title: HemoPred: a web server for predicting the hemolytic activity of peptides.
abstract: OBJECTIVE
  Toxicity arising from hemolytic activity of peptides hinders its further progress as drug candidates.
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Center of Data Mining & Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.|Department of Medical Laboratory Technology, University of Medical Technology, Yangon 11012, Myanmar.
authors.forename: Thet Su
authors.initials: TS
authors.lastname: Win
authors.affiliation: Center of Data Mining & Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
authors.forename: Aijaz Ahmad
authors.initials: AA
authors.lastname: Malik
authors.affiliation: Department of Clinical Microbiology & Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
authors.forename: Virapong
authors.initials: V
authors.lastname: Prachayasittikul
authors.affiliation: Department of Pharmaceutical Biosciences, BMC, Uppsala University, Sweden.
authors.forename: Jarl E
authors.initials: JE
authors.lastname: S Wikberg
authors.affiliation: Center of Data Mining & Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
authors.forename: Chanin
authors.initials: C
authors.lastname: Nantasenamat
authors.affiliation: Center of Data Mining & Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
authors.forename: Watshara
authors.initials: W
authors.lastname: Shoombuatong
mesh_terms: D000595:Amino Acid Sequence; D003198:Computer Simulation; D030562:Databases, Protein; D006461:Hemolysis; D053878:Hemolytic Agents / Q000737:chemistry* / Q000633:toxicity*; D006801:Humans; D000069550:Machine Learning; D010455:Peptides / Q000737:chemistry* / Q000633:toxicity*; D012984:Software
chemical_list: D053878:Hemolytic Agents; D010455:Peptides
keywords: classification; decision tree; hemolytic activity; hemolytic peptide; machine learning; random forest; support vector machine; therapeutic peptides