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

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.