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HemoPred: a web server for predicting the hemolytic activity of peptides
Thet Su Win1,2, Aijaz Ahmad Malik1, Virapong Prachayasittikul3
1Center of Data Mining & Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
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 β-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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