Glycoengineering

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

N-Glycosylation Sequon Analysis

Scanning for N-Glycosylation Sites

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"))

Mutating N-Glycosylation Sites

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

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

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/

2. GlycoSHIELD (Glycan Shielding Analysis)

GlycoSHIELD grafts libraries of pre-simulated glycan conformers onto a static protein structure and scores how much of the protein surface the glycans shield, without running new MD