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

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.