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GlycoDomain Viewer
Enables proteome-wide discovery of O-glycan sites using ‘bottom-up’ ETD-based mass spectrometric analysis. GlycoDomain Viewer is a genetic engineering approach that uses human cell lines to simplify O-glycosylation (SimpleCells). The method was implemented on 12 human cell lines from different organs. It presents a first map of the human O-glycoproteome with almost 3000 glycosites in over 600 O-glycoproteins as well as an improved NetOGlyc4.0 model for prediction of O-glycosylation.
I-GPA / Integrated GlycoProteome Analyzer
An Integrated GlycoProteome Analyzer including mapping system for complex N-glycoproteomes, which combines methods for tandem mass spectrometry with a database search and algorithmic suite. I-GPA is a scoring algorithm with decoy glycopeptides, where 95 N-glycopeptides from standard α1-acid glycoprotein were identified with 0% false positives, giving the same results as manual validation. I-GPA platform could make a major breakthrough in high-throughput mapping of complex N-glycoproteomes, which can be applied to biomarker discovery and ongoing global human proteome project.
GlycoPP / GLYCOsites Prediction in Prokaryotes
A webserver for predicting potential N-and O-glycosites in prokaryotic protein sequence(s), where N-glycosite is an Asn residue and O-glycosite could be a serine or threonine residue having a glycan attached covalently and enzymatically at amide or hydroxyl group respectively. An evaluation of the best performing models with 28 independent prokaryotic glycoproteins confirms the suitability of these models in predicting N- and O-glycosites in potential glycoproteins from aforementioned organisms, with reasonably high confidence.
A comprehensive tool for the systematic in silico identification of C-linked, N-linked, and O-linked glycosylation sites in the human proteome. GlycoMine was developed using the random forest algorithm and evaluated based on a well-prepared up-to-date benchmark dataset that encompasses all three types of glycosylation sites, which was curated from multiple public resources. Heterogeneous sequences and functional features were derived from various sources, and subjected to further two-step feature selection to characterize a condensed subset of optimal features that contributed most to the type-specific prediction of glycosylation sites.
CKSAAP_OGlysite / Composition of K-Spaced Amino Acid Pairs in O-glycosylation sites
Predicts mucin-type O-linked glycosylation sites in mammalian proteins. CKSAAP_OGlySite is a web-server developed to facilitate the biological community. This resource demonstrates higher prediction accuracy than some other existing predictors. By using other state-of-the-art machine learning methods as well as combining other encoding schemes, it is expected the CKSAAP encoding can play an important role in developing new O-glycosylation site predicting systems.
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