From prediction to design: Revealing the mechanisms of umami peptides using interpretable deep learning, quantum...

This study screened and designed umami peptides using deep learning model and module substitution strategies. The predictive model, which integrates pre-training, enhanced feature, and contrastive learning module, achieved an accuracy of 0.94, outperforming other models by 2-9 %. Umami peptides were identified through virtual hydrolysis, model predictions, and sensory evaluation. Peptides EN, ETR, GK4, RK5, ER6, EF7, IL8, VR9, DL10, and PK14 demonstrated umami taste and exhibited umami-enhancing effects with MSG. Module substitution strategy, where highly contributive module from umami peptides replace corresponding module in bitter peptides, facilitates peptide design and modification. The mechanism underlying module substitution and taste presentation were elucidated via molecular docking and active site analysis, revealing that substituted peptides form more hydrogen bonds and hydrophobic interactions with T1R1/T1R3. Amino acids D, E, Q, K, and R were critical for umami taste. This study provides an efficient tool for rapid umami peptide screening and expands the repository.