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

Gazal Kalyan, Vivek Junghare, Mohammad Farhan Khan, Shivam Pal, Sourya Bhattacharya, Snigdha Guha, Kaustav Majumder, Sohom Chakrabarty, Saugata Hazra

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)14995-15004
Number of pages10
JournalJournal of Agricultural and Food Chemistry
Volume69
Issue number49
DOIs
StatePublished - Dec 15 2021

Keywords

  • ACE-I inhibition
  • angiotensin-converting enzyme (ACE)
  • anti-hypertensive activity
  • bioactive peptides
  • in silico proteolysis
  • machine learning

ASJC Scopus subject areas

  • Chemistry(all)
  • Agricultural and Biological Sciences(all)

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