An improved prediction of catalytic residues in enzyme structures

The protein databases contain a huge number of function unknown proteins, including many proteins with newly determined 3D structures resulted from the Structural Genomics Projects. To accelerate experiment-based assignment of function, de novo prediction of protein functional sites, like active sit...

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Bibliographic Details
Published in:Protein engineering, design and selection design and selection, 2008-05, Vol.21 (5), p.295-302
Main Authors: Tang, Yu-Rong, Sheng, Zhi-Ya, Chen, Yong-Zi, Zhang, Ziding
Format: Article
Language:English
Subjects:
Algorithms
Binding Sites
Catalysis
catalytic residues
closeness centrality
Computational Biology - methods
Databases, Protein
Enzymes - chemistry
genetic algorithm
Humans
Hydrogen Bonding
Models, Genetic
Models, Molecular
Models, Statistical
Molecular Conformation
neural network
Neural Networks (Computer)
prediction
Protein Conformation
Sequence Analysis, Protein
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Summary:The protein databases contain a huge number of function unknown proteins, including many proteins with newly determined 3D structures resulted from the Structural Genomics Projects. To accelerate experiment-based assignment of function, de novo prediction of protein functional sites, like active sites in enzymes, becomes increasingly important. Here, we attempted to improve the prediction of catalytic residues in enzyme structures by seeking and refining different encodings (i.e. residue properties) as well as employing new machine learning algorithms. In particular, considering that catalytic residues can often reveal specific network centrality when representing enzyme structure as a residue contact network, the corresponding measurement (i.e. closeness centrality) was used as one of the most important encodings in our new predictor. Meanwhile, a genetic algorithm integrated neural network (GANN) was also employed. Thanks to the above strategies, our GANN predictor demonstrated a high accuracy of 91.2% in the prediction of catalytic residues based on balanced datasets (i.e. the 1:1 ratio of catalytic to non-catalytic residues). When the GANN method was optimally applied to real enzyme structures, 73.9% of the tested structures had the active site correctly located. Compared with two existing methods, the proposed GANN method also demonstrated a better performance.
ISSN:1741-0126
1741-0134
DOI:10.1093/protein/gzn003