HemoPred: a web server for predicting the hemolytic activity of peptides. (March 2017)

• Record Type: Journal Article
• Authors: Win, Thet Su; Malik, Aijaz Ahmad; Prachayasittikul, Virapong; S Wikberg, Jarl E; Nantasenamat, Chanin; Shoombuatong, Watshara
• Is Part Of: Future medicinal chemistry. Volume 9:Number 3(2017)
• Page Start: 275
• Page End: 291
• Publication Date: 2017-03
• Subjects: classification -- decision tree -- hemolytic activity -- hemolytic peptide -- machine learning -- random forest -- support vector machine -- therapeutic peptides; Pharmaceutical chemistry -- Periodicals; 615.19005
• DOI: 10.4155/fmc-2016-0188
• Languages: English
• ISSNs: 1756-8919

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 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.