- 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
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.
- H. M. Berman, J. Westbrook, Z. Feng et al., “The protein data bank,” Nucleic Acids Research, vol. 28, no. 1, pp. 235–242, 2000.
- J. A. Capra and M. Singh, “Predicting functionally important residues from sequence conservation,” Bioinformatics, vol. 23, no. 15, pp. 1875–1882, 2007.
- D. La, B. Sutch, and D. R. Livesay, “Predicting protein functional sites with phylogenetic motifs,” Proteins, vol. 58, no. 2, pp. 309–320, 2005.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- S. Hua and Z. Sun, “Support vector machine approach for protein subcellular localization prediction,” Bioinformatics, vol. 17, no. 8, pp. 721–728, 2001.
- 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.
- 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.
- 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.
- Y. C. Chen and J. K. Hwang, “Prediction of disulfide connectivity from protein sequences,” Proteins, vol. 61, no. 3, pp. 507–512, 2005.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- E. Cilia and A. Passerini, “Automatic prediction of catalytic residues by modeling residue structural neighborhood,” BMC Bioinformatics, vol. 11, article 115, 2010.