- Oral presentation
- Open Access
Structure-based kernels for the prediction of catalytic residues and their involvement in human inherited disease
© Xin et al; licensee BioMed Central Ltd. 2010
- Published: 07 December 2010
- Structural Neighbourhood
- Kernel Method
- Enzyme Catalysis
- Catalytic Residue
- Functional Site
Enzyme catalysis is involved in numerous biological processes and the disruption of enzymatic activity has been implicated in human disease. Despite the functional importance, various aspects of catalytic reactions are not completely understood, such as the mechanics of reaction chemistry and the geometry of catalytic residues within active sites. As a result, the computational prediction of catalytic residues has the potential to identify novel catalytic pockets, aid in the design of more efficient enzymes and also predict the molecular basis of disease.
Performance comparison between the three methods of catalytic residue prediction when evaluation was carried out by chain, family, superfamily and fold. Methods were evaluated on the same data set using 10-fold cross-validation. sn means the sensitivity when specificity is 0.95.
Our kernel method for functional sites prediction based on protein structures evaluates favourably against established methods on the same data set using the same evaluation procedure. The results from applying our catalytic residue predictor to disease mutations indicated that both loss and gain of catalytic residues are actively involved in human inherited disease.
- Xin F, Myers S, Li Y, Cooper D, Mooney S, Radivojac P: Structure-based kernels for the prediction of catalytic residues and their involvement in human inherited disease. Bioinformatics 2010, 26(16):1975–1982. 10.1093/bioinformatics/btq319PubMed CentralView ArticlePubMedGoogle Scholar
- Wu S, Liang MP, Altman RB: The SeqFEATURE library of 3D functional site models: comparison to existing methods and applications to protein function annotation. Genome Biol 2008, 9: R8. 10.1186/gb-2008-9-1-r8PubMed CentralView ArticlePubMedGoogle Scholar
- Gutteridge A, Bartlett GJ, Thornton JM: Using a neural network and spatial clustering to predict the location of active sites in enzymes. J Mol Biol 2003, 330(4):719–34. 10.1016/S0022-2836(03)00515-1View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.