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Prediction of Protein Modification Sites of Pyrrolidone Carboxylic Acid Using mRMR Feature Selection and Analysis

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  • Lu-Lu Zheng
  • Shen Niu
  • Pei Hao
  • KaiYan Feng
  • Yu-Dong Cai
  • Yixue Li

Abstract

Pyrrolidone carboxylic acid (PCA) is formed during a common post-translational modification (PTM) of extracellular and multi-pass membrane proteins. In this study, we developed a new predictor to predict the modification sites of PCA based on maximum relevance minimum redundancy (mRMR) and incremental feature selection (IFS). We incorporated 727 features that belonged to 7 kinds of protein properties to predict the modification sites, including sequence conservation, residual disorder, amino acid factor, secondary structure and solvent accessibility, gain/loss of amino acid during evolution, propensity of amino acid to be conserved at protein-protein interface and protein surface, and deviation of side chain carbon atom number. Among these 727 features, 244 features were selected by mRMR and IFS as the optimized features for the prediction, with which the prediction model achieved a maximum of MCC of 0.7812. Feature analysis showed that all feature types contributed to the modification process. Further site-specific feature analysis showed that the features derived from PCA's surrounding sites contributed more to the determination of PCA sites than other sites. The detailed feature analysis in this paper might provide important clues for understanding the mechanism of the PCA formation and guide relevant experimental validations.

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  • Lu-Lu Zheng & Shen Niu & Pei Hao & KaiYan Feng & Yu-Dong Cai & Yixue Li, 2011. "Prediction of Protein Modification Sites of Pyrrolidone Carboxylic Acid Using mRMR Feature Selection and Analysis," PLOS ONE, Public Library of Science, vol. 6(12), pages 1-11, December.
  • Handle: RePEc:plo:pone00:0028221
    DOI: 10.1371/journal.pone.0028221
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    References listed on IDEAS

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    1. Tao Huang & WeiRen Cui & LeLe Hu & KaiYan Feng & Yi-Xue Li & Yu-Dong Cai, 2009. "Prediction of Pharmacological and Xenobiotic Responses to Drugs Based on Time Course Gene Expression Profiles," PLOS ONE, Public Library of Science, vol. 4(12), pages 1-7, December.
    2. I. King Jordan & Fyodor A. Kondrashov & Ivan A. Adzhubei & Yuri I. Wolf & Eugene V. Koonin & Alexey S. Kondrashov & Shamil Sunyaev, 2005. "A universal trend of amino acid gain and loss in protein evolution," Nature, Nature, vol. 433(7026), pages 633-638, February.
    3. I. King Jordan & Fyodor A. Kondrashov & Ivan A. Adzhubei & Yuri I. Wolf & Eugene V. Koonin & Alexey S. Kondrashov & Shamil Sunyaev, 2005. "Erratum: A universal trend of amino acid gain and loss in protein evolution," Nature, Nature, vol. 435(7041), pages 528-528, May.
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    Cited by:

    1. Huilin Wang & Mingjun Wang & Hao Tan & Yuan Li & Ziding Zhang & Jiangning Song, 2014. "PredPPCrys: Accurate Prediction of Sequence Cloning, Protein Production, Purification and Crystallization Propensity from Protein Sequences Using Multi-Step Heterogeneous Feature Fusion and Selection," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-17, August.

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