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LAceP: Lysine Acetylation Site Prediction Using Logistic Regression Classifiers

Author

Listed:
  • Ting Hou
  • Guangyong Zheng
  • Pingyu Zhang
  • Jia Jia
  • Jing Li
  • Lu Xie
  • Chaochun Wei
  • Yixue Li

Abstract

Background: Lysine acetylation is a crucial type of protein post-translational modification, which is involved in many important cellular processes and serious diseases. However, identification of protein acetylated sites through traditional experiment methods is time-consuming and laborious. Those methods are not suitable to identify a large number of acetylated sites quickly. Therefore, computational methods are still very valuable to accelerate lysine acetylated site finding. Result: In this study, many biological characteristics of acetylated sites have been investigated, such as the amino acid sequence around the acetylated sites, the physicochemical property of the amino acids and the transition probability of adjacent amino acids. A logistic regression method was then utilized to integrate these information for generating a novel lysine acetylation prediction system named LAceP. When compared with existing methods, LAceP overwhelms most of state-of-the-art methods. Especially, LAceP has a more balanced prediction capability for positive and negative datasets. Conclusion: LAceP can integrate different biological features to predict lysine acetylation with high accuracy. An online web server is freely available at http://www.scbit.org/iPTM/.

Suggested Citation

  • Ting Hou & Guangyong Zheng & Pingyu Zhang & Jia Jia & Jing Li & Lu Xie & Chaochun Wei & Yixue Li, 2014. "LAceP: Lysine Acetylation Site Prediction Using Logistic Regression Classifiers," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-7, February.
  • Handle: RePEc:plo:pone00:0089575
    DOI: 10.1371/journal.pone.0089575
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    References listed on IDEAS

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    1. Sheng-Bao Suo & Jian-Ding Qiu & Shao-Ping Shi & Xing-Yu Sun & Shu-Yun Huang & Xiang Chen & Ru-Ping Liang, 2012. "Position-Specific Analysis and Prediction for Protein Lysine Acetylation Based on Multiple Features," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-11, November.
    2. Zhen Chen & Yong-Zi Chen & Xiao-Feng Wang & Chuan Wang & Ren-Xiang Yan & Ziding Zhang, 2011. "Prediction of Ubiquitination Sites by Using the Composition of k-Spaced Amino Acid Pairs," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-8, July.
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    Cited by:

    1. Qiqige Wuyun & Wei Zheng & Yanping Zhang & Jishou Ruan & Gang Hu, 2016. "Improved Species-Specific Lysine Acetylation Site Prediction Based on a Large Variety of Features Set," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-21, May.
    2. David Geisel & Peter Lenz, 2022. "Machine learning classification of trajectories from molecular dynamics simulations of chromosome segregation," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-33, January.

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