PEDS Advance Access published online on February 20, 2008
Protein Engineering Design and Selection, doi:10.1093/protein/gzn003
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An improved prediction of catalytic residues in enzyme structures
Bioinformatics Center, College of Biological Sciences, China Agricultural University, Beijing 100094, China
3 To whom correspondence should be addressed. E-mail: zidingzhang{at}cau.edu.cn
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.
Keywords: catalytic residues/closeness centrality/genetic algorithm/neural network/prediction
Received October 15, 2007; revised January 4, 2008; accepted January 4, 2008.
1 Both authors contributed equally to this work.
2 Present address: National Institute of Biological Sciences, No. 7 Science Park Road, Beijing 102206, China
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