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Please use this identifier to cite or link to this item: http://repository.li.mahidol.ac.th/dspace/handle/123456789/57726
Title: HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation
Authors: Md Mehedi Hasan
Nalini Schaduangrat
Shaherin Basith
Gwang Lee
Watshara Shoombuatong
Balachandran Manavalan
Kyushu Institute of Technology
Ajou University, School of Medicine
Japan Society for the Promotion of Science
Mahidol University
Ajou University
Keywords: Biochemistry, Genetics and Molecular Biology;Computer Science;Mathematics
Issue Date: 1-Jun-2020
Citation: Bioinformatics (Oxford, England). Vol.36, No.11 (2020), 3350-3356
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.
URI: http://repository.li.mahidol.ac.th/dspace/handle/123456789/57726
metadata.dc.identifier.url: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85082803673&origin=inward
ISSN: 13674811
Appears in Collections:Scopus 2020

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