Screening and characterization of umami peptides from enzymatic and fermented products of wheat gluten using machine learning

Abstract

To explore the umami peptides from wheat gluten hydrolysates (WGHs) with different degrees of hydrolysis (DH) after Corynebacterium glutamicum (C. glutamicum) fermentation, a high-throughput screening method for umami peptides was established based on machine learning. The results showed fermented WGHs with high DHs exhibited more favourable taste profile after 4 days of fermentation and produced more small molecule peptides. Fermentation increased the number of characteristic peptides in WGHs with DH10, DH15, and DH20. Among, fermented WGHs with DH15 were the important source of potential umami peptides. Six potential umami peptides were screened (QQLPQFEE, LSFE, EELR, YTCE, YTTD, EEDQ) and were demonstrated to form stable complex with the T1R1/T1R3 receptor through hydrogen bonding interactions, electrostatic interactions, and hydrophobic interactions forces. The affinity of peptides and receptor were in the range of −6.3 to −8.1 kcal/mol. GLU735, GLY755, SER734, SER757 and THR227 were the critical binding sites. Sensory evaluation and electronic tongue showed that six peptides exhibited umami profile and had the capacity to enhance umami and saltiness taste. EEDQ presented the strongest intensity of umami taste and QQLPQFEE had the lowest umami thresholds. This study provides reliable methods for effectively screening new microbial-derived umami peptides.