期刊
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
卷 21, 期 1, 页码 -出版社
MDPI
DOI: 10.3390/ijms21010075
关键词
quorum sensing peptides; physicochemical properties; support vector machine; random forest; machine learning; classification
资金
- TRF [MRG6180226]
- College of Arts, Media and Technology, Chiang Mai University
- TRF Research Career Development Grant from the Thailand Research Fund the O ffice of Higher Education Commission [RSA6280075]
- Mahidol University
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.
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