Glycoengineering

If you're engineering therapeutic proteins or antibodies, this handles the glycosylation analysis you'd otherwise code from scratch every time. It scans sequences for N-glycosylation sequons (the N-X-S/T motif where X isn't proline), predicts O-glycosylation hotspots based on local serine/threonine density, and includes helpers for adding or removing glycosites through conservative mutations. The heuristic O-glyc predictor is explicitly a baseline, not a replacement for NetOGlyc, which is the right move since nothing beats experimentally trained models for that. Comes with integrations for external tools like NetOGlyc and references to GlycoShield for vaccine design work. Honest assessment: this is solid for antibody Fc optimization and initial therapeutic protein characterization, though you'll want wet lab confirmation for anything headed to the clinic.

skills.sh installs: 2K
GitHub stars: 56.6K
last updated: Aug 21, 2026

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Install

$ npx -y skills add k-dense-ai/scientific-agent-skills --skill glycoengineering --agent claude-code

Installs into .claude/skills of the current project.

Files

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.