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.
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 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 service version together.

The service documentation and sample output were reviewed; new prediction jobs were not submitted during this refresh.

Structural shielding with GlycoSHIELD

GlycoSHIELD grafts pre-simulated glycan conformers onto protein structures and filters steric clashes. It does not predict which sequons are occupied or run fresh molecular dynamics for each query. See Tsai et al., 2024.

Read references/glycoshield.md for the pinned source, installation, direct Python API, input mapping and SASA analysis. The reviewed upstream CLI silently ignores several parsed options, including --mode, --threshold and --dryrun; use the documented direct API workaround. Outputs include per-site PDB/XTC ensembles and shielding encoded in a PDB B-factor column; those values are not crystallographic temperature factors.

Database evidence and glycan notation

Read references/glycan_databases.md for the live GlyTouCan SPARQL lookup, GlyConnect access limitations, curated resources and notation. Query exact accessions and preserve dataset/source dates, species, tissue/cell context and supporting publications. Missing records or service errors cannot establish that a protein is unglycosylated.

A monosaccharide composition such as Hex:5 HexNAc:4 dHex:1 does not resolve linkages, branch positions, or distinguish GlcNAc from GalNAc. A cartoon without linkages is schematic, not IUPAC condensed notation. Use an actual sequence (WURCS/GlycoCT/IUPAC with its uncertainty retained) and accession when available. Core fucose attaches to the innermost GlcNAc of an N-glycan, not to core mannose.

Antibody engineering decisions

Number Fc mutations in an explicit scheme (commonly EU), then map them to the actual construct. EU N297 is not residue 297 of an isolated Fc FASTA. Fc glycans and any Fab glycans must be distinguished analytically.

Fc sequence variants such as S298A/E333A/K334A or F243L-containing combinations can alter both receptor interaction and host-dependent glycan processing. Do not label F243L alone as a deterministic defucosylation switch. See the 2026 Fc-variant glycan study.

Experimental interpretation and verification boundary

Glycopeptide searches can support peptide identity, composition and sometimes site localization, without resolving a full glycan structure. Review localization fragments, competing assignments, search-space choices and error control. In Byonic, composition does not determine topology; O-glycosite ambiguity may remain even with an identified glycopeptide. Relative signal among detected glycoforms is not automatically absolute site occupancy; occupancy needs the appropriate modified and unmodified denominator and analytical response considerations.

Local sequence behavior is covered by synthetic tests. Public accession lookup was executed; API error handling was mocked. GlycoSHIELD source/argument handling was checked, but full conformer grafting, GROMACS SASA, DTU jobs, commercial MS software and biological performance were not executed. Supporting references record the service/source review date and unresolved access gaps.