Description
glycoengineering is an agent skill for analysing and engineering protein glycosylation. It scans for canonical N-glycosylation sequons, which are the motifs where sugars attach to proteins, describes serine and threonine rich regions, checks curated glycan evidence, and helps with antibody glycoform comparison and structural glycan shielding. It also triages candidate mucin-type O-GalNAc sites. The skill insists that four kinds of result are kept separate: a sequence motif, a prediction, experimentally supported occupancy, and a glycan structure or composition. None of these alone establishes the others. The workflow focuses on N-glycosylation in the eukaryotic secretory pathway and on mucin-type O-GalNAc. Other O-linked modifications, such as O-GlcNAc, O-mannose and O-fucose, need appropriate evidence and predictors, and enrichment of serine and threonine residues does not show which type is present. Use it when you are looking at candidate glycosylation sites in a protein, comparing antibody glycoforms, or considering how glycans might shield a surface. It is part of the k-dense-ai scientific agent skills repository on GitHub, which has about 49,600 stars.…
Highlights
- Scans N-glycosylation sequons
- O-GalNAc candidate triage
- Antibody glycoform comparison
- Keeps motif, prediction and evidence separate
- Notes the limits of O-linked predictions
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Provides a Python interface to bioinformatics services including UniProt, KEGG
glycoengineering FAQ
What does it analyse?
Protein glycosylation: canonical N-glycosylation sequons, O-GalNAc candidates, antibody glycoforms and glycan shielding.
Does a sequon mean the site is glycosylated?
No. The skill keeps a sequence motif, a prediction, experimentally supported occupancy and glycan structure as separate kinds of result.
Which O-linked types does it cover?
The mucin-type O-GalNAc workflow. Other types such as O-GlcNAc, O-mannose and O-fucose need their own evidence and predictors.
Can it tell O-linked types apart?
No. The skill says serine and threonine enrichment does not identify the type of modification.
How do I install it?
Run npx skills add https://github.com/k-dense-ai/scientific-agent-skills --skill glycoengineering.
Is it checked for security?
Its security audits show passes from Gen Agent Trust Hub, Socket and Snyk.
What are the best glycoengineering alternatives?
Popular alternatives to glycoengineering include gget, adaptyv and anndata. See all glycoengineering alternatives