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Cui Z, Meng H, Zhou T, Yu Y, Gu J, Zhang Z, Zhu Y, Zhang Y, Liu Y, Wang W. Noteworthy Consensus Effects of D/E Residues in Umami Peptides Used for Designing the Novel Umami Peptides. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:2789-2800. [PMID: 38278623 DOI: 10.1021/acs.jafc.3c07026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2024]
Abstract
Aspartic acid (D) and glutamic acid (E) play vital roles in the umami peptides. To understand their exact mechanism of action, umami peptides were collected and cut into 1/2/3/4 fragments. Connecting D/E to the N/C-termini of the fragments formed D/E consensus effect groups (DEEGs), and all fragments containing DEEG were summarized according to the ratio and ranking obtained in the above four situations. The interaction patterns between peptides in DEEG and T1R1/T1R3-VFD were compared by statistical analysis and molecular docking, and the most conservative contacts were found to be HdB_277_ARG and HdB_148_SER. The molecular docking score of the effector peptides significantly dropped compared to that of their original peptides (-1.076 ± 0.658 kcal/mol, p value < 0.05). Six types of consensus fingerprints were set according to the Top7 contacts. The exponential of relative umami was linearly correlated with ΔGbind (R2 = 0.961). Under the D/E consensus effect, the electrostatic effect of the umami peptide was improved, and the energy gap between the highest occupied molecular orbital-the least unoccupied molecular orbital (HOMO-LUMO) was decreased. The shortest path map showed that the peptides had similar T1R1-T1R3 recognition pathways. This study helps to reveal umami perception rules and provides support for the efficient screening of umami peptides based on the material richness in D/E sequences.
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Affiliation(s)
- Zhiyong Cui
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, P. R. China
| | - Hengli Meng
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, P. R. China
| | - Tianxing Zhou
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, P. R. China
- Department of Bioinformatics, Faculty of Science, The University of Melbourne, Victoria 3010, Australia
| | - Yanyang Yu
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, P. R. China
| | - Jiaming Gu
- College of Humanities and Development Studies, China Agricultural University, Beijing 100083, P. R. China
| | - Zhiwei Zhang
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, P. R. China
| | - Yiwen Zhu
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, P. R. China
| | - Yin Zhang
- Key Laboratory of Meat Processing of Sichuan, Chengdu University, Chengdu 610106, P. R. China
| | - Yuan Liu
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, P. R. China
| | - Wenli Wang
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, P. R. China
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