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IRC-Fuse: improved and robust prediction of redox-sensitive cysteine by fusing of multiple feature representations

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Abstract

Redox-sensitive cysteine (RSC) thiol contributes to many biological processes. The identification of RSC plays an important role in clarifying some mechanisms of redox-sensitive factors; nonetheless, experimental investigation of RSCs is expensive and time-consuming. The computational approaches that quickly and accurately identify candidate RSCs using the sequence information are urgently needed. Herein, an improved and robust computational predictor named IRC-Fuse was developed to identify the RSC by fusing of multiple feature representations. To enhance the performance of our model, we integrated the probability scores evaluated by the random forest models implementing different encoding schemes. Cross-validation results exhibited that the IRC-Fuse achieved accuracy and AUC of 0.741 and 0.807, respectively. The IRC-Fuse outperformed exiting methods with improvement of 10% and 13% on accuracy and MCC, respectively, over independent test data. Comparative analysis suggested that the IRC-Fuse was more effective and promising than the existing predictors. For the convenience of experimental scientists, the IRC-Fuse online web server was implemented and publicly accessible at http://kurata14.bio.kyutech.ac.jp/IRC-Fuse/.

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Acknowledgements

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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Authors and Affiliations

  1. Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan

    Md Mehedi Hasan & Hiroyuki Kurata

  2. Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo, 102-0083, Japan

    Md Mehedi Hasan

  3. Tulane Center of Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112, USA

    Md Ashad Alam

  4. Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand

    Watshara Shoombuatong

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  1. Md Mehedi Hasan
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  3. Watshara Shoombuatong
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  4. Hiroyuki Kurata
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Hasan, M.M., Alam, M.A., Shoombuatong, W. et al. IRC-Fuse: improved and robust prediction of redox-sensitive cysteine by fusing of multiple feature representations. J Comput Aided Mol Des 35, 315–323 (2021). https://doi.org/10.1007/s10822-020-00368-0

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