Journal
FUTURE MEDICINAL CHEMISTRY
Volume 10, Issue 15, Pages 1749-1767Publisher
FUTURE SCI LTD
DOI: 10.4155/fmc-2017-0300
Keywords
angiotensin-converting enzyme inhibitory peptides; antihypertensive peptides; hypertension; machine learning; random forest
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Funding
- Office of Higher Education Commission [MRG6180226]
- Thailand Research Fund [MRG6180226]
- New Researcher Grant from Mahidol University [A31/2561]
- Center of Excellence on Medical Biotechnology (CEMB), S&T Postgraduate Education and Research Development Office (PERDO), Office of Higher Education Commission (OHEC), Thailand
- Mahidol University [B.E.2557-2559]
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Aim: Hypertension is associated with development of cardiovascular disease and has become a significant health problem worldwide. Naturally-derived antihypertensive peptides have emerged as promising alternatives to synthetic drugs. Materials & methods: This study introduces predictor of antihypertensive activity of peptides constructed using random forest classifier as a function of various combinations of amino acid, dipeptide and pseudoamino acid composition descriptors. Results: Classification models were assessed via independent test set that demonstrated accuracy of 84.73%. Feature importance analysis revealed the preference of proline and hydrophobic amino acids at the C-terminal as well as the preference of short peptides for robust activity. Conclusion: Model presented herein serves as a useful tool for predicting and analysis of antihypertensive activity of peptides.
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