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Prediction of lysine ubiquitination with mRMR feature selection and analysis

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Abstract

Ubiquitination, one of the most important post-translational modifications of proteins, occurs when ubiquitin (a small 76-amino acid protein) is attached to lysine on a target protein. It often commits the labeled protein to degradation and plays important roles in regulating many cellular processes implicated in a variety of diseases. Since ubiquitination is rapid and reversible, it is time-consuming and labor-intensive to identify ubiquitination sites using conventional experimental approaches. To efficiently discover lysine-ubiquitination sites, a sequence-based predictor of ubiquitination site was developed based on nearest neighbor algorithm. We used the maximum relevance and minimum redundancy principle to identify the key features and the incremental feature selection procedure to optimize the prediction engine. PSSM conservation scores, amino acid factors and disorder scores of the surrounding sequence formed the optimized 456 features. The Mathew’s correlation coefficient (MCC) of our ubiquitination site predictor achieved 0.142 by jackknife cross-validation test on a large benchmark dataset. In independent test, the MCC of our method was 0.139, higher than the existing ubiquitination site predictor UbiPred and UbPred. The MCCs of UbiPred and UbPred on the same test set were 0.135 and 0.117, respectively. Our analysis shows that the conservation of amino acids at and around lysine plays an important role in ubiquitination site prediction. What’s more, disorder and ubiquitination have a strong relevance. These findings might provide useful insights for studying the mechanisms of ubiquitination and modulating the ubiquitination pathway, potentially leading to potential therapeutic strategies in the future.

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Acknowledgments

The authors acknowledge Yvonne Poindexter at the Vanderbilt University Cancer Biostatistics Center for her editing. This work was supported by grants from National High-Tech R&D Program of China (863 Program) (2006AA02Z334, 2007DFA31040), China National Key Projects for Infectious Disease (2008ZX10002-021), National Basic Research Program of China (2006CB910700), National Natural Science Foundation of China (Grant No. 31070752) and Key Research Program (CAS) (KSCX2-YW-R-112).

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Authors and Affiliations

  1. Institute of Systems Biology, Shanghai University, Shanghai, 200444, People’s Republic of China

    Yudong Cai & Lele Hu

  2. Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, People’s Republic of China

    Tao Huang & Yixue Li

  3. Shanghai Center for Bioinformation Technology, Shanghai, 200235, People’s Republic of China

    Tao Huang, Lu Xie & Yixue Li

  4. Centre for Computational Systems Biology, Fudan University, Shanghai, 200433, People’s Republic of China

    Yudong Cai

  5. Singapore Bioimaging Consortium, Agency for Science, Technology and Research, Singapore, 138667, Singapore

    Xiaohe Shi

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  1. Yudong Cai
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  2. Tao Huang
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  4. Xiaohe Shi
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  5. Lu Xie
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Corresponding authors

Correspondence to Yudong Cai or Yixue Li.

Additional information

Y. Cai and T. Huang contributed equally to this work.

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Supplementary Material

Dataset S1 - Benchmark dataset.

Table S1 - The IFS result.

Table S2 - The 456 optimal features.

Dataset S1 - Benchmark dataset (XLS 127 kb)

Table S1 - The IFS result (XLS 73 kb)

Table S2 - The 456 optimal features (XLS 67 kb)

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Cai, Y., Huang, T., Hu, L. et al. Prediction of lysine ubiquitination with mRMR feature selection and analysis. Amino Acids 42, 1387–1395 (2012). https://doi.org/10.1007/s00726-011-0835-0

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  • DOI: https://doi.org/10.1007/s00726-011-0835-0

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