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http://dx.doi.org/10.1007/s10059-009-0122-z

A Computational Approach for the Classification of Protein Tyrosine Kinases  

Park, Hyun-Chul (Program in Bioinformatics, Seoul National University)
Eo, Hae-Seok (School of Computational Sciences, Korea Institute for Advanced Study)
Kim, Won (Program in Bioinformatics, Seoul National University)
Publication Information
Molecules and Cells / v.28, no.3, 2009 , pp. 195-200 More about this Journal
Abstract
Protein tyrosine kinases (PTKs) play a central role in the modulation of a wide variety of cellular events such as differentiation, proliferation and metabolism, and their unregulated activation can lead to various diseases including cancer and diabetes. PTKs represent a diverse family of proteins including both receptor tyrosine kinases (RTKs) and non-receptor tyrosine kinases (NRTKs). Due to the diversity and important cellular roles of PTKs, accurate classification methods are required to better understand and differentiate different PTKs. In addition, PTKs have become important targets for drugs, providing a further need to develop novel methods to accurately classify this set of important biological molecules. Here, we introduce a novel statistical model for the classification of PTKs that is based on their structural features. The approach allows for both the recognition of PTKs and the classification of RTKs into their subfamilies. This novel approach had an overall accuracy of 98.5% for the identification of PTKs, and 99.3% for the classification of RTKs.
Keywords
classification; motifs and transmembrane domain; profile hidden Markov model; protein tyrosine kinase; receptor tyrosine kinase;
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