Deep learning drives efficient discovery of novel antihypertensive peptides from soybean protein isolate.

Journal: Food chemistry
PMID: 36323032

Abstract

As a potential and effective substitute for the drugs of antihypertension, the food-derived antihypertensive peptides have arisen great interest in scholars recently. However, the traditional screening methods for antihypertensive peptides are at considerable expense and laborious, which blocks the exploration of available antihypertensive peptides. In our study, we reported the use of a protein-specific deep learning model called ProtBERT to screen for antihypertensive peptides. Compared to other deep learning models, ProrBERT reached the highest the area under the receiver operating characteristic curve (AUC) value of 0.9785. In addition, we used ProtBERT to screen candidate peptides in soybean protein isolate (SPI), followed by molecular docking and in vitro validation, and eventually found that peptides LVPFGW (IC50 = 20.63 μM), VSFPVL (2.57 μM), and VLPF (5.78 μM) demonstrated the good antihypertensive activity. Deep learning such as ProtBERT will be a useful tool for the rapid screening and identification of antihypertensive peptides.

Authors

  • Yiyun Zhang
    National Engineering and Technology Research Center for Fruits and Vegetables, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, PR China. Electronic address: 18681357759@163.com.
  • Zijian Dai
    National Engineering and Technology Research Center for Fruits and Vegetables, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, PR China. Electronic address: daizijian666@163.com.
  • Xinjie Zhao
    College of Humanities and Development Studies, China Agricultural University, Beijing 100083, PR China. Electronic address: sinketsuzao@foxmail.com.
  • Changyu Chen
    Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan.
  • Siqi Li
    Software College, Northeastern University, Shenyang 110819, China.
  • Yantong Meng
    National Engineering and Technology Research Center for Fruits and Vegetables, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, PR China. Electronic address: mengyantong@cau.edu.cn.
  • Zhuoma Suonan
    National Engineering and Technology Research Center for Fruits and Vegetables, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, PR China. Electronic address: suonanzhuoma@cau.edu.cn.
  • Yuge Sun
    National Engineering and Technology Research Center for Fruits and Vegetables, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, PR China. Electronic address: sunyuge@cau.edu.cn.
  • Qun Shen
    National Engineering and Technology Research Center for Fruits and Vegetables, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, PR China; National Center of Technology Innovation (Deep Processing of Highland Barley) in Food Industry, China Agricultural University, No. 17 Qinghua East Road, Haidian District, Beijing 100083, PR China. Electronic address: shenqun@cau.edu.cn.
  • Liyang Wang
    Beijing Advanced Innovation Center for Food Nutrition and Human Health, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China.
  • Yong Xue
    Guangzhou Panyu Central Hospital, Guangzhou, China.

Keywords

External Resources

Popular Topics
Recent Journals