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Anti-hypertensive Peptide Predictor: A Machine Learning-Empowered Web Server for Prediction of Food-Derived Peptides
Gazal Kalyan1, Vivek Junghare1, Mohammad Farhan Khan2
1Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee 247667, India.
Journal of Agricultural and Food Chemistry
|December 2, 2021
Summary
Researchers developed a web server to efficiently identify food-derived peptides that inhibit Angiotensin converting enzyme-I (ACE-I). This tool uses machine learning and bioinformatics to speed up the discovery of potential ACE-I inhibitors from food proteins.
Area of Science:
- Biochemistry
- Bioinformatics
- Food Science
Background:
- Angiotensin converting enzyme-I (ACE-I) is a critical target for blood pressure regulation within the renin-angiotensin-aldosterone system (RAAS).
- Food-derived peptides exhibiting ACE-I inhibitory activity are of significant scientific interest for potential therapeutic applications.
- Current methods for identifying these peptides are time-consuming, resource-intensive, and costly.
Purpose of the Study:
- To develop and present a novel web server for the efficient identification and characterization of ACE-I inhibitory peptides from food proteins.
- To leverage machine learning and structural bioinformatics for *in silico* screening of potential ACE-I inhibitors.
- To reduce the time and cost associated with discovering novel bioactive peptides from dietary sources.
Main Methods:
- Development of a web server accepting FASTA or UniProt ID inputs.
- Implementation of *in silico* gastrointestinal digestion to simulate peptide release.
- Screening of generated peptides for ACE-I inhibitory activity using machine learning and structural bioinformatics models.
- Analysis of structural and functional features of active peptides and their interactions with ACE-I.
Main Results:
- A functional web server (AHPP) has been successfully developed and validated.
- The platform enables rapid *in silico* digestion and screening of peptides for ACE-I inhibitory potential.
- Detailed structural and functional insights into active peptides and their ACE-I binding are provided.
Conclusions:
- The developed web server significantly enhances the efficiency of identifying novel ACE-I inhibitory peptides from food proteins.
- This computational approach offers a cost-effective and time-saving alternative to traditional experimental methods.
- The tool facilitates a deeper understanding of peptide-ACE-I interactions, aiding in the development of functional foods and therapeutics.
Keywords:
ACE-I inhibitionangiotensin-converting enzyme (ACE)anti-hypertensive activitybioactive peptidesin silico proteolysismachine learningMore Related Videos
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