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BioMed Research International
Volume 2013 (2013), Article ID 674215, 7 pages
http://dx.doi.org/10.1155/2013/674215
Research Article

Prediction of Substrate-Enzyme-Product Interaction Based on Molecular Descriptors and Physicochemical Properties

1Shanghai Key Laboratory of Bio-Energy Crops, School of Life Science, Shanghai University, 333 Nancheng Road, Shanghai 200444, China
2Institute of Systems Biology, Shanghai University, Shanghai, China
3Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Shanghai 200444, China
4Department of Radiology, First People’s Hospital, Shanghai Jiaotong University, Shanghai 200080, China
5Department of Neurosurgery, Changhai Hospital, Second Military Medical University, Shanghai 200433, China
6Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA

Received 2 November 2013; Accepted 30 November 2013

Academic Editor: Yudong Cai

Copyright © 2013 Bing Niu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. A. Papin, N. D. Price, S. J. Wiback, D. A. Fell, and B. O. Palsson, “Metabolic pathways in the post-genome era,” Trends in Biochemical Sciences, vol. 28, no. 5, pp. 250–258, 2003. View at Publisher · View at Google Scholar · View at Scopus
  2. A.-L. Barabási and Z. N. Oltvai, “Network biology: understanding the cell's functional organization,” Nature Reviews Genetics, vol. 5, no. 2, pp. 101–113, 2004. View at Publisher · View at Google Scholar · View at Scopus
  3. P. Reichard, “Ribonucleotide reductases: the evolution of allosteric regulation,” Archives of Biochemistry and Biophysics, vol. 397, no. 2, pp. 149–155, 2002. View at Publisher · View at Google Scholar · View at Scopus
  4. C. J. Huberty, Applied Discriminant Analysis, vol. 297, John Wiley & Sons, New York, NY, USA, 1994.
  5. E. Fix and J. L. Hodges, “Discriminatory analysis. Nonparametric discrimination: consistency properties,” USAF School of Aviation Medicine: Randolph Field, pp. 261-279, San Antonio, Tex, USA, 1951.
  6. R. A. Johnson and D. W. Wichern, Applied MultiVariate Statistical Analysis, Prentice Hall, Englewood Cliffs, NJ, USA, 5th edition, 1982.
  7. H. Peng, F. Long, and C. Ding, “Feature selection based on mutual information: criteria of Max-Dependency, Max-Relevance, and Min-Redundancy,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp. 1226–1238, 2005. View at Publisher · View at Google Scholar · View at Scopus
  8. B. Niu, L. Lu, L. Liu et al., “HIV-1 protease cleavage site prediction based on amino acid property,” Journal of Computational Chemistry, vol. 30, no. 1, pp. 33–39, 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. Y. Cai, J. He, X. Li et al., “Prediction of protein subcellular locations with feature selection and analysis,” Protein and Peptide Letters, vol. 17, no. 4, pp. 464–472, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. Y. Cai, Z. He, X. Shi, X. Kong, L. Gu, and L. Xie, “A novel sequence-based method of predicting protein DNA-binding residues, using a machine learning approach,” Molecules and Cells, vol. 30, no. 2, pp. 99–105, 2010. View at Publisher · View at Google Scholar · View at Scopus
  11. Y. Cai, T. Huang, L. Hu, X. Shi, L. Xie, and Y. Li, “Prediction of lysine ubiquitination with mRMR feature selection and analysis,” Amino Acids, vol. 42, no. 4, pp. 1387–1395, 2012. View at Publisher · View at Google Scholar · View at Scopus
  12. L. Chen, Z.-S. He, T. Huang, and Y.-D. Cai, “Using compound similarity and functional domain composition for prediction of drug-target interaction networks,” Medicinal Chemistry, vol. 6, no. 6, pp. 388–395, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. L.-L. Hu, S. Niu, T. Huang, K. Wang, X.-H. Shi, and Y.-D. Cai, “Prediction and analysis of protein hydroxyproline and hydroxylysine,” PLoS One, vol. 5, no. 12, Article ID e15917, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. B. Li, K. Feng, L. Chen, T. Huang, and Y. Cai, “Prediction of protein-protein interaction sites by random forest algorithm with mRMR and IFS,” PLoS One, vol. 7, Article ID e43927, 2012. View at Publisher · View at Google Scholar
  15. B.-Q. Li, L.-L. Hu, S. Niu, Y.-D. Cai, and K.-C. Chou, “Predict and analyze S-nitrosylation modification sites with the mRMR and IFS approaches,” Journal of Proteomics, vol. 75, no. 5, pp. 1654–1665, 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. B.-Q. Li, T. Huang, L. Liu, Y.-D. Cai, and K.-C. Chou, “Identification of colorectal cancer related genes with mrmr and shortest path in protein-protein interaction network,” PLoS One, vol. 7, no. 4, Article ID e33393, 2012. View at Publisher · View at Google Scholar · View at Scopus
  17. K. Lin, Z. Qian, L. Lu et al., “Predicting miRNA's target from primary structure by the nearest neighbor algorithm,” Molecular Diversity, vol. 14, no. 4, pp. 719–729, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. S. Niu, T. Huang, K. Feng, Y. Cai, and Y. Li, “Prediction of tyrosine sulfation with mRMR feature selection and analysis,” Journal of Proteome Research, vol. 9, no. 12, pp. 6490–6497, 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. Y. Yuan, X. Shi, X. Li et al., “Prediction of interactiveness of proteins and nucleic acids based on feature selections,” Molecular Diversity, vol. 14, no. 4, pp. 627–633, 2010. View at Publisher · View at Google Scholar · View at Scopus
  20. Y.-D. Cai and K.-C. Chou, “Predicting subcellular localization of proteins in a hybridization space,” Bioinformatics, vol. 20, no. 7, pp. 1151–1156, 2004. View at Publisher · View at Google Scholar · View at Scopus
  21. Y.-D. Cai and A. J. Doig, “Prediction of Saccharomyces cerevisiae protein functional class from functional domain composition,” Bioinformatics, vol. 20, no. 8, pp. 1292–1300, 2004. View at Publisher · View at Google Scholar · View at Scopus
  22. K.-C. Chou and Y.-D. Cai, “Predicting protein-protein interactions from sequences in a hybridization space,” Journal of Proteome Research, vol. 5, no. 2, pp. 316–322, 2006. View at Publisher · View at Google Scholar · View at Scopus
  23. Z. Qian, Y.-D. Cai, and Y. Li, “A novel computational method to predict transcription factor DNA binding preference,” Biochemical and Biophysical Research Communications, vol. 348, no. 3, pp. 1034–1037, 2006. View at Publisher · View at Google Scholar · View at Scopus
  24. J. Song, “Prediction of homo-oligomeric proteins based on nearest neighbour algorithm,” Computers in Biology and Medicine, vol. 37, no. 12, pp. 1759–1764, 2007. View at Publisher · View at Google Scholar · View at Scopus
  25. T. M. Cover and P. E. Hart, “Nearst neighbor pattem classlfication,” IEEE Transactions on Information Theory, vol. 13, no. 1, pp. 21–27, 1967. View at Publisher · View at Google Scholar
  26. J. H. Friedman, F. Baskett, and L. J. Shustek, “An algorithm for finding nearest neighbors,” IEEE Transactions on Computers, vol. C-24, no. 10, pp. 1000–1006, 1975. View at Publisher · View at Google Scholar · View at Scopus
  27. S. Goto, T. Nishioka, and M. Kanehisa, “LIGAND: chemical database for enzyme reactions,” Bioinformatics, vol. 14, no. 7, pp. 591–599, 1998. View at Publisher · View at Google Scholar · View at Scopus
  28. B. Niu, L. Gu, C. R. Peng, J. Ding, X. C. Yuan, and W. C. Lu, “Small molecules' multi-metabolic pathways prediction using physico-chemical features and multi-task learning method,” Current Bioinformatics, vol. 8, no. 5, pp. 564–568, 2013. View at Publisher · View at Google Scholar
  29. C.-R. Peng, W.-C. Lu, B. Niu, M.-J. Li, X.-Y. Yang, and M.-L. Wu, “Predicting the metabolic pathways of small molecules based on their physicochemical properties,” Protein & Peptide Letters, vol. 19, no. 12, pp. 1250–1256, 2012. View at Publisher · View at Google Scholar
  30. C.-R. Peng, W.-C. Lu, B. Niu, Y.-J. Li, and L.-L. Hu, “Prediction of the functional roles of small molecules in lipid metabolism based on ensemble learning,” Protein & Peptide Letters, vol. 19, no. 1, pp. 108–112, 2012. View at Publisher · View at Google Scholar · View at Scopus
  31. L. Weber, “JChem Base—ChemAxon,” Chemistry World-Uk, vol. 5, no. 10, pp. 65–66, 2008. View at Google Scholar
  32. F. Csizmadia, “JChem: java applets and modules supporting chemical database handling from web browsers,” Journal of Chemical Information and Computer Sciences, vol. 40, no. 2, pp. 323–324, 2000. View at Publisher · View at Google Scholar
  33. I. Dubchak, I. Muchnik, C. Mayor, I. Dralyuk, and S.-H. Kim, “Recognition of a protein fold in the context of the SCOP classification,” Proteins: Structure, Function and Genetics, vol. 35, no. 4, pp. 401–407, 1999. View at Publisher · View at Google Scholar
  34. C. Chothia and A. V. Finkelstein, “The classification and origins of protein folding patterns,” Annual Review of Biochemistry, vol. 59, pp. 1007–1039, 1990. View at Publisher · View at Google Scholar · View at Scopus
  35. D. Frishman and P. Argos, “Seventy-five percent accuracy in protein secondary structure prediction,” Proteins: Structure, Function and Genetics, vol. 27, no. 3, pp. 329–335, 1997. View at Publisher · View at Google Scholar
  36. M. H. Mucchielli-Giorgi, S. Hazout, and P. Tufféry, “PredAcc: prediction of solvent accessibility,” Bioinformatics, vol. 15, no. 2, pp. 176–177, 1999. View at Publisher · View at Google Scholar · View at Scopus
  37. I. Dubchak, I. Muchnik, S. R. Holbrook, and S.-H. Kim, “Prediction of protein folding class using global description of amino acid sequence,” Proceedings of the National Academy of Sciences of the United States of America, vol. 92, no. 19, pp. 8700–8704, 1995. View at Publisher · View at Google Scholar · View at Scopus
  38. I. Dubchak, I. Muchnik, C. Mayor, I. Dralyuk, and S. H. Kim, “Recognition of a protein fold in the context of the structural classification of proteins (SCOP) classification,” Proteins, vol. 35, no. 4, pp. 401–407, 1999. View at Publisher · View at Google Scholar
  39. B. Niu, Y. Jin, L. Lu et al., “Prediction of interaction between small molecule and enzyme using AdaBoost,” Molecular Diversity, vol. 13, no. 3, pp. 313–320, 2009. View at Publisher · View at Google Scholar · View at Scopus
  40. Z. He, J. Zhang, X.-H. Shi et al., “Predicting drug-target interaction networks based on functional groups and biological features,” PLoS One, vol. 5, no. 3, Article ID e9603, 2010. View at Publisher · View at Google Scholar · View at Scopus
  41. L. Hu, T. Huang, X. Shi, W.-C. Lu, Y.-D. Cai, and K.-C. Chou, “Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties,” PLoS One, vol. 6, no. 1, Article ID e14556, 2011. View at Publisher · View at Google Scholar · View at Scopus
  42. T. Huang, X.-H. Shi, P. Wang et al., “Analysis and prediction of the metabolic stability of proteins based on their sequential features, subcellular locations and interaction networks,” PloS One, vol. 5, no. 6, Article ID e10972, 2010. View at Publisher · View at Google Scholar · View at Scopus
  43. P. Wang, L. Hu, G. Liu et al., “Prediction of antimicrobial peptides based on sequence alignment and feature selection methods,” PLoS One, vol. 6, no. 4, Article ID e18476, 2011. View at Publisher · View at Google Scholar · View at Scopus
  44. L. Chen, K.-Y. Feng, Y.-D. Cai, K.-C. Chou, and H.-P. Li, “Predicting the network of substrate-enzyme-product triads by combining compound similarity and functional domain composition,” BMC Bioinformatics, vol. 11, article 293, 2010. View at Publisher · View at Google Scholar · View at Scopus
  45. L. Chen, T. Huang, X.-H. Shi, Y.-D. Cai, and K.-C. Chou, “Analysis of protein pathway networks using hybrid properties,” Molecules, vol. 15, no. 11, pp. 8177–8192, 2010. View at Publisher · View at Google Scholar · View at Scopus
  46. K. C. Chou and H.-B. Shen, “Review: recent advances in developing web-servers for predicting protein attributes,” Natural Science, vol. 1, no. 2, pp. 63–92, 2009. View at Publisher · View at Google Scholar
  47. T. E. Creighton, Proteins: Structures and Molecular Properties, W. H. Freeman, New York, NY, USA, 2nd edition, 1993.
  48. G. E. Tusnády and I. Simon, “Principles governing amino acid composition of integral membrane proteins: application to topology prediction,” Journal of Molecular Biology, vol. 283, no. 2, pp. 489–506, 1998. View at Publisher · View at Google Scholar · View at Scopus
  49. Y. Freund, Y. Mansour, and R. E. Schapire, “Generalization bounds for averaged classifiers,” Annals of Statistics, vol. 32, no. 4, pp. 1698–1722, 2004. View at Publisher · View at Google Scholar · View at Scopus
  50. Y. Freund and R. E. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting,” Journal of Computer and System Sciences, vol. 55, no. 1, pp. 119–139, 1997. View at Publisher · View at Google Scholar · View at Scopus
  51. R. E. Schapire, Y. Freund, P. Bartlett, and W. S. Lee, “Boosting the margin: a new explanation for the effectiveness of voting methods,” Annals of Statistics, vol. 26, no. 5, pp. 1651–1686, 1998. View at Publisher · View at Google Scholar · View at Scopus
  52. R. E. Schapire and Y. Singer, “Improved boosting algorithms using confidence-rated predictions,” Machine Learning, vol. 37, no. 3, pp. 297–336, 1999. View at Publisher · View at Google Scholar · View at Scopus
  53. I. A. Yudushkin, A. Schleifenbaum, A. Kinkhabwala, B. G. Neel, C. Schultz, and P. I. H. Bastiaens, “Live-cell imaging of enzyme-substrate interaction reveals spatial regulation of PTP1B,” Science, vol. 315, no. 5808, pp. 115–119, 2007. View at Publisher · View at Google Scholar · View at Scopus
  54. M. J. Ondrechen, J. M. Briggs, and J. A. McCammon, “A model for enzyme-substrate interaction in alanine racemase,” Journal of the American Chemical Society, vol. 123, no. 12, pp. 2830–2834, 2001. View at Publisher · View at Google Scholar · View at Scopus
  55. C. Sadasivan and V. C. Yee, “Interaction of the factor XIII activation peptide with α-thrombin. Crystal structure of its enzyme-substrate analog complex,” Journal of Biological Chemistry, vol. 275, no. 47, pp. 36942–36948, 2000. View at Publisher · View at Google Scholar · View at Scopus