Welcome to the official repository and documentation overview for GlycoEP, an open-access web server designed for the accurate prediction of N-, O-, and C-linked glycosites in eukaryotic protein sequences. Glycosylation is a critical post-translational modification involved in protein folding, cell-cell interactions, and host-pathogen recognition. GlycoEP was developed to provide biologists with a more robust tool for identifying these sites using high-quality, non-redundant datasets. Web Server: [(https://webs.iiitd.edu.in/raghava/glycoep/suppli.html)] Citation Chauhan JS, Rao A, Raghava GPS (2013). In silico Platform for Prediction of N-, O- and C-Glycosites in Eukaryotic Protein Sequences. PLoS ONE 8(6): e67008. https://doi.org/10.1371/journal.pone.0067008 GitHub:-https://github.com/Manish-IIITD-repository/GlycoEP About the Platform GlycoEP addresses the limitations of existing glycosylation prediction tools by utilizing larger, more stringently filtered datasets and advanced machine-learning techniques[cite: 1]. The platform supports the prediction of three major types of eukaryotic glycosylation: N-linked: Attachment to the Nitrogen atom of Asparagine (Asn) in the Asn−X−Ser/Thr sequon. O-linked: Attachment to Serine (Ser) or Threonine (Thr) residues. C-linked: Attachment to the first Tryptophan (Trp) in motifs like W−X−X−W. The server offers two tiers of prediction models: Standard Models: Developed from non-redundant proteins where no two sequences share more than 40% similarity. Advanced Models: Developed from highly non-redundant pattern datasets where no two glycosite patterns share more than 60% similarity, ensuring more robust and biologically meaningful predictions. Key Features Predictive Modeling Machine Learning: Support Vector Machine (SVM) was identified as the optimum tool for these models, utilizing the RBF kernel. High Accuracy: Advanced SVM models achieved accuracies of 84.26% for N-linked, 86.87% for O-linked, and 91.43% for C-linked glycosites. Feature Integration: Models incorporate Binary Profiles of Patterns (BPP), Composition Profiles (CPP), and PSSM profiles (PPP). Structural Information Secondary Structure: Incorporates three-state structure information (Coil, Helix, Strand) obtained via PSIPRED. Surface Accessibility: Includes Predicted Accessible Surface Area (ASA) values, which significantly improve O-glycosite prediction. Built-in Tools Sequon Scanner: A dedicated tool to scan input protein sequences for both universal and rare N-linked glycosylation motifs. Multiple Submissions: The server allows users to submit multiple sequences simultaneously for high-throughput analysis. Overview of Model Development GlycoEP models were trained and evaluated using a 5-fold cross-validation procedure. The datasets were derived from the SWISS-PROT database (June 2011 release), focusing exclusively on experimentally verified eukaryotic glycoproteins. Overlapping patterns of 21 residues were generated for each potential glycosite to capture the local sequence context. Feature N-linked O-linked C-linked Primary Motif Asn−X−Ser/Thr Ser/Thr (no consensus) W−X−X−W/C/F Advanced Accuracy 84.24% 86.87% 91.43% MCC (Advanced) 0.54 0.20 0.78 Applications Proteomics: Characterization and annotation of glycosites in eukaryotic proteins. Therapeutics: Analysis of human therapeutic glycoproteins, which constitute over 70% of current protein-based drugs. Biological Research: Investigating the role of glycans in protein folding, cell signaling, and host-pathogen interactions. Contact & Authors Developed under the Open Source Drug Discovery (OSDD) initiative Prof. Dr. Gajendra PS Raghava raghava@iiitd.ac.in License This platform and its associated research are distributed under the Creative Commons Attribution License (CC BY 2.0).