Table of Contents Author Guidelines Submit a Manuscript
Advances in Bioinformatics
Volume 2011, Article ID 958129, 9 pages
Research Article

Prediction of Enzyme Mutant Activity Using Computational Mutagenesis and Incremental Transduction

Department of Computer Science, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA

Received 8 May 2011; Revised 27 June 2011; Accepted 4 August 2011

Academic Editor: Sandor Vajda

Copyright © 2011 Nada Basit and Harry Wechsler. 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. H. Lodish, Molecular Cell Biology, W.H. Freeman, New York, NY, USA, 5th edition, 2004.
  2. T. H. Creighton, Proteins: Structures and Molecular Properties, W.H. Freeman, San Francisco, Calif, USA, 1993.
  3. J. Pevsner, Bioinformatics and Functional Genomics, Wiley-Blackwell, Hoboken, NJ, USA, 2nd edition, 2009.
  4. A. Z. Machalek, Inside the Cell, U.S. Department of Health and Human Services, 2007,
  5. M. Masso and I. Vaisman, “Accurate prediction of enzyme mutant activity based on a multibody statistical potential,” Bioinformatics, vol. 23, no. 23, pp. 3155–3161, 2007. View at Publisher · View at Google Scholar · View at Scopus
  6. M. Masso and I. Vaisman, “Accurate prediction of stability changes in protein mutants by combining machine learning with structure based computational mutagenesis,” Bioinformatics, vol. 24, no. 18, pp. 2002–2009, 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. H. M. Berman, J. Westbrook, Z. Feng et al., “The protein data bank,” Nucleic Acids Research, vol. 28, no. 1, pp. 235–242, 2000. View at Google Scholar · View at Scopus
  8. O. S. Platt, D. J. Brambilla, W. F. Rosse et al., “Mortality in sickle cell disease. Life expectancy and risk factors for early death,” The New England Journal of Medicine, vol. 330, no. 23, pp. 1639–1644, 1994. View at Publisher · View at Google Scholar · View at Scopus
  9. D. R. Bloch, Organic Chemistry Demystified, McGraw-Hill, New York, NY, USA, 2006.
  10. D. L. Nelson and M. M. Cox, Lehninger's Principles of Biochemistry, W.H. Freeman, New York, NY, USA, 4th edition, 2005.
  11. “The Twenty Amino Acids,” Birkbeck University, London, UK, 2010,
  12. I. Vaisman, A. Tropsha, and W. Zheng, “Compositional preferences in quadruplets of nearest neighbor residues in protein structures: statistical geometry analysis,” in Proceedings of the IEEE Symposium on Intelligent Systems, pp. 163–168, 1998.
  13. M. Masso, K. Hijazi, N. Parvez, and I. Vaisman, “Computational mutagenesis of E. coli lac repressor: insight into structure-function relationships and accurate prediction of mutant activity,” in Lecture Notes in Bioinformatics, I. Mandoiu, R. Sunderraman, and A. Zelikovsky, Eds., vol. 4983, pp. 390–401, Springer, Berlin, Germany, 2008. View at Google Scholar
  14. R. K. Singh, A. Tropsha, and I. Vaisman, “Delaunay tessellation of proteins: four body nearest-neighbor propensities of amino acid residues,” Journal of Computational Biology, vol. 3, no. 2, pp. 213–221, 1996. View at Google Scholar · View at Scopus
  15. C. B. Barber, D. P. Dobkin, and H. Huhdanpaa, “The quickhull algorithm for convex hulls,” ACM Transactions on Mathematical Software, vol. 22, no. 4, pp. 469–483, 1996. View at Google Scholar · View at Scopus
  16. I. Vaisman, “Statistical and computational geometry of biomolecular structure,” in Handbook of Computational Statistics, J. E. Gentle, W. Härdle, and Y. Mori, Eds., Springer, Berlin, Germany, 2004. View at Google Scholar
  17. M. Masso and I. Vaisman, “Comprehensive mutagenesis of HIV-1 protease: a computational geometry approach,” Biochemical and Biophysical Research Communications, vol. 305, no. 2, pp. 322–326, 2003. View at Publisher · View at Google Scholar · View at Scopus
  18. M. Masso, “Knowledge-based study of protein structure-function correlations using computational geometry,” in Proceedings of the IEEE International Conference on Bioinformatics & Biomedicine (BIBM '09) Tutorial, George Mason University, Washington, DC, USA, 2009.
  19. V. Cherkassky and F. Mulier, Learning From Data Concepts, Theory, and Methods, John Wiley & Sons, New York, NY, USA, 2nd edition, 2007.
  20. X. Zhu, “Semi-supervised learning literature survey,” 2005, View at Google Scholar
  21. V. Vapnik, Estimation of Dependencies Based on Empirical Data, Springer, New York, NY, USA, 1982.
  22. V. Vapnik, Statistical Learning Theory, John Wiley & Sons, New York, NY, USA, 1998.
  23. O. Chapelle, B. Schölkopf, and A. Zien, Semi-Supervised Learning, MIT Press, 2006.
  24. T. M. Cover and P. Hart, “Nearest neighbor pattern classification,” IEEE Transactions on Information Theory, vol. 13, pp. 21–27, 1967. View at Google Scholar
  25. R. El-Yaniv and L. Gerzon, “Effective transductive learning via objective model selection,” Pattern Recognition Letters, vol. 26, no. 13, pp. 2104–2115, 2005. View at Publisher · View at Google Scholar · View at Scopus
  26. M. Masso, Z. Lu, and I. Vaisman, “Computational mutagenesis studies of protein structure-function correlations,” Proteins, vol. 64, no. 1, pp. 234–245, 2006. View at Publisher · View at Google Scholar · View at Scopus
  27. P. Tan, M. Seinbach, and V. Kumar, Introduction to Data Mining, Addison-Wesley, 2006.
  28. S. Russell and P. Norvig, Artificial Intelligence—A Modern Approach, Prentice Hall, New York, NY, USA, 3rd edition, 2010.
  29. 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 Google Scholar · View at Scopus
  30. J. Friedman, T. Hastie, and R. Tibshirani, “Additive logistic regression: a statistical view of boosting,” Annals of Statistics, vol. 28, no. 2, pp. 337–407, 2000. View at Google Scholar · View at Scopus
  31. B. Boser, I. Guyon, and V. Vapnik, “A training algorithm for optimal margin classifiers,” Computational Learning Theory, vol. 5, pp. 144–152, 1992. View at Google Scholar
  32. J. Weston, F. Pérez-Cruz, O. Bousquet, O. Chapelle, A. Elisseeff, and B. Schölkopf, “Feature selection and transduction for prediction of molecular bioactivity for drug design,” Bioinformatics, vol. 19, no. 6, pp. 764–771, 2003. View at Publisher · View at Google Scholar · View at Scopus
  33. MATLAB version 6.5.0 / 7.10.0,
  34. WEKA version 3.7.1,