Glycosylation site prediction using ensembles of Support Vector Machine classifiers

Abstract

Background: Glycosylation is one of the most complex post-translational modifications (PTMs) of proteins in eukaryotic cells. Glycosylation plays an important role in biological processes ranging from protein folding and subcellular localization, to ligand recognition and cell-cell interactions. Experimental identification of glycosylation sites is expensive and laborious. Hence, there is significant interest in the development of computational methods for reliable prediction of glycosylation sites from amino acid sequences.

Retracted: Prediction of posttranslational modification sites from sequences with kernel methods

“…ROC curves of AADOGlySite, DOGlySite, and AAOGlySite for mucin-type O-glycosylation site prediction by 10-fold crossvalidation. For comparison, the corresponding performance measures reported in literature are shown: CKSAAPOGlySite, 24 Li et al, 26 EnsembleGly, 27 and NetOGlyc 3.1. 24 Li et al, 26 EnsembleGly, 27 and NetOGlyc 3.1.…”

“…Comparison of AUCs for AADOGlySite, DOGlySite, AAOGlySite, Li et al, 26 and EnsembleGly. 27 and AADphos are drawn in Figure 2, Table 2, we show the performance in terms of precision and recall to compare our methods with the three models. Obviously, our methods perform much more better than others when precision is concerned as the evaluation criteria.…”

Golgi Glycosylation

“…There are more than 15 polypeptide GalNAc transferases ( ppGalNAcT) in mammals and several in Drosophila (Zhang and Ten Hagen 2010), but none in yeast. Much work has been devoted to determining consensus sites for O-GalNAc addition to Ser or Thr and criteria are beginning to appear (Caragea et al 2007;Gerken et al 2008).…”

Identification of N-Glycosylation Sites with Sequence and Structural Features Employing Random Forests

“…There have been different significant approaches for the identification of N-Linked Glycosylated sites by Gupta et al [6] who reported 76% accuracy for the identification of N-Glycosylation sites in humans and Caragea et al [7] who employed ensembles of Support Vector Machines for the prediction of Glycosylation sites (N-linked,O-linked and C-mannosylation) in this work the accuracy for N-linked glycosylation with Single SVM is 94%, but contains less number of experimentally verified N-Glycosylation sites thus, a rational comparison of our approach with these methodologies is not possible owing to the difference in the datasets and methodologies.…”

“…In a recent study Caragea et. al [7] have used ensemble of Support Vector Machines (SVM) classifiers using string kernels to identify potential glycosylated sites. In their comprehensive study on protein environment of N-glycosylation sites Petrescu et al and Ben-Dor et al [5,8]have indicated that the sequence and structural properties play an important role in deciding the occupancy of the sequon.…”