Journal Menu
- About this Journal
- Abstracting and Indexing
- Aims and Scope
- Annual Issues
- Article Processing Charges
- Articles in Press
- Author Guidelines
- Bibliographic Information
- Citations to this Journal
- Contact Information
- Editorial Board
- Editorial Workflow
- Free eTOC Alerts
- Publication Ethics
- Reviewers Acknowledgment
- Submit a Manuscript
- Subscription Information
- Table of Contents
BioMed Research International
Volume 2013 (2013), Article ID 802945, 9 pages
http://dx.doi.org/10.1155/2013/802945
Research Article
On the Structural Context and Identification of Enzyme Catalytic Residues
Department of Medical Informatics, Tzu Chi University, 701 Zhongyang Road, Section 3, Hualien 97004, Taiwan
Received 29 November 2012; Accepted 28 December 2012
Academic Editor: Tun-Wen Pai
Copyright © 2013 Yu-Tung Chien and Shao-Wei Huang. 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
- 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 Scopus
- J. A. Capra and M. Singh, “Predicting functionally important residues from sequence conservation,” Bioinformatics, vol. 23, no. 15, pp. 1875–1882, 2007. View at Publisher · View at Google Scholar · View at Scopus
- D. La, B. Sutch, and D. R. Livesay, “Predicting protein functional sites with phylogenetic motifs,” Proteins, vol. 58, no. 2, pp. 309–320, 2005. View at Publisher · View at Google Scholar · View at Scopus
- M. Ota, K. Kinoshita, and K. Nishikawa, “Prediction of catalytic residues in enzymes based on known tertiary structure, stability profile, and sequence conservation,” Journal of Molecular Biology, vol. 327, no. 5, pp. 1053–1064, 2003. View at Publisher · View at Google Scholar · View at Scopus
- B. Sterner, R. Singh, and B. Berger, “Predicting and annotating catalytic residues: an information theoretic approach,” Journal of Computational Biology, vol. 14, no. 8, pp. 1058–1073, 2007. View at Publisher · View at Google Scholar · View at Scopus
- J. W. Torrance, G. J. Bartlett, C. T. Porter, and J. M. Thornton, “Using a library of structural templates to recognise catalytic sites and explore their evolution in homologous families,” Journal of Molecular Biology, vol. 347, no. 3, pp. 565–581, 2005. View at Publisher · View at Google Scholar · View at Scopus
- N. Nagano, C. A. Orengo, and J. M. Thornton, “One fold with many functions: the evolutionary relationships between TIM barrel families based on their sequences, structures and functions,” Journal of Molecular Biology, vol. 321, no. 5, pp. 741–765, 2002. View at Publisher · View at Google Scholar · View at Scopus
- A. C. Wallace, R. A. Laskowski, and J. M. Thornton, “Derivation of 3D coordinate templates for searching structural databases: application to Ser-His-Asp catalytic triads in the serine proteinases and lipases,” Protein Science, vol. 5, no. 6, pp. 1001–1013, 1996. View at Scopus
- S. Sacquin-Mora, E. Laforet, and R. Lavery, “Locating the active sites of enzymes using mechanical properties,” Proteins, vol. 67, no. 2, pp. 350–359, 2007. View at Publisher · View at Google Scholar · View at Scopus
- A. Ben-Shimon and M. Eisenstein, “Looking at enzymes from the inside out: the proximity of catalytic residues to the molecular centroid can be used for detection of active sites and enzyme-ligand interfaces,” Journal of Molecular Biology, vol. 351, no. 2, pp. 309–326, 2005. View at Publisher · View at Google Scholar · View at Scopus
- G. Amitai, A. Shemesh, E. Sitbon et al., “Network analysis of protein structures identifies functional residues,” Journal of Molecular Biology, vol. 344, no. 4, pp. 1135–1146, 2004. View at Publisher · View at Google Scholar · View at Scopus
- Y. Wei, J. Ko, L. F. Murga, and M. J. Ondrechen, “Selective prediction of interaction sites in protein structures with THEMATICS,” BMC Bioinformatics, vol. 8, article 119, 2007. View at Publisher · View at Google Scholar · View at Scopus
- W. Tong, Y. Wei, L. F. Murga, M. J. Ondrechen, and R. J. Williams, “Partial Order Optimum Likelihood (POOL): maximum likelihood prediction of protein active site residues using 3D structure and sequence properties,” PLoS Computational Biology, vol. 5, no. 1, Article ID e1000266, 2009. View at Publisher · View at Google Scholar · View at Scopus
- Y. T. Chien and S. W. Huang, “Accurate prediction of protein catalytic residues by side chain orientation and residue contact density,” PLoS ONE, vol. 7, Article ID e47951, 2012.
- S. W. Huang, C. H. Shih, C. P. Lin, and J. K. Hwang, “Prediction of NMR order parameters in proteins using weighted protein contact-number model,” Theoretical Chemistry Accounts, vol. 121, no. 3-4, pp. 197–200, 2008. View at Publisher · View at Google Scholar · View at Scopus
- C. P. Lin, S. W. Huang, Y. L. Lai et al., “Deriving protein dynamical properties from weighted protein contact number,” Proteins, vol. 72, no. 3, pp. 929–935, 2008. View at Publisher · View at Google Scholar · View at Scopus
- Y. T. Chien and S. W. Huang, “Prediction of protein catalytic residues by local structural rigidity,” in Proceedings of the 6th International Conference on Complex, Intelligent and Software Intensive Systems (CISIS '12), pp. 592–596, Palermo, Italy, 2012.
- C. H. Q. Ding and I. Dubchak, “Multi-class protein fold recognition using support vector machines and neural networks,” Bioinformatics, vol. 17, no. 4, pp. 349–358, 2001. View at Scopus
- C. S. Yu, J. Y. Wang, J. M. Yang, P. C. Lyu, C. J. Lin, and J. K. Hwang, “Fine-grained protein fold assignment by support vector machines using generalized npeptide coding schemes and jury voting from multiple-parameter sets,” Proteins, vol. 50, no. 4, pp. 531–536, 2003. View at Publisher · View at Google Scholar · View at Scopus
- C. S. Yu, C. J. Lin, and J. K. Hwang, “Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions,” Protein Science, vol. 13, no. 5, pp. 1402–1406, 2004. View at Publisher · View at Google Scholar · View at Scopus
- S. Hua and Z. Sun, “Support vector machine approach for protein subcellular localization prediction,” Bioinformatics, vol. 17, no. 8, pp. 721–728, 2001. View at Scopus
- S. Hua and Z. Sun, “A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach,” Journal of Molecular Biology, vol. 308, no. 2, pp. 397–407, 2001. View at Publisher · View at Google Scholar · View at Scopus
- H. Kim and H. Park, “Protein secondary structure prediction based on an improved support vector machines approach,” Protein Engineering, vol. 16, no. 8, pp. 553–560, 2003. View at Scopus
- B. Rost and C. Sander, “Prediction of protein secondary structure at better than 70% accuracy,” Journal of Molecular Biology, vol. 232, no. 2, pp. 584–599, 1993. View at Publisher · View at Google Scholar · View at Scopus
- Y. C. Chen and J. K. Hwang, “Prediction of disulfide connectivity from protein sequences,” Proteins, vol. 61, no. 3, pp. 507–512, 2005. View at Publisher · View at Google Scholar · View at Scopus
- Y. C. Chen, Y. S. Lin, C. J. Lin, and J. K. Hwang, “Prediction of the bonding states of cysteines using the support vector machines based on multiple feature vectors and cysteine state sequences,” Proteins, vol. 55, no. 4, pp. 1036–1042, 2004. View at Publisher · View at Google Scholar · View at Scopus
- S. W. Huang and J. K. Hwang, “Computation of conformational entropy from protein sequences using the machine-learning method—application to the study of the relationship between structural conservation and local structural stability,” Proteins, vol. 59, no. 4, pp. 802–809, 2005. View at Publisher · View at Google Scholar · View at Scopus
- H. Kim and H. Park, “Prediction of protein relative solvent accessibility with support vector machines and long-range interaction 3D local descriptor,” Proteins, vol. 54, no. 3, pp. 557–562, 2004. View at Publisher · View at Google Scholar · View at Scopus
- C. C. Chang and C. J. Lin, LIBSVM: a library for support vector machines, 2001, http://www.csie.ntu.edu.tw/~cjlin/libsvm.
- Y. R. Tang, Z. Y. Sheng, Y. Z. Chen, and Z. Zhang, “An improved prediction of catalytic residues in enzyme structures,” Protein Engineering, Design and Selection, vol. 21, no. 5, pp. 295–302, 2008. View at Publisher · View at Google Scholar · View at Scopus
- S. F. Altschul, T. L. Madden, A. A. Schäffer et al., “Gapped BLAST and PSI-BLAST: a new generation of protein database search programs,” Nucleic Acids Research, vol. 25, no. 17, pp. 3389–3402, 1997. View at Publisher · View at Google Scholar · View at Scopus
- C. T. Porter, G. J. Bartlett, and J. M. Thornton, “The Catalytic Site Atlas: a resource of catalytic sites and residues identified in enzymes using structural data,” Nucleic Acids Research, vol. 32, pp. D129–D133, 2004. View at Scopus
- W. Kabsch and C. Sander, “Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features,” Biopolymers, vol. 22, no. 12, pp. 2577–2637, 1983. View at Scopus
- N. V. Petrova and C. H. Wu, “Prediction of catalytic residues using Support Vector Machine with selected protein sequence and structural properties,” BMC Bioinformatics, vol. 7, article 312, 2006. View at Publisher · View at Google Scholar · View at Scopus
- E. Cilia and A. Passerini, “Automatic prediction of catalytic residues by modeling residue structural neighborhood,” BMC Bioinformatics, vol. 11, article 115, 2010. View at Publisher · View at Google Scholar · View at Scopus