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Predicting Metabolic Pathways of Small Molecules and Enzymes Based on Interaction Information of Chemicals and Proteins

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  • Yu-Fei Gao
  • Lei Chen
  • Yu-Dong Cai
  • Kai-Yan Feng
  • Tao Huang
  • Yang Jiang

Abstract

Metabolic pathway analysis, one of the most important fields in biochemistry, is pivotal to understanding the maintenance and modulation of the functions of an organism. Good comprehension of metabolic pathways is critical to understanding the mechanisms of some fundamental biological processes. Given a small molecule or an enzyme, how may one identify the metabolic pathways in which it may participate? Answering such a question is a first important step in understanding a metabolic pathway system. By utilizing the information provided by chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions, a novel method was proposed by which to allocate small molecules and enzymes to 11 major classes of metabolic pathways. A benchmark dataset consisting of 3,348 small molecules and 654 enzymes of yeast was constructed to test the method. It was observed that the first order prediction accuracy evaluated by the jackknife test was 79.56% in identifying the small molecules and enzymes in a benchmark dataset. Our method may become a useful vehicle in predicting the metabolic pathways of small molecules and enzymes, providing a basis for some further analysis of the pathway systems.

Suggested Citation

  • Yu-Fei Gao & Lei Chen & Yu-Dong Cai & Kai-Yan Feng & Tao Huang & Yang Jiang, 2012. "Predicting Metabolic Pathways of Small Molecules and Enzymes Based on Interaction Information of Chemicals and Proteins," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-9, September.
    • Handle: RePEc:plo:pone00:0045944
    DOI: 10.1371/journal.pone.0045944
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    References listed on IDEAS

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    1. Lei Chen & Wei-Ming Zeng & Yu-Dong Cai & Kai-Yan Feng & Kuo-Chen Chou, 2012. "Predicting Anatomical Therapeutic Chemical (ATC) Classification of Drugs by Integrating Chemical-Chemical Interactions and Similarities," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-7, April.
    2. Yiannis A I Kourmpetis & Aalt D J van Dijk & Marco C A M Bink & Roeland C H J van Ham & Cajo J F ter Braak, 2010. "Bayesian Markov Random Field Analysis for Protein Function Prediction Based on Network Data," PLOS ONE, Public Library of Science, vol. 5(2), pages 1-10, February.
    3. Le-Le Hu & Tao Huang & Yu-Dong Cai & Kuo-Chen Chou, 2011. "Prediction of Body Fluids where Proteins are Secreted into Based on Protein Interaction Network," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-8, July.
    4. Lele Hu & Tao Huang & Xiaohe Shi & Wen-Cong Lu & Yu-Dong Cai & Kuo-Chen Chou, 2011. "Predicting Functions of Proteins in Mouse Based on Weighted Protein-Protein Interaction Network and Protein Hybrid Properties," PLOS ONE, Public Library of Science, vol. 6(1), pages 1-10, January.
    5. Kuo-Chen Chou & Hong-Bin Shen, 2010. "A New Method for Predicting the Subcellular Localization of Eukaryotic Proteins with Both Single and Multiple Sites: Euk-mPLoc 2.0," PLOS ONE, Public Library of Science, vol. 5(4), pages 1-9, April.
    6. Le-Le Hu & Chen Chen & Tao Huang & Yu-Dong Cai & Kuo-Chen Chou, 2011. "Predicting Biological Functions of Compounds Based on Chemical-Chemical Interactions," PLOS ONE, Public Library of Science, vol. 6(12), pages 1-9, December.
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