Artificial Intelligence Research Laboratory Bioinformatics and Computational Biology Program Computational Intelligence, Learning, and Discovery Program Department of Computer Science Rocky 2006 Acknowledgements : This work is supported in part by a grant from the National Institutes of Health (GM ) to Vasant Honavar & Drena Dobbs Glycosylation Site Prediction using Machine Learning Approaches Cornelia Caragea, Jivko Sinapov, Adrian Silvescu, Drena Dobbs and Vasant Honavar Biological Motivation Glycosylation is one of the most complex post-translational modifications (PTMs). It is the site-specific enzymatic addition of saccharides to proteins and lipids. Most proteins in eukaryotic cells undergo glycosylation. Types of Glycosylation M K LI TI L C F L S R L L P S L T Q E S S Q EID Non-Glycosylated? Glycosylated? N-linked?O-linked?C-linked? H3N+H3N+ COO - Problem: Predict glycosylation sites from amino acid sequence Previous Approaches Trained Neural Networks used in netOglyc prediction server (Hansen et al., 1995) Dataset: mucin type O-linked glycosylation sites in mammalian proteins Trained SVMs based on physical properties, 0/1 system and a combination of these two (Li et al., 2006) Dataset: mucin type O-linked glycosylation sites in mammalian proteins Negative examples extracted from sequences with no known glycosylated sites Trained/tested using different ratios of positive and negative sites Our Approach We investigate 3 types of glycosylation and use an ensemble classifier approach Dataset: N-, C- and O-linked glycoslation sites in proteins from several different species: human, rat, mouse, insect, worm, horse, etc. Negative examples extracted from sequences with at least one experimentally verified glycosylated site Dataset O-GlycBase v6.00: O-, N- & C- glycosylated proteins with 242 glycosylated entries available at Glycosylation Type Positive Sites Negative Sites O-Linked (S/T) N-Linked (N) C-Linked (W)4773 Total Train DB Sampling.... train.... Bag of Trained Classifiers Test DB Weighted Majority Vote Predictions train Training an ensemble classifier Classifiers SVM 0/1 String Kernel Substitution Matrix Kernel Blast - Polynomial Kernel J48 Naïve Bayes Identity windows Identity plus additional information C-mannosylation Glycosylation N-linked glycosylation GPI anchor N-acetylglucosamine (N-GlcNAc) O-N-acetylgalactosamine (O-GalNAc) O-N-acetylglucosamine (O-GlcNAc) O-fucose O-glucose O-mannose O-hexose O-xylose C-mannose O-linked glycosylation ROC Curves for N-Linked ROC Curves for O-Linked ROC Curves for C-Linked Comparison of ROC Curves for single and ensemble classifier Results Conclusion In this work we addressed the problem of predicting glycosylation sites. Three types of machine learning algorithms were used: SVM, NB, and DT. We built predictive ensemble classifiers based on data corresponding to three forms of glycosylation: O-, N-, and C-Linked glycosylation. Our experiments show encouraging results.