HemoPred: a web server for predicting the hemolytic activity of peptides.

ABSTRACT

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 α-helix and ß-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.