// Analyze and engineer protein glycosylation. Scan sequences for N-glycosylation sequons (N-X-S/T), predict O-glycosylation hotspots, and access curated glycoengineering tools (NetOGlyc, GlycoShield, GlycoWorkbench). For glycoprotein engineering, therapeutic antibody optimization, and vaccine design.
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| name | glycoengineering |
| description | Analyze and engineer protein glycosylation. Scan sequences for N-glycosylation sequons (N-X-S/T), predict O-glycosylation hotspots, and access curated glycoengineering tools (NetOGlyc, GlycoShield, GlycoWorkbench). For glycoprotein engineering, therapeutic antibody optimization, and vaccine design. |
| license | Unknown |
| metadata | {"skill-author":"Kuan-lin Huang"} |
Glycosylation is the most common and complex post-translational modification (PTM) of proteins, affecting over 50% of all human proteins. Glycans regulate protein folding, stability, immune recognition, receptor interactions, and pharmacokinetics of therapeutic proteins. Glycoengineering involves rational modification of glycosylation patterns for improved therapeutic efficacy, stability, or immune evasion.
Two major glycosylation types:
Use this skill when:
N-glycosylation occurs at the sequon N-X-[S/T] where X ≠ Proline.
import re
from typing import List, Tuple
def find_n_glycosylation_sequons(sequence: str) -> List[dict]:
"""
Scan a protein sequence for canonical N-linked glycosylation sequons.
Motif: N-X-[S/T], where X ≠ Proline.
Args:
sequence: Single-letter amino acid sequence
Returns:
List of dicts with position (1-based), motif, and context
"""
seq = sequence.upper()
results = []
i = 0
while i <= len(seq) - 3:
triplet = seq[i:i+3]
if triplet[0] == 'N' and triplet[1] != 'P' and triplet[2] in {'S', 'T'}:
context = seq[max(0, i-3):i+6] # ±3 residue context
results.append({
'position': i + 1, # 1-based
'motif': triplet,
'context': context,
'sequon_type': 'NXS' if triplet[2] == 'S' else 'NXT'
})
i += 3
else:
i += 1
return results
def summarize_glycosylation_sites(sequence: str, protein_name: str = "") -> str:
"""Generate a research log summary of N-glycosylation sites."""
sequons = find_n_glycosylation_sequons(sequence)
lines = [f"# N-Glycosylation Sequon Analysis: {protein_name or 'Protein'}"]
lines.append(f"Sequence length: {len(sequence)}")
lines.append(f"Total N-glycosylation sequons: {len(sequons)}")
if sequons:
lines.append(f"\nN-X-S sites: {sum(1 for s in sequons if s['sequon_type'] == 'NXS')}")
lines.append(f"N-X-T sites: {sum(1 for s in sequons if s['sequon_type'] == 'NXT')}")
lines.append(f"\nSite details:")
for s in sequons:
lines.append(f" Position {s['position']}: {s['motif']} (context: ...{s['context']}...)")
else:
lines.append("No canonical N-glycosylation sequons detected.")
return "\n".join(lines)
# Example: IgG1 Fc region
fc_sequence = "APELLGGPSVFLFPPKPKDTLMISRTPEVTCVVVDVSHEDPEVKFNWYVDGVEVHNAKTKPREEQYNSTYRVVSVLTVLHQDWLNGKEYKCKVSNKALPAPIEKTISKAKGQPREPQVYTLPPSREEMTKNQVSLTCLVKGFYPSDIAVEWESNGQPENNYKTTPPVLDSDGSFFLYSKLTVDKSRWQQGNVFSCSVMHEALHNHYTQKSLSLSPGK"
print(summarize_glycosylation_sites(fc_sequence, "IgG1 Fc"))
def eliminate_glycosite(sequence: str, position: int, replacement: str = "Q") -> str:
"""
Eliminate an N-glycosylation site by substituting Asn → Gln (conservative).
Args:
sequence: Protein sequence
position: 1-based position of the Asn to mutate
replacement: Amino acid to substitute (default Q = Gln; similar size, not glycosylated)
Returns:
Mutated sequence
"""
seq = list(sequence.upper())
idx = position - 1
assert seq[idx] == 'N', f"Position {position} is '{seq[idx]}', not 'N'"
seq[idx] = replacement.upper()
return ''.join(seq)
def add_glycosite(sequence: str, position: int, flanking_context: str = "S") -> str:
"""
Introduce an N-glycosylation site by mutating a residue to Asn,
and ensuring X ≠ Pro and +2 = S/T.
Args:
position: 1-based position to introduce Asn
flanking_context: 'S' or 'T' at position+2 (if modification needed)
"""
seq = list(sequence.upper())
idx = position - 1
# Mutate to Asn
seq[idx] = 'N'
# Ensure X+1 != Pro (mutate to Ala if needed)
if idx + 1 < len(seq) and seq[idx + 1] == 'P':
seq[idx + 1] = 'A'
# Ensure X+2 = S or T
if idx + 2 < len(seq) and seq[idx + 2] not in ('S', 'T'):
seq[idx + 2] = flanking_context
return ''.join(seq)
def predict_o_glycosylation_hotspots(
sequence: str,
window: int = 7,
min_st_fraction: float = 0.4,
disallow_proline_next: bool = True
) -> List[dict]:
"""
Heuristic O-glycosylation hotspot scoring based on local S/T density.
Not a substitute for NetOGlyc; use as fast baseline.
Rules:
- O-GalNAc glycosylation clusters on Ser/Thr-rich segments
- Flag Ser/Thr residues in windows enriched for S/T
- Avoid S/T immediately followed by Pro (TP/SP motifs inhibit GalNAc-T)
Args:
window: Odd window size for local S/T density
min_st_fraction: Minimum fraction of S/T in window to flag site
"""
if window % 2 == 0:
window = 7
seq = sequence.upper()
half = window // 2
candidates = []
for i, aa in enumerate(seq):
if aa not in ('S', 'T'):
continue
if disallow_proline_next and i + 1 < len(seq) and seq[i+1] == 'P':
continue
start = max(0, i - half)
end = min(len(seq), i + half + 1)
segment = seq[start:end]
st_count = sum(1 for c in segment if c in ('S', 'T'))
frac = st_count / len(segment)
if frac >= min_st_fraction:
candidates.append({
'position': i + 1,
'residue': aa,
'st_fraction': round(frac, 3),
'window': f"{start+1}-{end}",
'segment': segment
})
return candidates
Web service for high-accuracy O-GalNAc site prediction:
import requests
def submit_netoglycv4(fasta_sequence: str) -> str:
"""
Submit sequence to NetOGlyc 4.0 web service.
Returns the job URL for result retrieval.
Note: This uses the DTU Health Tech web service. Results take ~1-5 min.
"""
url = "https://services.healthtech.dtu.dk/cgi-bin/webface2.cgi"
# NetOGlyc submission (parameters may vary with web service version)
# Recommend using the web interface directly for most use cases
print("Submit sequence at: https://services.healthtech.dtu.dk/services/NetOGlyc-4.0/")
return url
# Also: NetNGlyc for N-glycosylation prediction
# URL: https://services.healthtech.dtu.dk/services/NetNGlyc-1.0/
GlycoShield-MD analyzes how glycans shield protein surfaces during MD simulations:
# Installation
pip install glycoshield
# Basic usage: analyze glycan shielding from glycosylated protein MD trajectory
glycoshield \
--topology glycoprotein.pdb \
--trajectory glycoprotein.xtc \
--glycan_resnames BGLCNA FUC \
--output shielding_analysis/
import requests
def query_glyconnect(uniprot_id: str) -> dict:
"""Query GlyConnect for glycosylation data for a protein."""
url = f"https://glyconnect.expasy.org/api/proteins/uniprot/{uniprot_id}"
response = requests.get(url, headers={"Accept": "application/json"})
if response.status_code == 200:
return response.json()
return {}
# Example: query EGFR glycosylation
egfr_glyco = query_glyconnect("P00533")
| Goal | Strategy | Notes |
|---|---|---|
| Enhance ADCC | Defucosylation at Fc Asn297 | Afucosylated IgG1 has ~50× better FcγRIIIa binding |
| Reduce immunogenicity | Remove non-human glycans | Eliminate α-Gal, NGNA epitopes |
| Improve PK half-life | Sialylation | Sialylated glycans extend half-life |
| Reduce inflammation | Hypersialylation | IVIG anti-inflammatory mechanism |
| Create glycan shield | Add N-glycosites to surface | Masks vulnerable epitopes (vaccine design) |
| Mutation | Effect |
|---|---|
| N297A/Q (IgG1) | Removes Fc glycosylation (aglycosyl) |
| N297D (IgG1) | Removes Fc glycosylation |
| S298A/E333A/K334A | Increases FcγRIIIa binding |
| F243L (IgG1) | Increases defucosylation |
| T299A | Removes Fc glycosylation |
| Symbol | Full Name | Type |
|---|---|---|
| Glc | Glucose | Hexose |
| GlcNAc | N-Acetylglucosamine | HexNAc |
| Man | Mannose | Hexose |
| Gal | Galactose | Hexose |
| Fuc | Fucose | Deoxyhexose |
| Neu5Ac | N-Acetylneuraminic acid (Sialic acid) | Sialic acid |
| GalNAc | N-Acetylgalactosamine | HexNAc |
Typical complex biantennary N-glycan:
Neu5Ac-Gal-GlcNAc-Man\
Man-GlcNAc-GlcNAc-[Asn]
Neu5Ac-Gal-GlcNAc-Man/
(±Core Fuc at innermost GlcNAc)