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Front. Genet. | doi: 10.3389/fgene.2019.00129

PreAIP: Computational prediction of anti-inflammatory peptides by integrating multiple complementary features

  • 1Biomedical Informatics R&D Center, Kyushu Institute of Technology, Japan

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 PreAIP 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 anti-inflammatory peptides and contributes to the development of anti-inflammatory peptides therapeutics and biomedical research. The curated datasets and the PreAIP are freely available at http://kurata14.bio.kyutech.ac.jp/PreAIP/.

Keywords: Inflammatory diseases, Anti-inflammatory peptides prediction, Feature encoding, machine learning, Feature Selection

Received: 25 Jul 2018; Accepted: 06 Feb 2019.

Edited by:

Yuriy L. Orlov, Institute of Cytology and Genetics, Russian Academy of Sciences, Russia

Reviewed by:

Deepak Singla, Punjab Agricultural University, India
Hifzur R. Ansari, King Abdullah International Medical Research Center KAIMRC, Saudi Arabia  

Copyright: © 2019 Khatun, Hasan and Kurata. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Hiroyuki Kurata, Biomedical Informatics R&D Center, Kyushu Institute of Technology, Fukuoka, Japan, kurata@bio.kyutech.ac.jp