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Computational Identification of Protein Methylation Sites through Bi-Profile Bayes Feature Extraction

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  • Jianlin Shao
  • Dong Xu
  • Sau-Na Tsai
  • Yifei Wang
  • Sai-Ming Ngai

Abstract

Protein methylation is one type of reversible post-translational modifications (PTMs), which plays vital roles in many cellular processes such as transcription activity, DNA repair. Experimental identification of methylation sites on proteins without prior knowledge is costly and time-consuming. In silico prediction of methylation sites might not only provide researches with information on the candidate sites for further determination, but also facilitate to perform downstream characterizations and site-specific investigations. In the present study, a novel approach based on Bi-profile Bayes feature extraction combined with support vector machines (SVMs) was employed to develop the model for Prediction of Protein Methylation Sites (BPB-PPMS) from primary sequence. Methylation can occur at many residues including arginine, lysine, histidine, glutamine, and proline. For the present, BPB-PPMS is only designed to predict the methylation status for lysine and arginine residues on polypeptides due to the absence of enough experimentally verified data to build and train prediction models for other residues. The performance of BPB-PPMS is measured with a sensitivity of 74.71%, a specificity of 94.32% and an accuracy of 87.98% for arginine as well as a sensitivity of 70.05%, a specificity of 77.08% and an accuracy of 75.51% for lysine in 5-fold cross validation experiments. Results obtained from cross-validation experiments and test on independent data sets suggest that BPB-PPMS presented here might facilitate the identification and annotation of protein methylation. Besides, BPB-PPMS can be extended to build predictors for other types of PTM sites with ease. For public access, BPB-PPMS is available at http://www.bioinfo.bio.cuhk.edu.hk/bpbppms.

Suggested Citation

  • Jianlin Shao & Dong Xu & Sau-Na Tsai & Yifei Wang & Sai-Ming Ngai, 2009. "Computational Identification of Protein Methylation Sites through Bi-Profile Bayes Feature Extraction," PLOS ONE, Public Library of Science, vol. 4(3), pages 1-7, March.
  • Handle: RePEc:plo:pone00:0004920
    DOI: 10.1371/journal.pone.0004920
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    References listed on IDEAS

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    1. Maria-Elena Torres-Padilla & David-Emlyn Parfitt & Tony Kouzarides & Magdalena Zernicka-Goetz, 2007. "Histone arginine methylation regulates pluripotency in the early mouse embryo," Nature, Nature, vol. 445(7124), pages 214-218, January.
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    Cited by:

    1. Yan Xu & Jun Ding & Ling-Yun Wu & Kuo-Chen Chou, 2013. "iSNO-PseAAC: Predict Cysteine S-Nitrosylation Sites in Proteins by Incorporating Position Specific Amino Acid Propensity into Pseudo Amino Acid Composition," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-7, February.
    2. Shao-Ping Shi & Jian-Ding Qiu & Xing-Yu Sun & Sheng-Bao Suo & Shu-Yun Huang & Ru-Ping Liang, 2012. "PMeS: Prediction of Methylation Sites Based on Enhanced Feature Encoding Scheme," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-11, June.
    3. Jiangning Song & Hao Tan & Andrew J Perry & Tatsuya Akutsu & Geoffrey I Webb & James C Whisstock & Robert N Pike, 2012. "PROSPER: An Integrated Feature-Based Tool for Predicting Protease Substrate Cleavage Sites," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-23, November.
    4. Wenzheng Bao & Bin Yang & Rong Bao & Yuehui Chen, 2019. "LipoFNT: Lipoylation Sites Identification with Flexible Neural Tree," Complexity, Hindawi, vol. 2019, pages 1-9, July.

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