Glycoengineering

What it does

When your agent uses it

• Glycoprotein engineering
• Antibody Fc glycosylation
• Glycan shielding
• Site-specific glycoproteomics interpretation

Example prompts

• “Use the glycoengineering skill to analyz and engineers protein glycosylation by scanning canonical N-glycosylation sequons, describing S/T-rich…”
• “/glycoengineering”

Requirements

• Python 3
• Compatibility (from SKILL.md): 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.

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

1. 1Record the protein accession, isoform/version, exact sequence, expression host,
2. 2Scan canonical N-X-[S/T] candidates with X not Pro. Retain overlaps: NNST
3. 3Prioritize secreted/luminal/extracellular regions using topology and curated
4. 4For O-GalNAc, use S/T density only as a descriptive feature, then inspect a
5. 5Propose explicitly numbered mutations, list every changed residue and rescan
6. 6Validate occupancy and glycoforms experimentally, with localization evidence,

What it can do on your machine

Read from SKILL.md and the folder at commit 92ace75. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships 2 files in scripts/ (Python), which the agent can run.

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • pubmed.ncbi.nlm.nih.gov
  • doi.org
  • services.healthtech.dtu.dk
  • gitlab.mpcdf.mpg.de
  • support.proteinmetrics.com

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

• 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.

  From compatibility in the SKILL.md frontmatter.

Context cost

Glycoengineering loads about 3k tokens when it runs, and up to ~6.7k if it reads all its reference files. Until then it costs about 90 tokens; SKILL.md has 1,149 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~90

When it runs · the whole SKILL.md, loaded when a task matches

~3k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~6.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from K-Dense-AI/scientific-agent-skills at commit 92ace75, republished under its MIT licence (© K-Dense-AI). 1,149 words, ~2,985 tokens.

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.

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.

license

Unknown

metadata.version

1.4

metadata.skill-author

Kuan-lin Huang

metadata.last-reviewed

2026-10-01

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]

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)]})

© K-Dense-AI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (scripts, references) in skills/glycoengineering of K-Dense-AI/scientific-agent-skills.

• SKILL.md
• references/glycan_databases.md
• references/glycoshield.md
• scripts/glycoengineering_tools.py
• scripts/glytoucan_lookup.py

Open the folder on GitHubat commit 92ace75

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in K-Dense-AI/scientific-agent-skills, which our catalogue first saw on October 7, 2026.

• lingxling/awesome-skills-cnclaude-scientific-skills/skills/glycoengineering