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探索小肽与 T1R1/T1R3 鲜味味觉受体之间的关系,用于鲜味肽预测:一种联合方法。

Exploring the Relationship between Small Peptides and the T1R1/T1R3 Umami Taste Receptor for Umami Peptide Prediction: A Combined Approach.

机构信息

College of Food Science and Engineering, Shandong Agricultural University, Key Laboratory of Food Nutrition and Human Health in Universities of Shandong, Taian 271018, People's Republic of China.

出版信息

J Agric Food Chem. 2024 Jun 12;72(23):13262-13272. doi: 10.1021/acs.jafc.4c00187. Epub 2024 May 22.

Abstract

Umami peptides are known for enhancing the taste experience by binding to oral umami T1R1 and T1R3 receptors. Among them, small peptides (composed of 2-4 amino acids) constitute nearly 40% of reported umami peptides. Given the diversity in amino acids and peptide sequences, umami small peptides possess tremendous untapped potential. By investigating 168,400 small peptides, we screened candidates binding to T1R1/T1R3 through molecular docking and molecular dynamics simulations, explored bonding types, amino acid characteristics, preferred binding sites, etc. Utilizing three-dimensional molecular descriptors, bonding information, and a back-propagation neural network, we developed a predictive model with 90.3% accuracy, identifying 24,539 potential umami peptides. Clustering revealed three classes with distinct logP (-2.66 ± 1.02, -3.52 ± 0.93, -2.44 ± 1.23) and asphericity (0.28 ± 0.12, 0.26 ± 0.11, 0.25 ± 0.11), indicating significant differences in shape and hydrophobicity ( < 0.05) among potential umami peptides binding to T1R1/T1R3. Following clustering, nine representative peptides (CQ, DP, NN, CSQ, DMC, TGS, DATE, HANR, and STAN) were synthesized and confirmed to possess umami taste through sensory evaluations and electronic tongue analyses. In summary, this study provides insights into exploring small peptide interactions with umami receptors, advancing umami peptide prediction models.

摘要

鲜味肽通过与口腔鲜味 T1R1 和 T1R3 受体结合而增强味觉体验。其中,小肽(由 2-4 个氨基酸组成)构成了近 40%的报道鲜味肽。鉴于氨基酸和肽序列的多样性,鲜味小肽具有巨大的未开发潜力。通过研究 168400 个小肽,我们通过分子对接和分子动力学模拟筛选了与 T1R1/T1R3 结合的候选物,探索了结合类型、氨基酸特征、首选结合位点等。利用三维分子描述符、键合信息和反向传播神经网络,我们开发了一个具有 90.3%准确性的预测模型,鉴定了 24539 个潜在的鲜味肽。聚类揭示了三个具有明显不同 logP(-2.66 ± 1.02、-3.52 ± 0.93、-2.44 ± 1.23)和各向异性(0.28 ± 0.12、0.26 ± 0.11、0.25 ± 0.11)的类,表明与 T1R1/T1R3 结合的潜在鲜味肽在形状和疏水性方面存在显著差异(<0.05)。聚类后,合成了 9 个代表性肽(CQ、DP、NN、CSQ、DMC、TGS、DATE、HANR 和 STAN),并通过感官评价和电子舌分析证实它们具有鲜味。总之,这项研究提供了对探索小肽与鲜味受体相互作用的深入了解,推进了鲜味肽预测模型。

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