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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 GlycoSHI
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add K-Dense-AI/scientific-agent-skills --skill glycoengineering -a cursor -y
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.
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.
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.
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.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)]})
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.
| Service | Current documented scope and interpretation |
|---|---|
| NetNGlyc 1.0 | Human N-glycosylation; reports network potential and jury agreement. Default threshold 0.5; not a calibrated occupancy probability. Up to 2,000 sequences / 200,000 residues total / 4,000 per sequence. SignalP runs, but extracellular topology is not checked. The server may score N-P-S/T; exclude these from the canonical candidate set unless independent evidence warrants review. |
| NetOGlyc 4.0 | Mammalian mucin-type O-GalNAc; outputs GFF2 confidence scores, with scores greater than 0.5 marked positive. Up to 50 sequences / 200,000 residues total / 4,000 per sequence. Prefer full sequence including signal peptide; isolated sites need 15 residues of flanking context on both sides. A positive supports regional likelihood, not guaranteed site occupancy or glycan type beyond this model's O-GalNAc scope. |
The service documentation and sample output were reviewed; new prediction jobs were not submitted during this refresh.
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.
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.
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.
| Objective | Candidate strategy | Required interpretation |
|---|---|---|
| Increase FcγRIIIa engagement / ADCC | Reduce Fc core fucosylation while preserving the glycan | Effect size depends on antibody, receptor and assay; do not assume a universal fold gain. Structural evidence. |
| Remove canonical Fc N-glycosylation | N297Q/A/D or disrupt the +2 residue with T299A | Sequon disruption; not a guarantee of otherwise unchanged structure or effector function. Verify the actual product. |
| Alter serum persistence | Characterize glycoform-specific clearance with the relevant protein | IgG high-mannose clearance can increase; sialylation is not a universal IgG half-life recipe. Human PK study. |
| Investigate anti-inflammatory Fc activity | Compare defined sialylated glycoforms | Linkage, preparation and biological model matter. Positive results in particular models do not establish a universal IVIG mechanism. Defined Fc study. |
| Reduce non-human glycan epitopes | Measure α-Gal and Neu5Gc and select compatible production conditions | Sequence editing alone does not control the host's glycan processing. |
| Study epitope accessibility | Introduce or remove a mapped surface sequon | Confirm occupancy, folding, binding and antigenicity; shielding calculations are geometric hypotheses. |
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.
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.