Thet Su Win1,2, Nalini Schaduangrat1, Virapong Prachayasittikul3, Chanin Nantasenamat1, Watshara Shoombuatong1. 1. Center of Data Mining & Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand. 2. Department of Medical Technology, University of Medical Technology, Yangon 11012, Myanmar. 3. Department of Clinical Microbiology & Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
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.
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.
Authors: Valeria V Kleandrova; Julio A Rojas-Vargas; Marcus T Scotti; Alejandro Speck-Planche Journal: Mol Divers Date: 2021-11-21 Impact factor: 3.364