Vector-G: Multi-Modular SVM-Based Heterotrimeric G Protein Prediction

Heterotrimeric G proteins interact with G protein-coupled receptors in response to stimulation by hormones, neurotransmitters, chemokines, and sensory signals to intracellular signaling cascades. Recently reported studies indicate that G protein subunits play a significant role in different eukaryotic diseases including inflammation, neurological diseases, cardiovascular diseases, endocrine disorders as well as plant pathogen response, infectious hyphae growth, differentiation and virulence of pathogenic fungi. Thus a study of their functions, signaling pathways, and protein interactions may lead to the development of various preventive approaches. The diversity of alpha, beta and gamma subunits of G proteins necessitates a prediction algorithm that helps in the identification of new proteins such as Gbeta where WD-40 repeats are not well characterized. The currently available techniques for finding G proteins are homology based search analyses and wet lab experiments, which are not very effective in finding new classes of proteins. We present here a robust computational method for finding new G proteins and their homologs using a SVM based pattern recognition algorithm. Several physicochemical and compositional properties including dipeptide, tripeptide and hydrophobicity composition are used for generating the SVM classifiers. This method has 96.17%, 95.38%, 97.6% sensitivity and 99.45%, 100%, 100% specificity on test sets for G protein alpha, beta, and gamma subunits, respectively. This algorithm correctly predicts the known alpha, beta and gamma subunits reported in literature. One important contribution of this algorithm is that it helps in improving genome annotation of several proteins as G proteins and serves as a useful tool for comparative genomic analysis of G proteins. Using this method, novel G protein subunits are predicted in 31 genomes covering plant, fungi and animal kingdom. The software is available at the website http://biomine.cs.uah.edu/bioinformatics/svm_prog/scripts/GProteins/vectorg.html. Supplementary files: The supplementary files are available on http://www.bioinfo.de/isb/2008/08/0013/supplementary_ material/.

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