Journal
FUTURE MEDICINAL CHEMISTRY
Volume 9, Issue 3, Pages 275-291Publisher
Newlands Press Ltd
DOI: 10.4155/fmc-2016-0188
Keywords
classification; decision tree; hemolytic activity; hemolytic peptide; machine learning; random forest; support vector machine; therapeutic peptides
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Funding
- Mahidol-Norway Capacity Building Initiative in Myanmar (CBIM)
- Thailand Research Fund [MRG5980220, RSA5780031]
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Aim: Toxicity arising from hemolytic activity of peptides hinders its further progress as drug candidates. Materials & methods: This study describes a sequence-based predictor based on a random forest classifier using amino acid composition, dipeptide composition and physicochemical descriptors (named HemoPred). Results: This approach could outperform previously reported method and typical classification methods (e.g., support vector machine and decision tree) verified by fivefold cross-validation and external validation with accuracy and Matthews correlation coefficient in excess of 95% and 0.91, respectively. Results revealed the importance of hydrophobic and Cys residues on alpha-helix and beta-sheet, respectively, on the hemolytic activity. Conclusion: A sequence-based predictor which is publicly available as the web service of HemoPred, is proposed to predict and analyze the hemolytic activity of peptides.
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