π-π stacking were the predominant interaction modes at the binding site. Molecular dynamics simulations confirmed the structural stability of the 5 complexes, with MDG, CDSS, and DSTT showing reduced binding fluctuation and minimal receptor conformational changes, suggesting stronger binding stability. Electronic tongue analysis validated the presence of detectable sweet taste signals for all 5 peptides. Among them, MDG, CDSS, and DSTT demonstrated particularly stable interactions and clear sweetness responses, highlighting their potential as candidates for natural sweetener development. This study presents a practical computational framework for the efficient screening and evaluation of sweet peptides from milk protein sources. The proposed strategy may support the discovery of dairy-derived sweeteners with potential applications in sugar-reduced or functional dairy products.