Volume 11 Supplement 10

Highlights from the Sixth International Society for Computational Biology (ISCB) Student Council Symposium

Open Access

Structure-based kernels for the prediction of catalytic residues and their involvement in human inherited disease

  • Fuxiao Xin1,
  • Steven Myers1,
  • Yong Fuga Li1,
  • David N Cooper2,
  • Sean D Mooney3 and
  • Predrag Radivojac1Email author
BMC Bioinformatics201011(Suppl 10):O4

DOI: 10.1186/1471-2105-11-S10-O4

Published: 07 December 2010

Background

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.

Results

We proposed a new kernel-based algorithm for the prediction of catalytic residues and functional sites in general in protein structures [1]. The method relies upon explicit modelling of similarity between residue-centred neighbourhoods in protein structures. Specifically, we start with a construction of oriented structural neighbourhoods followed by separating the neighbourhood volume into small cells. The similarities between two structural neighbourhoods are accumulation of their similarity in each cell. The kernel function is a product of three kernels, each addressing a separate aspect of protein function: (i) the geometric kernel addresses the shape similarity, (ii) the chemical kernel addresses the similarity in physicochemical properties, and (iii) the evolutionary kernel addresses the evolutionary similarity of conservation patterns for the residues in two structural neighbourhoods. Our approach was favourably evaluated against two of the leading alternative approaches, FEATURE [2] and GBT [3], as shown in Table 1. The new algorithm was used to identify known mutations associated with inherited disease whose molecular mechanism might be predicted to operate specifically though the loss or gain of catalytic residues. It should therefore provide a viable approach in identifying the molecular basis of disease in which the loss or gain of function is not caused solely by the disruption of protein stability. Our analysis suggests that both loss and gain of catalytic residues are actively involved in human inherited disease.
Table 1

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.

 

FEATURE

GBT

Structure Kernel

 

AUC

sn

AUC

sn

AUC

sn

Fold

76.4

0.20

80.4

0.34

86.1

0.40

Superfamily

76.5

0.20

80.8

0.34

86.1

0.40

Family

76.7

0.21

80.7

0.34

86.8

0.42

Chain

76.7

0.21

81.1

0.34

87.3

0.45

Conclusions

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.

Authors’ Affiliations

(1)
School of Informatics and Computing, Indiana University
(2)
Institute of Medical Genetics, School of Medicine, Cardiff University
(3)
Buck Institute for Age Research

References

  1. 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
  2. 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
  3. 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

Copyright

© Xin et al; licensee BioMed Central Ltd. 2010

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.

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