Alterlab Glycoengineering

---
name: alterlab-glycoengineering
description: Analyze and engineer protein glycosylation — scan sequences for N-glycosylation sequons (N-X-S/T), predict O-glycosylation hotspots, and reach curated glycoengineering tools (NetOGlyc, GlycoShield, GlycoWorkbench). Use when identifying or designing glycosylation sites, optimizing therapeutic-antibody or biologic glycoforms, or doing glycoprotein engineering and vaccine-design work. Part of the AlterLab Academic Skills suite.
license: MIT
allowed-tools: Read Write Edit Bash(python:*) Bash(uv:*)
compatibility: "Sequon scanning and mutation helpers are pure-Python stdlib (re, typing) — run them under `uv run python` directly. The optional database helpers need `requests` (and the batch example needs `pandas`); external web services (NetNGlyc/NetOGlyc, GlyGen, GlyTouCan) need network access but no API key or account. Endpoint status verified 2026-09."
metadata:
    skill-author: AlterLab
    version: "1.1.0"
    last_updated: "2026-09-23"
---

# Glycoengineering

## Overview

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:**
- **N-glycosylation**: Attached to asparagine (N) in the sequon N-X-[S/T] where X ≠ Proline; occurs in the ER/Golgi
- **O-glycosylation**: Attached to serine (S) or threonine (T); no strict consensus motif; primarily GalNAc initiation

## When to Use This Skill

Use this skill when:

- **Antibody engineering**: Optimize Fc glycosylation for enhanced ADCC, CDC, or reduced immunogenicity
- **Therapeutic protein design**: Identify glycosylation sites that affect half-life, stability, or immunogenicity
- **Vaccine antigen design**: Engineer glycan shields to focus immune responses on conserved epitopes
- **Biosimilar characterization**: Compare glycan patterns between reference and biosimilar
- **Drug target analysis**: Does glycosylation affect target engagement for a receptor?
- **Protein stability**: N-glycans often stabilize proteins; identify sites for stabilizing mutations

### Does NOT Trigger

| Scenario | Use Instead |
|----------|-------------|
| Glycan/carbohydrate cheminformatics (structures, descriptors, SMILES) | `alterlab-rdkit` |
| Identifying glycopeptides from mass-spectrometry raw data | `alterlab-pyopenms` |
| Predicting the 3D structure of the glycoprotein itself | `alterlab-alphafold`, `alterlab-boltz` |
| MD simulation of a glycan shield (setup, force fields, trajectories) | `alterlab-molecular-dynamics` |
| Looking up a protein's curated sequence features in UniProt | `alterlab-uniprot` |

## N-Glycosylation Sequon Analysis

### Scanning for N-Glycosylation Sites

N-glycosylation occurs at the sequon **N-X-[S/T]** where X ≠ Proline.

```python
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 = []
    # Step by 1, not 3: adjacent sequons can overlap (e.g. NNST has a sequon at
    # both position 1 (N-N-S) and position 2 (N-S-T)); skipping ahead misses them.
    for i in range(len(seq) - 2):
        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'
            })
    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"))
```

### Mutating N-Glycosylation Sites

```python
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)
```

## O-Glycosylation Analysis

### Heuristic O-Glycosylation Hotspot Prediction

```python
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
```

## External Glycoengineering Tools

### 1. NetOGlyc 4.0 (O-glycosylation prediction)

Web service for high-accuracy O-GalNAc site prediction:
- **URL**: https://services.healthtech.dtu.dk/services/NetOGlyc-4.0/
- **Input**: FASTA protein sequence
- **Output**: Per-residue O-glycosylation probability scores
- **Method**: Neural network trained on experimentally verified O-GalNAc sites

NetOGlyc 4.0 has no stable public REST API — the CGI submission endpoint and
its form parameters change between web-service revisions. For reliable results,
submit FASTA at the web interface and download the result table:

- NetOGlyc 4.0 (O-GalNAc): https://services.healthtech.dtu.dk/services/NetOGlyc-4.0/
- NetNGlyc 1.0 (N-glyc): https://services.healthtech.dtu.dk/services/NetNGlyc-1.0/

The standalone packages are also downloadable from those pages for offline/batch
runs. Use the inline `find_n_glycosylation_sequons` above as a fast pre-screen.

### 2. GlycoShield-MD (Glycan Shielding Analysis)

GlycoShield-MD analyzes how glycans shield protein surfaces during MD simulations:
- **URL**: https://gitlab.mpcdf.mpg.de/dioscuri-biophysics/glycoshield-md/
- **Use**: Map glycan shielding on protein surface over MD trajectory
- **Output**: Per-residue shielding fraction, visualization

GlycoSHIELD is **not on PyPI** (verified 2026-09 — `pip install glycoshield` fails with
404). Install it from the project's GitLab repository following its own README, which also
documents the expected inputs: a glycoprotein topology (PDB), a trajectory, and the glycan
residue names to treat as the shield. Output is a per-residue shielding fraction you can map
onto the surface — the number that tells you which epitopes a glycan actually occludes.

### 3. GlycoWorkbench (Glycan Structure Drawing/Analysis)

- **URL**: https://github.com/glycoinfo/eurocarbdb
- **Use**: Draw glycan structures, calculate masses, annotate MS spectra
- **Format**: GlycoCT, IUPAC condensed glycan notation

### 4. Experimentally verified glycosylation: GlyGen (and GlyConnect)

**GlyGen** (`api.glygen.org`) is the route that currently works programmatically —
verified 2026-09 with plain GET requests returning JSON:

```python
import requests

def glygen_protein(uniprot_canonical_ac: str) -> dict:
    """Protein record incl. reported glycosylation sites. Accession is canonical, e.g. 'P00533-1'."""
    r = requests.get(f"https://api.glygen.org/protein/detail/{uniprot_canonical_ac}/", timeout=30)
    return r.json() if r.ok else {}

def glygen_glycan(glytoucan_ac: str) -> dict:
    """Glycan record: mass, monosaccharide count, IUPAC/WURCS, cross-references."""
    r = requests.get(f"https://api.glygen.org/glycan/detail/{glytoucan_ac}/", timeout=30)
    return r.json() if r.ok else {}

egfr = glygen_protein("P00533-1")
```

**GlyConnect** (https://glyconnect.expasy.org/) remains an excellent curated resource to
browse, but its public REST routes are unreliable: as of 2026-09
`/api/proteins/uniprot/{acc}` returns HTTP 500 and the Swagger spec 404s. Use the web
interface for GlyConnect, and GlyGen for scripted access.

### 5. UniCarbKB (Glycan Structure Database)

- **URL**: https://www.unicarbkb.org/ (the bare `unicarbkb.org` redirects here)
- **Use**: Browse glycan structures, search by mass or composition
- **Format**: GlycoCT or IUPAC notation

## Key Glycoengineering Strategies

### For Therapeutic Antibodies

| 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) |

### Common Mutations Used

| 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 |

## Glycan Notation

### IUPAC Condensed Notation (Monosaccharide abbreviations)

| 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 |

### Complex N-Glycan Structure

```
Typical complex biantennary N-glycan:
Neu5Ac-Gal-GlcNAc-Man\
                       Man-GlcNAc-GlcNAc-[Asn]
Neu5Ac-Gal-GlcNAc-Man/
(±Core Fuc at innermost GlcNAc)
```

## Best Practices

- **Start with NetNGlyc/NetOGlyc** for computational prediction before experimental validation
- **Verify with mass spectrometry**: Glycoproteomics (Byonic, Mascot) for site-specific glycan profiling
- **Consider site context**: Not all predicted sequons are actually glycosylated (accessibility, cell type, protein conformation)
- **For antibodies**: Fc N297 glycan is critical — always characterize this site first
- **Use GlyConnect** to check if your protein of interest has experimentally verified glycosylation data

## Additional Resources

- **GlyTouCan** (glycan structure repository): https://glytoucan.org/
- **GlyConnect**: https://glyconnect.expasy.org/
- **CFG Functional Glycomics**: http://www.functionalglycomics.org/
- **DTU Health Tech servers** (NetNGlyc, NetOGlyc): https://services.healthtech.dtu.dk/
- **GlycoWorkbench**: https://glycoworkbench.software.informer.com/
- **Review**: Apweiler R et al. (1999) Biochim Biophys Acta. PMID: 10580125
- **Therapeutic glycoengineering review**: Jefferis R (2009) Nature Reviews Drug Discovery. PMID: 19247305

Part of the AlterLab Academic Skills suite.