Deng Baichuan, Ni Xiaojun, Zhai Zhenya, Tang Tianyue, Tan Chengquan, Yan Yijing, Deng Jinping, Yin Yulong
Guangdong Provincial Key Laboratory of Animal Nutrition Control, Subtropical Institute of Animal Nutrition and Feed, College of Animal Science, South China Agricultural University , Guangzhou 510642, Guangdong, P.R. China.
National Engineering Laboratory for Pollution Control and Waste Utilization in Livestock and Poultry Production, Key Laboratory of Agro-Ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences , Changsha 410125, Hunan, P.R. China.
J Agric Food Chem. 2017 Nov 8;65(44):9774-9781. doi: 10.1021/acs.jafc.7b03367. Epub 2017 Oct 25.
Angiotensin-converting enzyme (ACE) inhibitory peptides derived from food proteins have been widely reported for hypertension treatment. In this paper, a benchmark data set containing 141 unique ACE inhibitory dipeptides was constructed through database mining, and a quantitative structure-activity relationships (QSAR) study was carried out to predict half-inhibitory concentration (IC) of ACE activity. Sixteen descriptors were tested and the model generated by G-scale descriptor showed the best predictive performance with the coefficient of determination (R) and cross-validated R (Q) of 0.6692 and 0.6220, respectively. For most other descriptors, R were ranging from 0.52 to 0.68 and Q were ranging from 0.48 to 0.61. A complex model combining all 16 descriptors was carried out and variable selection was performed in order to further improve the prediction performance. The quality of model using integrated descriptors (R 0.7340 ± 0.0038, Q 0.7151 ± 0.0019) was better than that of G-scale. An in-depth study of variable importance showed that the most correlated properties to ACE inhibitory activity were hydrophobicity, steric, and electronic properties and C-terminal amino acids contribute more than N-terminal amino acids. Five novel predicted ACE-inhibitory peptides were synthesized, and their IC values were validated through in vitro experiments. The results indicated that the constructed model could give a reliable prediction of ACE-inhibitory activity of peptides, and it may be useful in the design of novel ACE-inhibitory peptides.
源自食物蛋白的血管紧张素转换酶（ACE）抑制肽已被广泛报道用于高血压治疗。本文通过数据库挖掘构建了一个包含141种独特ACE抑制二肽的基准数据集，并进行了定量构效关系（QSAR）研究以预测ACE活性的半抑制浓度（IC）。测试了16个描述符，由G-scale描述符生成的模型显示出最佳预测性能，决定系数（R）和交叉验证R（Q）分别为0.6692和0.6220。对于大多数其他描述符，R在0.52至0.68之间，Q在0.48至0.61之间。进行了一个结合所有16个描述符的复杂模型，并进行了变量选择以进一步提高预测性能。使用综合描述符的模型质量（R 0.7340±0.0038，Q 0.7151±0.0019）优于G-scale。对变量重要性的深入研究表明，与ACE抑制活性最相关的性质是疏水性、空间和电子性质，并且C端氨基酸比N端氨基酸贡献更大。合成了5种新预测的ACE抑制肽，并通过体外实验验证了它们的IC值。结果表明，构建的模型可以对肽的ACE抑制活性进行可靠的预测，并且可能有助于新型ACE抑制肽的设计。
