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Please use this identifier to cite or link to this item: http://repository.li.mahidol.ac.th/dspace/handle/123456789/49546
Title: IQSP: A sequence-based tool for the prediction and analysis of quorum sensing peptides via chou’s 5-steps rule and informative physicochemical properties
Authors: Phasit Charoenkwan
Nalini Schaduangrat
Chanin Nantasenamat
Theeraphon Piacham
Watshara Shoombuatong
Mahidol University
Chiang Mai University
Keywords: Biochemistry, Genetics and Molecular Biology;Chemical Engineering;Chemistry;Computer Science
Issue Date: 1-Jan-2020
Citation: International Journal of Molecular Sciences. Vol.21, No.1 (2020)
Abstract: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. Understanding of quorum-sensing peptides (QSPs) in their functional mechanism plays an essential role in finding new opportunities to combat bacterial infections by designing drugs. With the avalanche of the newly available peptide sequences in the post-genomic age, it is highly desirable to develop a computational model for efficient, rapid and high-throughput QSP identification purely based on the peptide sequence information alone. Although, few methods have been developed for predicting QSPs, their prediction accuracy and interpretability still requires further improvements. Thus, in this work, we proposed an accurate sequence-based predictor (called iQSP) and a set of interpretable rules (called IR-QSP) for predicting and analyzing QSPs. In iQSP, we utilized a powerful support vector machine (SVM) cooperating with 18 informative features from physicochemical properties (PCPs). Rigorous independent validation test showed that iQSP achieved maximum accuracy and MCC of 93.00% and 0.86, respectively. Furthermore, a set of interpretable rules IR-QSP was extracted by using random forest model and the 18 informative PCPs. Finally, for the convenience of experimental scientists, the iQSP web server was established and made freely available online. It is anticipated that iQSP will become a useful tool or at least as a complementary existing method for predicting and analyzing QSPs.
URI: http://repository.li.mahidol.ac.th/dspace/handle/123456789/49546
metadata.dc.identifier.url: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85076926437&origin=inward
ISSN: 14220067
16616596
Appears in Collections:Scopus 2020

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