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|Title:||PAAP: A web server for predicting antihypertensive activity of peptides|
|Authors:||Thet Su Win|
University of Medical Technology
|Keywords:||Biochemistry, Genetics and Molecular Biology;Pharmacology, Toxicology and Pharmaceutics|
|Citation:||Future Medicinal Chemistry. Vol.10, No.15 (2018), 1749-1767|
|Abstract:||© 2018 Newlands Press. 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.|
|Appears in Collections:||Scopus 2018|
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