Glycan Databases and Resources Reference

Primary Databases

GlyTouCan

• URL: https://glytoucan.org/
• Content: Unique accession numbers (GTC IDs) for glycan structures
• Use: Standardized glycan identification across databases
• Format: GlycoCT, WURCS, IUPAC

import requests

def lookup_glytoucan(glytoucan_id: str) -> dict:
    """Fetch glycan details from GlyTouCan."""
    url = f"https://api.glytoucan.org/glycan/{glytoucan_id}"
    response = requests.get(url, headers={"Accept": "application/json"})
    return response.json() if response.ok else {}

GlyConnect

• URL: https://glyconnect.expasy.org/
• Content: Protein glycosylation database with site-specific glycan profiles
• Integration: Links UniProt proteins to experimentally verified glycosylation
• Use: Look up known glycosylation for your target protein

import requests

def get_glycoprotein_info(uniprot_id: str) -> dict:
    """Get glycosylation data for a protein from GlyConnect."""
    base_url = "https://glyconnect.expasy.org/api"
    response = requests.get(f"{base_url}/proteins/uniprot/{uniprot_id}")
    return response.json() if response.ok else {}

def get_glycan_compositions(glyconnect_protein_id: int) -> list:
    """Get all glycan compositions for a GlyConnect protein entry."""
    base_url = "https://glyconnect.expasy.org/api"
    response = requests.get(f"{base_url}/compositions/protein/{glyconnect_protein_id}")
    return response.json().get("data", []) if response.ok else []

UniCarbKB

• URL: https://unicarbkb.org/
• Content: Curated glycan structures with biological context
• Features: Tissue/cell-type specific glycan data, mass spectrometry data

KEGG Glycan

• URL: https://www.genome.jp/kegg/glycan/
• Content: Glycan structures in KEGG format, biosynthesis pathways
• Integration: Links to KEGG PATHWAY maps for glycan biosynthesis

CAZy (Carbohydrate-Active Enzymes)

• URL: http://www.cazy.org/
• Content: Enzymes that build, break, and modify glycans
• Use: Identify enzymes for glycoengineering applications

Prediction Servers

NetNGlyc 1.0

• URL: https://services.healthtech.dtu.dk/services/NetNGlyc-1.0/
• Method: Neural network for N-glycosylation site prediction
• Input: Protein FASTA sequence
• Output: Per-asparagine probability score; threshold ~0.5

NetOGlyc 4.0

• URL: https://services.healthtech.dtu.dk/services/NetOGlyc-4.0/
• Method: Neural network for O-GalNAc glycosylation prediction
• Input: Protein FASTA sequence
• Output: Per-serine/threonine probability; threshold ~0.5

GlycoMine (Machine Learning)

• Machine learning predictor for N-, O- and C-glycosylation
• Multiple glycan types: N-GlcNAc, O-GalNAc, O-GlcNAc, O-Man, O-Fuc, O-Glc, C-Man

SymLink (Glycosylation site & sequon predictor)

• Species-specific N-glycosylation prediction
• More specific than simple sequon scanning

Mass Spectrometry Glycoproteomics Tools

Byonic (Protein Metrics)

• De novo glycopeptide identification from MS2 spectra
• Comprehensive glycan database
• Site-specific glycoform assignment

Mascot Glycan Analysis

• Glycan-specific search parameters
• Common for bottom-up glycoproteomics

GlycoWorkbench

• URL: https://www.eurocarbdb.org/project/glycoworkbench
• Glycan structure drawing and mass calculation
• Annotation of MS/MS spectra with glycan fragment ions

Skyline

• Targeted quantification of glycopeptides
• Integrates with glycan database

Glycan Nomenclature Systems

Oxford Notation (For N-glycans)

Codes complex N-glycans as text strings:

G0F   = Core-fucosylated, biantennary, no galactose
G1F   = Core-fucosylated, one galactose
G2F   = Core-fucosylated, two galactoses
G2FS1 = Core-fucosylated, two galactoses, one sialic acid
G2FS2 = Core-fucosylated, two galactoses, two sialic acids
M5    = High mannose 5 (Man5GlcNAc2)
M9    = High mannose 9 (Man9GlcNAc2)

Symbol Nomenclature for Glycans (SNFG)

Standard colored symbols for publications:

• Blue circle = Glucose
• Green circle = Mannose
• Yellow circle = Galactose
• Blue square = N-Acetylglucosamine
• Yellow square = N-Acetylgalactosamine
• Purple diamond = N-Acetylneuraminic acid (sialic acid)
• Red triangle = Fucose

Therapeutic Glycoproteins and Key Glycosylation Sites

Batch Analysis Example

from glycoengineering_tools import find_n_glycosylation_sequons, predict_o_glycosylation_hotspots
import pandas as pd

def analyze_glycosylation_landscape(sequences_dict: dict) -> pd.DataFrame:
    """
    Batch analysis of glycosylation for multiple proteins.

    Args:
        sequences_dict: {protein_name: sequence}

    Returns:
        DataFrame with glycosylation summary per protein
    """
    results = []
    for name, seq in sequences_dict.items():
        n_sites = find_n_glycosylation_sequons(seq)
        o_sites = predict_o_glycosylation_hotspots(seq)

        results.append({
            'protein': name,
            'length': len(seq),
            'n_glycosites': len(n_sites),
            'o_glyco_hotspots': len(o_sites),
            'n_glyco_density': len(n_sites) / len(seq) * 100,
            'n_glyco_positions': [s['position'] for s in n_sites]
        })

    return pd.DataFrame(results)