期刊
FRONTIERS IN GENETICS
卷 10, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fgene.2019.00129
关键词
inflammatory disease; anti-inflammatory peptides prediction; feature encoding; feature selection; random forest
资金
- JSPS KAKENHI [17K20009]
- Ministry of Economy, Trade and Industry, Japan (METI)
- Japan Agency for Medical Research and Development (AMED)
- Grants-in-Aid for Scientific Research [17K20009] Funding Source: KAKEN
Numerous inflammatory diseases and autoimmune disorders by therapeutic peptides have received substantial consideration; however, the exploration of anti-inflammatory peptides via biological experiments is often a time-consuming and expensive task. The development of novel in silico predictors is desired to classify potential anti-inflammatory peptides prior to in vitro investigation. Herein, an accurate predictor, called PreAIP (Predictor of Anti-Inflammatory Peptides) was developed by integrating multiple complementary features. We systematically investigated different types of features including primary sequence, evolutionary and structural information through a random forest classifier. The final PreAlP model achieved an AUC value of 0.833 in the training dataset via 10-fold cross-validation test, which was better than that of existing models. Moreover, we assessed the performance of the PreAIP with an AUC value of 0.840 on a test dataset to demonstrate that the proposed method outperformed the two existing methods. These results indicated that the PreAIP is an accurate predictor for identifying AlPs and contributes to the development of AlPs therapeutics and biomedical research. The curated datasets and the PreAlP are freely available at http://kurata14.bio.kyutech.ac.jp/PreAIP/.
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