A computational approach for the classification of protein tyrosine kinases.
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.
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Article: Profile hidden Markov models.[show abstract] [hide abstract]
ABSTRACT: The recent literature on profile hidden Markov model (profile HMM) methods and software is reviewed. Profile HMMs turn a multiple sequence alignment into a position-specific scoring system suitable for searching databases for remotely homologous sequences. Profile HMM analyses complement standard pairwise comparison methods for large-scale sequence analysis. Several software implementations and two large libraries of profile HMMs of common protein domains are available. HMM methods performed comparably to threading methods in the CASP2 structure prediction exercise.Bioinformatics 02/1998; 14(9):755-63. · 5.32 Impact Factor
- Methods in Enzymology 02/1991; 200:38-62. · 2.00 Impact Factor
Conference Proceeding: Classifying G-protein Coupled Receptors with Support Vector Machine.[show abstract] [hide abstract]
ABSTRACT: G-protein coupled receptors (GPCRs) are a class of pharmacologically relevant transmembrane proteins with specific characteristics. They play a key role in different biological process and are very important for understanding human diseases. However, ligand specificity of many receptors remains unknown and only one crystal structure solved to date. It is highly desirable to predict receptor’s type using only sequence information. In this paper, Support Vector Machine is introduced to predict receptor’s type based on its amino acid composition. The prediction is performed to the amine-binding classes of the rhodopsin-like family. The overall predictive accuracy about 94% has been achieved in a ten-fold cross-validation.Advances in Neural Networks - ISNN 2004, International Symposium on Neural Networks, Dalian, China, August 19-21, 2004, Proceedings, Part II; 01/2004