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ProIn-Fuse: improved and robust prediction of proinflammatory peptides by fusing of multiple feature representations

Abstract

A proinflammatory peptide (PIP) is a type of signaling molecules that are secreted from immune cells, which contributes to the first line of defense against invading pathogens. Numerous experiments have shown that PIPs play an important role in human physiology such as vaccines and immunotherapeutic drugs. Considering high-throughput laboratory methods that are time consuming and costly, effective computational methods are great demand to timely and accurately identify PIPs. Thus, in this study, we proposed a computational model in conjunction with a multiple feature representation, called ProIn-Fuse, to improve the performance of PIPs identification. Specifically, a feature representation learning model was utilized to generate the probabilistic scores by using the random forest models employing eight sequence encoding schemes. Finally, the ProIn-Fuse was constructed by linearly combining the resultant eight probabilistic scores. Evaluated through independent test, the ProIn-Fuse yielded an accuracy of 0.746, which was 10% higher than those obtained by the state-of-the-art PIP predictors. The proposed ProIn-Fuse can facilitate faster and broader applications of PIPs in drug design and development. The web server, datasets and online instruction are freely accessible at http://kurata14.bio.kyutech.ac.jp/ProIn-Fuse.

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Acknowledgements

We thank the reviewers for their great comments in helping to improve this manuscript. This work is supported by the Grant-in-Aid for JSPS Research Fellow (19F19377) from Japan Society for the Promotion of Science (JSPS), partially supported from Japan Society for the Promotion of Science by Grant-in-Aid for Scientific Research (B) (19H04208) and by the developing key technologies for discovering and manufacturing pharmaceuticals used for next-generation treatments and diagnoses both from the Ministry of Economy, Trade and Industry, Japan (METI) and from Japan Agency for Medical Research and Development (AMED).

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Correspondence to Md. Mehedi Hasan or Hiroyuki Kurata.

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Khatun, M.S., Hasan, M.M., Shoombuatong, W. et al. ProIn-Fuse: improved and robust prediction of proinflammatory peptides by fusing of multiple feature representations. J Comput Aided Mol Des 34, 1229–1236 (2020). https://doi.org/10.1007/s10822-020-00343-9

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Keywords

  • Proinflammatory peptide
  • Immune diseases
  • Feature encoding
  • Random forest; machine learning