TY - JOUR
T1 - Anti-hypertensive Peptide Predictor
T2 - A Machine Learning-Empowered Web Server for Prediction of Food-Derived Peptides with Potential Angiotensin-Converting Enzyme-I Inhibitory Activity
AU - Kalyan, Gazal
AU - Junghare, Vivek
AU - Khan, Mohammad Farhan
AU - Pal, Shivam
AU - Bhattacharya, Sourya
AU - Guha, Snigdha
AU - Majumder, Kaustav
AU - Chakrabarty, Sohom
AU - Hazra, Saugata
N1 - Funding Information:
The Department of Biotechnology (DBT), Ministry of Science and Technology, Government of India (grant number DBT/2015/IIT-R/325 to G.K.), supported this research.
Publisher Copyright:
©
PY - 2021/12/15
Y1 - 2021/12/15
N2 - 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.
AB - 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.
KW - ACE-I inhibition
KW - angiotensin-converting enzyme (ACE)
KW - anti-hypertensive activity
KW - bioactive peptides
KW - in silico proteolysis
KW - machine learning
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U2 - 10.1021/acs.jafc.1c04555
DO - 10.1021/acs.jafc.1c04555
M3 - Article
C2 - 34855377
AN - SCOPUS:85120747282
SN - 0021-8561
VL - 69
SP - 14995
EP - 15004
JO - Journal of Agricultural and Food Chemistry
JF - Journal of Agricultural and Food Chemistry
IS - 49
ER -