期刊
JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY
卷 69, 期 49, 页码 14995-15004出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jafc.1c04555
关键词
angiotensin-converting enzyme (ACE); bioactive peptides; anti-hypertensive activity; in silico proteolysis; machine learning; ACE-I inhibition
资金
- Department of Biotechnology (DBT), Ministry of Science and Technology, Government of India [DBT/2015/IIT-R/325]
The newly developed web server utilizes machine learning and structural bioinformatics to identify ACE-I inhibitory peptides from food sources through in silico digestion and screening of peptides, potentially enhancing efficiency and reducing cost.
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.
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