Publication: PepBio: Predicting the bioactivity of host defense peptides
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Issued Date
2017-01-01
Resource Type
ISSN
20462069
Other identifier(s)
2-s2.0-85024910984
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Mahidol University
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SCOPUS
Bibliographic Citation
RSC Advances. Vol.7, No.56 (2017), 35119-35134
Suggested Citation
Saw Simeon, Hao Li, Thet Su Win, Aijaz Ahmad Malik, Abdul Hafeez Kandhro, Theeraphon Piacham, Watshara Shoombuatong, Pornlada Nuchnoi, Jarl E.S. Wikberg, M. Paul Gleeson, Chanin Nantasenamat PepBio: Predicting the bioactivity of host defense peptides. RSC Advances. Vol.7, No.56 (2017), 35119-35134. doi:10.1039/c7ra01388d Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/42144
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Title
PepBio: Predicting the bioactivity of host defense peptides
Abstract
© 2017 The Royal Society of Chemistry. Host defense peptides (HDPs) represents a class of ubiquitous and rapid responding immune molecules capable of direct inactivation of a wide range of pathogens. Recent research has shown HDPs to be promising candidates for development as a novel class of broad-spectrum chemotherapeutic agent that is effective against both pathogenic microbes and malignant neoplasm. This study aims to quantitatively explore the relationship between easy-to-interpret amino acid composition descriptors of HDPs with their respective bioactivities. Classification models were constructed using the C4.5 decision tree and random forest classifiers. Good predictive performance was achieved as deduced from the accuracy, sensitivity and specificity in excess of 90% and Matthews correlation coefficient in excess of 0.5 for all three evaluated data subsets (e.g. training, 10-fold cross-validation and external validation sets). The source code and data set used for the construction of classification models are available on GitHub at https://github.com/chaninn/pepbio/.