Publication: HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation
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Issued Date
2020-06-01
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ISSN
13674811
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2-s2.0-85082803673
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Mahidol University
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SCOPUS
Bibliographic Citation
Bioinformatics (Oxford, England). Vol.36, No.11 (2020), 3350-3356
Suggested Citation
Md Mehedi Hasan, Nalini Schaduangrat, Shaherin Basith, Gwang Lee, Watshara Shoombuatong, Balachandran Manavalan HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation. Bioinformatics (Oxford, England). Vol.36, No.11 (2020), 3350-3356. doi:10.1093/bioinformatics/btaa160 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/57726
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Title
HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation
Abstract
© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. MOTIVATION: Therapeutic peptides failing at clinical trials could be attributed to their toxicity profiles like hemolytic activity, which hamper further progress of peptides as drug candidates. The accurate prediction of hemolytic peptides (HLPs) and its activity from the given peptides is one of the challenging tasks in immunoinformatics, which is essential for drug development and basic research. Although there are a few computational methods that have been proposed for this aspect, none of them are able to identify HLPs and their activities simultaneously. RESULTS: In this study, we proposed a two-layer prediction framework, called HLPpred-Fuse, that can accurately and automatically predict both hemolytic peptides (HLPs or non-HLPs) as well as HLPs activity (high and low). More specifically, feature representation learning scheme was utilized to generate 54 probabilistic features by integrating six different machine learning classifiers and nine different sequence-based encodings. Consequently, the 54 probabilistic features were fused to provide sufficiently converged sequence information which was used as an input to extremely randomized tree for the development of two final prediction models which independently identify HLP and its activity. Performance comparisons over empirical cross-validation analysis, independent test and case study against state-of-the-art methods demonstrate that HLPpred-Fuse consistently outperformed these methods in the identification of hemolytic activity. AVAILABILITY AND IMPLEMENTATION: For the convenience of experimental scientists, a web-based tool has been established at http://thegleelab.org/HLPpred-Fuse. CONTACT: glee@ajou.ac.kr or watshara.sho@mahidol.ac.th or bala@ajou.ac.kr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.