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Prediction of Antimicrobial Peptides Based on Sequence Alignment and Feature Selection Methods

Author

Listed:
  • Ping Wang
  • Lele Hu
  • Guiyou Liu
  • Nan Jiang
  • Xiaoyun Chen
  • Jianyong Xu
  • Wen Zheng
  • Li Li
  • Ming Tan
  • Zugen Chen
  • Hui Song
  • Yu-Dong Cai
  • Kuo-Chen Chou

Abstract

Antimicrobial peptides (AMPs) represent a class of natural peptides that form a part of the innate immune system, and this kind of ‘nature's antibiotics’ is quite promising for solving the problem of increasing antibiotic resistance. In view of this, it is highly desired to develop an effective computational method for accurately predicting novel AMPs because it can provide us with more candidates and useful insights for drug design. In this study, a new method for predicting AMPs was implemented by integrating the sequence alignment method and the feature selection method. It was observed that, the overall jackknife success rate by the new predictor on a newly constructed benchmark dataset was over 80.23%, and the Mathews correlation coefficient is 0.73, indicating a good prediction. Moreover, it is indicated by an in-depth feature analysis that the results are quite consistent with the previously known knowledge that some amino acids are preferential in AMPs and that these amino acids do play an important role for the antimicrobial activity. For the convenience of most experimental scientists who want to use the prediction method without the interest to follow the mathematical details, a user-friendly web-server is provided at http://amp.biosino.org/.

Suggested Citation

  • Ping Wang & Lele Hu & Guiyou Liu & Nan Jiang & Xiaoyun Chen & Jianyong Xu & Wen Zheng & Li Li & Ming Tan & Zugen Chen & Hui Song & Yu-Dong Cai & Kuo-Chen Chou, 2011. "Prediction of Antimicrobial Peptides Based on Sequence Alignment and Feature Selection Methods," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-9, April.
    • Handle: RePEc:plo:pone00:0018476
    DOI: 10.1371/journal.pone.0018476
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    Cited by:

    1. Guohua Huang & Yuchao Zhang & Lei Chen & Ning Zhang & Tao Huang & Yu-Dong Cai, 2014. "Prediction of Multi-Type Membrane Proteins in Human by an Integrated Approach," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-11, March.
    2. Jianjun He & Hong Gu & Wenqi Liu, 2012. "Imbalanced Multi-Modal Multi-Label Learning for Subcellular Localization Prediction of Human Proteins with Both Single and Multiple Sites," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-10, June.
    3. Giuseppe Maccari & Mariagrazia Di Luca & Riccardo Nifosí & Francesco Cardarelli & Giovanni Signore & Claudia Boccardi & Angelo Bifone, 2013. "Antimicrobial Peptides Design by Evolutionary Multiobjective Optimization," PLOS Computational Biology, Public Library of Science, vol. 9(9), pages 1-12, September.
    4. Catherine Mooney & Niall J Haslam & Gianluca Pollastri & Denis C Shields, 2012. "Towards the Improved Discovery and Design of Functional Peptides: Common Features of Diverse Classes Permit Generalized Prediction of Bioactivity," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-12, October.

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