Abstract
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.
Financial & competing interests disclosure
This work is supported by the Office of Higher Education Commission and the Thailand Research Fund (No. MRG6180226); the New Researcher Grant (A31/2561) from Mahidol University; and the Center of Excellence on Medical Biotechnology (CEMB), S&T Postgraduate Education and Research Development Office (PERDO), Office of Higher Education Commission (OHEC), Thailand. Partial support from the annual budget grant (B.E.2557-2559) of Mahidol University is also acknowledged. The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.
No writing assistance was utilized in the production of this manuscript.