Simulation screening of umami peptides in the microbiota of soy sauce based on molecular docking and molecular dynamics

Abstract

Simulation screening and molecular simulation techniques are gradually being applied to the study of bioactive peptides. In this study, we explored and simulated the screening process using metagenomic sequencing data of microorganisms from soy sauce. An LSTM-based screening model was constructed to identify the top 10 scoring peptides from a sample dataset of 19,335 raw peptides, primarily derived from Bacillus and Weissella confusa. Through homology modeling, molecular docking, and molecular dynamics simulations, Ser, Asp, Asn, Glu, and Gln were identified as the main amino acids involved in ligand-receptor binding. Hydrogen bonding, van der Waals forces, and hydrophobic interactions were revealed as the primary forces mediating receptor and ligand binding. The umami thresholds of ISWCFTY and QISPYRRI were verified through wet experiments to be 0.13 mg/mL and 0.11 mg/mL, respectively, exhibiting high umami intensity. Overall, this study presents a high-throughput screening method for predicting umami peptides, offering cost reduction, simplified steps, and saved manpower.