Guo Weidan, Ren Kangzi, Long Zhao, Fu Xiangjin, Zhang Jianan, Liu Min, Chen Yaquan
College of Food Science and Engineering, Central South University of Forestry and Technology, Changsha 410004, China.
Seasonings Green Manufacturing Engineering Technology Research Center of Hunan Province, Hun an Huixiangxuan Bio. Tech. Ltd. Com., Liuyang 410323, China.
Food Chem X. 2024 Oct 29;24:101940. doi: 10.1016/j.fochx.2024.101940. eCollection 2024 Dec 30.
In this study, a partial least squares discriminant analysis (PLS-DA) discriminant model for umami peptides was constructed based on molecular dynamics simulation data, achieving a value of 0.949 and a value of 0.558. Using this novel model and bioinformatics screening methods, five new umami peptides (EALEATAQ, SPPTEE, SEEG, KEE, and FEE, with umami taste thresholds of 0.139, 0.085, 0.096, 0.060, and 0.079 mg/mL, respectively) were identified in Douchi. Molecular docking revealed that the residues ASN150 of T1R1, as well as SER170, GLU301 and GLN389 of T1R3, might be key amino acid residues for the binding of umami peptides to T1R1/T1R3. Molecular dynamics simulations revealed significant differences in the root-mean-square fluctuation (RMSF) values between the two complex systems of umami peptides-T1R1/T1R3 and non-umami peptides-T1R1/T1R3. The newly constructed umami peptide discriminant model can improve the accuracy of umami peptide screening and enhance the efficiency of discovering new umami peptides.
在本研究中，基于分子动力学模拟数据构建了鲜味肽的偏最小二乘判别分析（PLS - DA）判别模型，其值为0.949，值为0.558。利用这一新型模型和生物信息学筛选方法，在豆豉中鉴定出5种新的鲜味肽（EALEATAQ、SPPTEE、SEEG、KEE和FEE，鲜味阈值分别为0.139、0.085、0.096、0.060和0.079 mg/mL）。分子对接显示，T1R1的ASN150残基以及T1R3的SER170、GLU301和GLN389残基可能是鲜味肽与T1R1/T1R3结合的关键氨基酸残基。分子动力学模拟显示，鲜味肽 - T1R1/T1R3和非鲜味肽 - T1R1/T1R3这两个复合系统之间的均方根波动（RMSF）值存在显著差异。新构建的鲜味肽判别模型可以提高鲜味肽筛选的准确性，增强发现新鲜味肽的效率。
