Anti-hypertensive Peptide Predictor: A Machine Learning-Empowered Web Server for Prediction of Food-Derived Peptides with Potential Angiotensin-Converting Enzyme-I Inhibitory Activity

Abstract

Angiotensin converting enzyme-I (ACE-I) is a key therapeutic target of the renin-angiotensin-aldosterone system (RAAS), the central pathway of blood pressure regulation. Food-derived peptides with ACE-I inhibitory activities are receiving significant research attention. However, identification of ACE-I inhibitory peptides from different food proteins is a labor-intensive, lengthy, and expensive process. For successful identification of potential ACE-I inhibitory peptides from food sources, a machine learning and structural bioinformatics-based web server has been developed and reported in this study. The web server can take input in the FASTA format or through UniProt ID to perform the in silico gastrointestinal digestion and then screen the resulting peptides for ACE-I inhibitory activity. This unique platform provides elaborated structural and functional features of the active peptides and their interaction with ACE-I. Thus, it can potentially enhance the efficacy and reduce the time and cost in identifying and characterizing novel ACE-I inhibitory peptides from food proteins. URL: http://hazralab.iitr.ac.in/ahpp/index.php. ©