Identification of Novel Umami Peptides from Yak Bone Collagen and Mechanism Exploration Through In Silico Discovery, Molecular Docking, and Electronic Tongue
Umami peptides were screened and identified from yak bone collagen for the first time by <i>in silico</i> analysis, molecular docking, and electronic tongue. Twenty proteases with known cleavage sites were used for the simulated proteolysis, and results indicated that "pepsin + papain" was the optimal enzymatic strategy for yak bone collagen to generate peptides with potential umami taste. Moreover, 82 novel unreported peptides with umami taste were found from the simulated hydrolysate, among which 9 peptides exhibited high binding affinity with the T1R1/T1R3 receptor (both -CDOCKER energy and CDOCKER interaction energy > 40 kcal/mol) via molecular docking. Subsequently, six novel umami peptides were identified through sensory evaluation and electronic tongue analysis, including VY, VM, SL, SN, VN, and IS (umami sensory score > 5). These peptides were also <i>in silico</i> characterized with high hydrophobicity, good water solubility, non-toxicity, non-allergenicity, good intestinal absorption, and good oral bioavailability. Furthermore, the identified peptides could bind with the key residues (such as HIS281 and LEU304) within the Venus flytrap domain of the T1R3 subunit of receptor T1R1/T1R3 through hydrogen bonds and electrostatic attractions to generate umami perception. This study revealed the mechanism of umami peptides identified from yak bone collagen and provides a novel approach for the development of umami peptides from animal sources.
Authors
- Hongying Du (ORCID: https://orcid.org/0000-0001-6229-2713)
- Yimeng Mei
- Runfang Guo
- Yulong Wu (ORCID: https://orcid.org/0000-0001-5406-9175)
- Jin Zhang (ORCID: https://orcid.org/0000-0002-6359-4147)
- Ruoyu Xie
- Jun Hu (ORCID: https://orcid.org/0000-0001-9250-8853)
- Wenxuan Chen (ORCID: https://orcid.org/0000-0002-2961-2313)
- Xiaoli Wu (ORCID: https://orcid.org/0009-0003-9821-898X)
- Ke Zhao
Institutions
- Hebei Agricultural University (CN)
- Nanjing Forestry University (CN)
- ZheJiang Academy of Agricultural Sciences (CN)
Publication Details
- Journal
- Foods
- Published
- 2025-11-26
- DOI
- https://doi.org/10.3390/foods14234057
- Citations
- 1
- Primary Topic
- Biochemical Analysis and Sensing Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.12