文章基本信息

标题：A Novel Machine Learning Strategy for the Prediction of Antihypertensive Peptides Derived from Food with High Efficiency
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作者：Liyang Wang ; Dantong Niu ; Xiaoya Wang 等
期刊名称：Foods
电子版ISSN：2304-8158
出版年度：2021
卷号：10
期号：3
页码：1-14
DOI：10.3390/foods10030550
出版社：MDPI Publishing
摘要：Strategies to screen antihypertensive peptides with high throughput and rapid speed will doubtlessly contribute to the treatment of hypertension. Food-derived antihypertensive peptides can reduce blood pressure without side effects. In the present study, a novel model based on the eXtreme Gradient Boosting (XGBoost) algorithm was developed and compared with the dominating machine learning models. To further reflect on the reliability of the method in a real situation, the optimized XGBoost model was utilized to predict the antihypertensive degree of the k-mer peptides cutting from six key proteins in bovine milk, and the peptide–protein docking technology was introduced to verify the findings. The results showed that the XGBoost model achieved outstanding performance, with an accuracy of 86.50% and area under the receiver operating characteristic curve of 94.11%, which were better than the other models. Using the XGBoost model, the prediction of antihypertensive peptides derived from milk protein was consistent with the peptide–protein docking results, and was more efficient. Our results indicate that using the XGBoost algorithm as a novel auxiliary tool is feasible to screen for antihypertensive peptides derived from food, with high throughput and high efficiency.
关键词：antihypertensive peptides; XGBoost algorithm; peptide–protein technology; milk protein; high throughput; high efficiency antihypertensive peptides ; XGBoost algorithm ; peptide–protein technology ; milk protein ; high throughput ; high efficiency