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dc.contributor.authorThet Su Winen_US
dc.contributor.authorAijaz Ahmad Maliken_US
dc.contributor.authorVirapong Prachayasittikulen_US
dc.contributor.authorJarl E. S Wikbergen_US
dc.contributor.authorChanin Nantasenamaten_US
dc.contributor.authorWatshara Shoombuatongen_US
dc.contributor.otherMahidol Universityen_US
dc.contributor.otherUniversity of Medical Technologyen_US
dc.contributor.otherUppsala Universiteten_US
dc.identifier.citationFuture Medicinal Chemistry. Vol.9, No.3 (2017), 275-291en_US
dc.description.abstract© 2017 Future Science Ltd. 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.en_US
dc.rightsMahidol Universityen_US
dc.subjectBiochemistry, Genetics and Molecular Biologyen_US
dc.titleHemoPred: A web server for predicting the hemolytic activity of peptidesen_US
Appears in Collections:Scopus 2016-2017

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