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Artificial Intelligence Research LaboratoryBioinformatics and Computational Biology ProgramComputational Intelligence, Learning, and Discovery ProgramDepartment of Computer Science
RECOMB 2007
Acknowledgements: This work is supported in part by a grant from the National Institutes of Health (GM 066387) to Vasant Honavar & Drena Dobbs
Glycosylation Site Prediction using Machine Learning Approaches Cornelia Caragea, Jivko Sinapov, Adrian Silvescu, Drena Dobbs and Vasant Honavar
Biological MotivationGlycosylation 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 L I T I L
C
F
LSR
LLPSL
T
QE S
S Q E I D
Non O-Glycosylated?O-Glycosylated?
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
DatasetO-GlycBase v6.00: O- , N- & C- glycosylated proteins with 242 glycosylated entries available at http://www.cbs.dtu.dk/databases/OGLYCBASE/Oglyc.base.html
Glycosylation Type
Positive Sites
Negative Sites
O-Linked (S/T)
2098 11623
N-Linked (N)
251 1430
C-Linked (W)
47 73
Total 2366 13126
Train DBSampling
. . . .
S1 S2 S3 Sk
C1
train
C2 C3 Ck . . . .
Bag of Trained Classifiers
Test DB
WeightedMajority
VotePredictions
train
train
train
train
Training an ensemble classifier
Classifiers• SVM • 0/1 String Kernel
• Substitution Matrix Kernel
• PSI-Blast PSSM - Polynomial Kernel
• Decision Tree• Naïve Bayes• Identity windows• Identity plus additional information
( S(x i,y i)i1
|w|
)e where S(x i,y i) 1 if x i y i and 0 otherwise
S(x i,y i) entry(x i,y i) in the Blosum62 matrix
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
ConclusionIn this work we addressed the problem of predicting O-, N-, and C-Linked glycosylation sites from protein sequences. We trained and evaluated an Ensemble Classifier in conjunction with SVM, Naïve Bayes and Decision Tree models. Our experiments show that an ensemble classifier approach achieves low generalization error and can outperform a single trained classifier.