/glycoengineering

name: glycoengineering
description: Analyzes and engineers protein glycosylation by scanning canonical N-glycosylation sequons, describing S/T-rich regions, checking curated glycan evidence, and preparing NetNGlyc, NetOGlyc and GlycoSHIELD workflows. Use for glycoprotein engineering, antibody Fc glycosylation, glycan shielding, and site-specific glycoproteomics interpretation.
license: Unknown
compatibility: Local sequence helpers require Python 3.10+. Public GlyTouCan lookup requires requests and network access. DTU predictors use a browser; optional GlycoSHIELD needs a separate source installation, glycan libraries, and GROMACS for SASA analysis.
metadata:
  version: "1.4"
  skill-author: Kuan-lin Huang
  last-reviewed: "2026-10-01"

Glycoengineering

When to use

Use for canonical N-glycosylation candidate analysis, O-GalNAc candidate triage, antibody glycoform comparison, or structural glycan shielding. Keep four kinds of result separate: sequence motif, prediction, experimentally supported occupancy, and glycan structure/composition. None alone establishes all the others.

This workflow focuses on eukaryotic secretory-pathway N-glycosylation and mucin-type O-GalNAc. O-GlcNAc, O-mannose, O-fucose and other O-linked modifications require appropriate evidence and predictors; S/T enrichment does not identify the type.

Workflow

1. Record the protein accession, isoform/version, exact sequence, expression host, signal peptide/transmembrane topology and construct boundaries. Preserve a mapping from submitted sequence coordinates to mature protein, PDB chain/residue identifiers and antibody numbering where relevant. 2. Scan canonical N-X-[S/T] candidates with X not Pro. Retain overlaps: `NNST` has candidates starting at 1 and 2. A missing canonical motif does not rule out unusual N-glycosylation; a present motif does not establish occupancy. 3. Prioritize secreted/luminal/extracellular regions using topology and curated evidence. Sequence-only results in a cytoplasmic region are not evidence of secretory-pathway glycosylation. 4. For O-GalNAc, use S/T density only as a descriptive feature, then inspect a suitable predictor and cell/transferase context. **Do not exclude SP/TP motifs**: adjacent Pro can be favorable, depending on the GalNAc-transferase and context. See [the GalNAc-T substrate study](https://doi.org/10.1093/glycob/cwu089). 5. Propose explicitly numbered mutations, list every changed residue and rescan the full product for lost/created overlapping motifs. Evaluate structural and expression effects independently of glycosylation. 6. Validate occupancy and glycoforms experimentally, with localization evidence, glycan composition/structure confidence, and the biological assay required by the engineering objective. For biosimilar comparisons, match sample handling, analytical coverage and quantification before comparing glycan percentages.

Local sequence analysis

The standard-library helper [scripts/glycoengineering_tools.py](scripts/glycoengineering_tools.py) replaces copied snippets. Import it with this skill's `scripts/` directory on `PYTHONPATH`, or run Python from that directory. It accepts raw canonical 20-amino-acid sequences, normalizes whitespace/case, and rejects FASTA headers, gaps, unknown residues and empty input. Parse FASTA records first; do not remove unknown residues because that changes coordinates. Coordinates are 1-based in the normalized submitted sequence.

from glycoengineering_tools import (
    normalize_sequence, find_n_glycosylation_sequons,
    eliminate_glycosite, add_glycosite, find_st_rich_sites,
)

sequence = normalize_sequence("NNST")
sites = find_n_glycosylation_sequons(sequence)
assert [site["position"] for site in sites] == [1, 2]

mutant = eliminate_glycosite(sequence, 1, "Q")
assert mutant == "QNST"  # the overlapping sequon at position 2 remains
assert [site["position"] for site in find_n_glycosylation_sequons(mutant)] == [2]

# Three intended changes: A1N, P2A, A3T. P->A must be explicit.
parent = "APA"
product = add_glycosite(parent, 1, "T", allow_proline_substitution=True)
changes = [(i, before, after) for i, (before, after) in
           enumerate(zip(parent, product), 1) if before != after]
assert product == "NAT" and len(changes) == 3

# Density is a fraction of residues, not an O-glycosylation probability.
o_candidates = find_st_rich_sites("STPST", window=7, min_st_fraction=0.4)
assert [site["position"] for site in o_candidates] == [1, 2, 4, 5]

`eliminate_glycosite` requires a complete canonical sequon and a one-residue replacement other than N. `add_glycosite` can alter up to three residues; it retains an existing S/T at +2. Neither function predicts a mutant's fold, glycan occupancy, function or tolerability. An N-to-Q substitution can change protein behavior even without a glycan effect.

The S/T density window must be a positive odd integer. Terminal windows shorten, and their denominator is the actual window length. Dense regions may help triage mucin-like sequence; isolated real O-GalNAc sites can be missed.

For batch analysis, keep identifiers and normalized sequence lengths:

sequences = {"overlap": "NNST", "control": "APAA", "mucin_like": "STPST"}
rows = []
for name, raw_sequence in sequences.items():
    seq = normalize_sequence(raw_sequence)
    positions = [site["position"] for site in find_n_glycosylation_sequons(seq)]
    rows.append({"protein": name, "length": len(seq),
                 "n_sequon_positions": positions,
                 "n_sequons_per_100_residues": 100 * len(positions) / len(seq),
                 "st_rich_positions": [site["position"] for site in find_st_rich_sites(seq)]})

Prediction services

Use the official forms, which accept FASTA and return web results; this skill does not provide a stable submission API or job-polling endpoint. Saving a CGI URL is not a submitted job. Preserve the output, input and