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.
journal_name
Future Med Chemjournal_title
Future medicinal chemistryauthors
Win TS,Schaduangrat N,Prachayasittikul V,Nantasenamat C,Shoombuatong Wdoi
10.4155/fmc-2017-0300subject
Has Abstractpub_date
2018-08-01 00:00:00pages
1749-1767issue
15eissn
1756-8919issn
1756-8927journal_volume
10pub_type
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