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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/20244
Title: Anti-hypertensive Peptide Predictor: A Machine Learning-Empowered Web Server for Prediction of Food-Derived Peptides with Potential Angiotensin-Converting Enzyme-I Inhibitory Activity
Authors: Kalyan G.
Junghare V.
Khan M.F.
Pal S.
Bhattacharya S.
Guha S.
Majumder K.
Chakrabarty S.
Hazra, Saugata
Published in: Journal of Agricultural and Food Chemistry
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. ©
Citation: Journal of Agricultural and Food Chemistry, 69(49): 14995-15004
URI: https://doi.org/10.1021/acs.jafc.1c04555
http://repository.iitr.ac.in/handle/123456789/20244
Issue Date: 2021
Publisher: American Chemical Society
Keywords: ACE-I inhibition
angiotensin-converting enzyme (ACE)
anti-hypertensive activity
bioactive peptides
in silico proteolysis
machine learning
ISSN: 218561
Author Scopus IDs: 57194140464
57200569168
57365123800
57365411600
57202078739
57200855017
26027994500
57365123900
15837197000
Author Affiliations: Kalyan, G., Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India
Junghare, V., Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India
Khan, M.F., Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India
Pal, S., Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India
Bhattacharya, S., Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India
Guha, S., Department of Food Science and Technology, University of Nebraska-Lincoln, Lincoln, NE 68588, United States
Majumder, K., Department of Food Science and Technology, University of Nebraska-Lincoln, Lincoln, NE 68588, United States
Chakrabarty, S., Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India
Hazra, S., Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India, Centre for Nanotechnology, Indian Institute of Technology Roorkee, Roorkee, 247667, India
Funding Details: 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. DBT/2015/IIT-R/325; Department of Biotechnology, Ministry of Science and Technology, India, DBT
Corresponding Author: Hazra, S.; Department of Biosciences and Bioengineering, India; email: saugata.iitk@gmail.com
Appears in Collections:Journal Publications [BT]

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