synthetic-sciences Glycobiology: Glycosylation Analysis
Overview
Glycobiology provides lightweight computational tools for predicting and analyzing glycosylation sites in protein sequences. This skill covers N-glycosylation sequon motif finding (N-X-S/T where X is not P), O-glycosylation hotspot prediction using a sliding window serine/threonine density heuristic, glycan structure tool references, and combined glycoprotein analysis with domain mapping. All analyses use pure Python with minimal dependencies (Biopython for sequence I/O, regex for pattern matching).
When to Use This Skill
- Predicting N-glycosylation sites from protein sequences
- Identifying O-glycosylation hotspot regions
- Planning glycosylation site mutagenesis experiments
- Comparing predicted vs experimentally determined glycosylation sites
- Annotating glycosylation in biotherapeutic protein design
- Surveying glycan analysis tools for downstream structural studies
Related Skills: For protein function and existing glycosylation annotations use uniprot-database. For protein 3D structure and site accessibility use alphafold-database. For sequence manipulation use biopython.
Installation
uv pip install biopython numpy
No additional dependencies required — this skill uses pure Python.
Quick Start
import re
def find_n_glycosylation_sites(sequence):
"""Find N-X-S/T sequons where X != P."""
sites = []
for i in range(len(sequence) - 2):
if sequence[i] == 'N' and sequence[i+1] != 'P' and sequence[i+2] in ('S', 'T'):
sites.append({
'position': i + 1, # 1-based
'motif': sequence[i:i+3],
'context': sequence[max(0,i-3):i+6]
})
return sites
# Example: human EPO
epo_seq = "MGVHECPAWLWLLLSLLSLPLGLPVLGAPPRLICDSRVLERYLLEAKEAENITTGCAEHCSLNENITVPDTKVNFYAWKRMEVGQQAVEVWQGLALLSEAVLRGQALLVNSSQPWEPLQLHVDKAVSGLRSLTTLLRALGAQKEAISPPDAASAAPLRTITADTFRKLFRVYSNFLRGKLKLYTGEACRTGDR"
sites = find_n_glycosylation_sites(epo_seq)
print(f"N-glycosylation sites: {len(sites)}")
for s in sites:
print(f" Position {s['position']}: {s['motif']} (context: ...{s['context']}...)")
Core Capabilities
1. N-Glycosylation Motif Finding
Identify N-X-S/T sequons in protein sequences.
import re
from Bio import SeqIO
def find_n_glycosylation_sites(sequence, exclude_proline=True):
"""Find N-linked glycosylation sequons (N-X-S/T).
Args:
sequence: protein sequence string
exclude_proline: if True, exclude N-P-S/T motifs (standard rule)
"""
sites = []
seq = str(sequence).upper()
for i in range(len(seq) - 2):
if seq[i] != 'N':
continue
x_residue = seq[i + 1]
st_residue = seq[i + 2]
# Standard rule: X != P
if exclude_proline and x_residue == 'P':
continue
if st_residue not in ('S', 'T'):
continue
# Context (5 residues each side)
context_start = max(0, i - 5)
context_end = min(len(seq), i + 8)
context = seq[context_start:context_end]
sites.append({
'position': i + 1, # 1-based position
'residue': seq[i],
'motif': seq[i:i+3],
'x_residue': x_residue,
'acceptor': st_residue,
'context': context,
'context_start': context_start + 1
})
return sites
def scan_fasta_for_n_glyc(fasta_path):
"""Scan all sequences in a FASTA file for N-glycosylation sites."""
results = []
for record in SeqIO.parse(fasta_path, 'fasta'):
sites = find_n_glycosylation_sites(str(record.seq))
results.append({
'id': record.id,
'description': record.description,
'length': len(record.seq),
'n_sites': len(sites),
'sites': sites,
'density': len(sites) / len(record.seq) * 1000 # per 1000 residues
})
import pandas as pd
df = pd.DataFrame([{k: v for k, v in r.items() if k != 'sites'} for r in results])
print(f"Scanned {len(df)} sequences")
print(f"Total N-glyc sites: {df['n_sites'].sum()}")
print(f"Mean density: {df['density'].mean():.1f} per 1000 residues")
return results
# Example with multiple sequences
sites = find_n_glycosylation_sites("MANLQTSPNGTLKNVTSITDA")
for s in sites:
print(f"N{s['position']}: {s['motif']} (X={s['x_residue']}, acceptor={s['acceptor']})")
2. O-Glycosylation Hotspot Prediction
Identify regions enriched in serine/threonine that may be O-glycosylated.
import numpy as np
def predict_o_glycosylation_hotspots(sequence, window_size=20, threshold=0.4,
exclude_near_proline=True):
"""Predict O-glycosylation hotspots using S/T density heuristic.
O-glycosylation preferentially occurs in S/T-rich regions, often near
proline residues. This heuristic identifies candidate regions.
Args:
sequence: protein sequence string
window_size: sliding window size
threshold: minimum S/T fraction to call a hotspot
exclude_near_proline: if True, downweight S/T not near P
"""
seq = str(sequence).upper()
n = len(seq)
if n < window_size:
return []
# Calculate S/T density in sliding windows
densities = []
for i in range(n - window_size + 1):
window = seq[i:i + window_size]
st_count = window.count('S') + window.count('T')
density = st_count / window_size
densities.append(density)
densities = np.array(densities)
# Find hotspot regions
hotspots = []
in_hotspot = False
start = 0
for i, d in enumerate(densities):
if d >= threshold and not in_hotspot:
start = i
in_hotspot = True
elif d < threshold and in_hotspot:
hotspots.append({
'start': start + 1, # 1-based
'end': i + window_size,
'length': i + window_size - start,
'max_density': float(densities[start:i].max()),
'mean_density': float(densities[start:i].mean()),
'region': seq[start:i + window_size]
})
in_hotspot = False
if in_hotspot:
hotspots.append({
'start': start + 1,
'end': n,
'length': n - start,
'max_density': float(densities[start:].max()),
'mean_density': float(densities[start:].mean()),
'region': seq[start:]
})
# Individual S/T residues in hotspots
all_st_sites = []
for hs in hotspots:
region_start = hs['start'] - 1
region_seq = hs['region']
for j, aa in enumerate(region_seq):
if aa in ('S', 'T'):
pos = region_start + j + 1
# Check proline neighbors
has_nearby_P = False
for k in range(-3, 4):
check_pos = region_start + j + k
if 0 <= check_pos < n and seq[check_pos] == 'P':
has_nearby_P = True
break
all_st_sites.append({
'position': pos,
'residue': aa,
'in_hotspot': True,
'near_proline': has_nearby_P
})
print(f"O-glycosylation hotspots: {len(hotspots)}")
for hs in hotspots:
print(f" Pos {hs['start']}-{hs['end']}: density={hs['max_density']:.2f}, "
f"{hs['region'][:30]}{'...' if len(hs['region'])>30 else ''}")
print(f"Candidate S/T residues: {len(all_st_sites)}")
return hotspots, all_st_sites
3. Glycan Structure Resources
Curated list of tools for glycan structure analysis and prediction.
GLYCAN_TOOLS = {
'NetNGlyc': {
'url': 'https://services.healthtech.dtu.dk/services/NetNGlyc-1.0/',
'description': 'Neural network prediction of N-glycosylation sites',
'input': 'Protein sequence (FASTA)',
'output': 'N-glycosylation probability per Asn',
'note': 'More accurate than motif-only scanning; considers sequence context'
},
'NetOGlyc': {
'url': 'https://services.healthtech.dtu.dk/services/NetOGlyc-4.0/',
'description': 'Neural network prediction of mucin-type O-glycosylation',
'input': 'Protein sequence (FASTA)',
'output': 'O-glycosylation probability per Ser/Thr',
'note': 'Version 4.0 trained on O-GalNAc glycoproteomics data'
},
'GlycoWorkbench': {
'url': 'https://code.google.com/archive/p/glycoworkbench/',
'description': 'Glycan structure drawing and MS annotation',
'input': 'Mass spectrometry data',
'output': 'Glycan structure assignments'
},
'GLYCAM-Web': {
'url': 'https://glycam.org/',
'description': 'Glycan 3D structure building and MD simulation',
'input': 'Glycan sequence (IUPAC condensed)',
'output': '3D coordinates (PDB), AMBER parameters'
},
'Glycoshield-MD': {
'url': 'https://github.com/GlycoSHIELD-MD/GlycoSHIELD-MD',
'description': 'Model glycan shields on protein structures for MD',
'input': 'Protein PDB + glycosylation sites',
'output': 'Glycosylated protein structure'
},
'SweetTalk': {
'url': 'https://glycoproteome.expasy.org/sweettalk/',
'description': 'Glycoprotein structure visualization',
'input': 'UniProt ID',
'output': 'Interactive glycoprotein 3D viewer'
}
}
def list_glycan_tools():
"""Print available glycan analysis tools."""
for name, info in GLYCAN_TOOLS.items():
print(f"\n{name}")
print(f" URL: {info['url']}")
print(f" Description: {info['description']}")
if 'note' in info:
print(f" Note: {info['note']}")
4. Glycoprotein Analysis
Combined N/O glycosylation analysis with domain mapping.
def analyze_glycoprotein(sequence, protein_name='protein',
uniprot_sites=None):
"""Comprehensive glycosylation analysis.
Args:
sequence: protein sequence
protein_name: name for reporting
uniprot_sites: dict of known sites from UniProt
{'N_glyc': [positions], 'O_glyc': [positions]}
"""
seq = str(sequence).upper()
print(f"=== Glycoprotein Analysis: {protein_name} ===")
print(f"Length: {len(seq)} residues")
# N-glycosylation
n_sites = find_n_glycosylation_sites(seq)
print(f"\nN-glycosylation sites (sequon motif): {len(n_sites)}")
for s in n_sites:
print(f" N{s['position']}: {s['motif']}")
# O-glycosylation hotspots
o_hotspots, o_sites = predict_o_glycosylation_hotspots(seq)
# Comparison with UniProt annotations
if uniprot_sites:
known_n = set(uniprot_sites.get('N_glyc', []))
predicted_n = set(s['position'] for s in n_sites)
tp = known_n & predicted_n
fp = predicted_n - known_n
fn = known_n - predicted_n
print(f"\nComparison with UniProt annotations:")
print(f" True positives (predicted & annotated): {len(tp)}")
print(f" False positives (predicted, not annotated): {len(fp)}")
print(f" False negatives (annotated, not predicted): {len(fn)}")
if fn:
print(f" Missed sites: {sorted(fn)}")
for pos in fn:
context = seq[max(0,pos-4):pos+5]
print(f" N{pos}: ...{context}... (may have N-P-S/T or non-standard motif)")
# Summary
print(f"\nSummary:")
print(f" N-glyc sites: {len(n_sites)}")
print(f" O-glyc hotspots: {len(o_hotspots)}")
print(f" Total candidate O-glyc S/T: {len(o_sites)}")
print(f" Glycosylation density: {(len(n_sites) + len(o_sites)) / len(seq) * 100:.1f}%")
return {
'n_glyc_sites': n_sites,
'o_glyc_hotspots': o_hotspots,
'o_glyc_sites': o_sites
}
Typical Workflows
Workflow 1: Predict All Glycosylation Sites in a Protein
sequence = "MANLQTSPNGTLKNVTSITDANLTQSPNNFTLRNITSANDTVTQSTPNVTQLNVSQSTPNVTQLNVSQST"
results = analyze_glycoprotein(sequence, protein_name='MyProtein')
Workflow 2: Compare Predicted vs UniProt-Annotated Sites
sequence = "MGVHECPAWLWLLLSLLSLPLGLPVLGAPPRLICDSRVLERYLLEAKEAENITTGCAEHCSLNENITVPDT"
known = {'N_glyc': [24, 38, 83], 'O_glyc': []} # Known from UniProt
results = analyze_glycoprotein(sequence, protein_name='EPO', uniprot_sites=known)
Best Practices
- N-glycosylation prediction — sequon motif (N-X-S/T, X!=P) is necessary but not sufficient; not all sequons are glycosylated; use NetNGlyc for probability-based prediction
- O-glycosylation — much harder to predict than N-glyc; the S/T density heuristic identifies candidate regions but has high false positive rate; use NetOGlyc for better accuracy
- Validate with UniProt — always cross-reference predictions with experimentally verified glycosylation annotations in UniProt
- Signal peptide — glycosylation occurs in the ER/Golgi; ensure the protein has a signal peptide (positions are relative to mature protein in databases)
- Structural context — glycosylation requires surface accessibility; use AlphaFold to check if predicted sites are surface-exposed
- Biotherapeutics — for antibody/protein drug design, N-glycosylation in Fc regions (N297 in IgG) is critical for effector function
Troubleshooting
Problem: Sequon search finds sites in cytoplasmic proteins Solution: N-glycosylation only occurs in secretory pathway proteins. Filter predictions by subcellular localization (check UniProt).
Problem: Known glycosylation site not detected Solution: Some rare N-glycosylation occurs at non-canonical motifs (N-X-C). Check if the site has N-P-S/T which is excluded by default.
Problem: Too many O-glycosylation hotspots Solution: Increase the density threshold (e.g., 0.5). Focus on regions that are both S/T-rich and in extracellular domains.