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Prediction of Protein Domain with mRMR Feature Selection and Analysis

Author

Listed:
  • Bi-Qing Li
  • Le-Le Hu
  • Lei Chen
  • Kai-Yan Feng
  • Yu-Dong Cai
  • Kuo-Chen Chou

Abstract

The domains are the structural and functional units of proteins. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop effective methods for predicting the protein domains according to the sequences information alone, so as to facilitate the structure prediction of proteins and speed up their functional annotation. However, although many efforts have been made in this regard, prediction of protein domains from the sequence information still remains a challenging and elusive problem. Here, a new method was developed by combing the techniques of RF (random forest), mRMR (maximum relevance minimum redundancy), and IFS (incremental feature selection), as well as by incorporating the features of physicochemical and biochemical properties, sequence conservation, residual disorder, secondary structure, and solvent accessibility. The overall success rate achieved by the new method on an independent dataset was around 73%, which was about 28–40% higher than those by the existing method on the same benchmark dataset. Furthermore, it was revealed by an in-depth analysis that the features of evolution, codon diversity, electrostatic charge, and disorder played more important roles than the others in predicting protein domains, quite consistent with experimental observations. It is anticipated that the new method may become a high-throughput tool in annotating protein domains, or may, at the very least, play a complementary role to the existing domain prediction methods, and that the findings about the key features with high impacts to the domain prediction might provide useful insights or clues for further experimental investigations in this area. Finally, it has not escaped our notice that the current approach can also be utilized to study protein signal peptides, B-cell epitopes, HIV protease cleavage sites, among many other important topics in protein science and biomedicine.

Suggested Citation

  • Bi-Qing Li & Le-Le Hu & Lei Chen & Kai-Yan Feng & Yu-Dong Cai & Kuo-Chen Chou, 2012. "Prediction of Protein Domain with mRMR Feature Selection and Analysis," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-14, June.
  • Handle: RePEc:plo:pone00:0039308
    DOI: 10.1371/journal.pone.0039308
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    References listed on IDEAS

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    Cited by:

    1. Bi-Qing Li & Tao Huang & Jian Zhang & Ning Zhang & Guo-Hua Huang & Lei Liu & Yu-Dong Cai, 2013. "An Ensemble Prognostic Model for Colorectal Cancer," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-8, May.
    2. Xin Ma & Jing Guo & Xiao Sun, 2016. "DNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-20, December.
    3. Lei Chen & Jing Lu & Jian Zhang & Kai-Rui Feng & Ming-Yue Zheng & Yu-Dong Cai, 2013. "Predicting Chemical Toxicity Effects Based on Chemical-Chemical Interactions," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-9, February.

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