Molecular Food Science Laboratory, School of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou, Zhejiang 310018, China.
College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China.
Food Chem. 2019 Jun 15;283:611-620. doi: 10.1016/j.foodchem.2019.01.078. Epub 2019 Jan 19.
The comprehensive mechanistic understanding of pungency and the binding interactions between pungent capsaicinoids from foods and their receptors have attracted increasing attention in food sensory and pharmaceutical fields. In this study, linear and quadratic statistically significant quantitative structure-pungency correlations have firstly been established for capsaicinoids by combining genetic function approximation and brute force approach and subsequently validated by the tests of cross validation, randomization, external prediction, Roy's r metrics and Golbraikh-Tropsha's criteria. The resultant optimal predictive correlation models have strong internal and external predictive capacities (r = 0.949-0.989, r = 0.860-0.955, r = 0.859-0.904), which elucidate the elementary electrostatic, hydrogen bonding, hydrophobic and steric structural requirements for the pungent perception of capsaicinoids. Finally, a series of new capsaicinoids was designed based on the insights from the established correlation models, and most of which showed excellent predicted pungency potency and acceptable ADMET properties.
辛辣感的综合机制理解以及食物中辣味辣椒素与其受体之间的结合相互作用在食品感官和制药领域引起了越来越多的关注。在这项研究中，我们首次通过遗传函数逼近和暴力算法相结合，为辣椒素建立了线性和二次统计上显著的定量结构-辛辣相关性，并通过交叉验证、随机化、外部预测、Roy 的 r 度量和 Golbraikh-Tropsha 标准进行了验证。所得的最佳预测相关模型具有很强的内部和外部预测能力（r=0.949-0.989，r=0.860-0.955，r=0.859-0.904），阐明了对辣椒素的辛辣感知的基本静电、氢键、疏水和空间结构要求。最后，根据所建立的相关模型的见解，设计了一系列新的辣椒素，其中大多数表现出优异的预测辛辣强度和可接受的 ADMET 特性。
