Tooluniverse Protein Therapeutic Design
This is a comprehensive workflow for designing therapeutic proteins from scratch using AI tools like RFdiffusion for backbone generation and ProteinMPNN for sequence optimization. You'll go through target characterization, generate multiple backbone candidates, design sequences, validate structures with ESMFold or AlphaFold2, and assess developability metrics like aggregation and expression potential. The skill enforces a report-first approach and includes detailed checklists to keep you from skipping validation steps. It's built around NVIDIA NIM tools, so you'll need an API key from build.nvidia.com. The documentation is thorough about parameter naming quirks and common mistakes, which matters when you're running expensive design cycles. Best for computational biologists who need structured guidance through the entire de novo protein design pipeline.
- skills.sh installs
- 400
- GitHub stars
- 1.8K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add mims-harvard/tooluniverse --skill tooluniverse-protein-therapeutic-design --agent claude-codeInstalls into .claude/skills of the current project.
Files
Therapeutic Protein Designer
AI-guided de novo protein design using RFdiffusion backbone generation, ProteinMPNN sequence optimization, and structure validation for therapeutic protein development.
KEY PRINCIPLES:
- Structure-first - Generate backbone geometry before sequence
- Target-guided - Design binders with target structure in mind
- Iterative validation - Predict structure to validate designs
- Developability-aware - Consider aggregation, immunogenicity, expression
- Evidence-separated - Report folding, pose, geometry, protonation and experimental evidence independently
- Actionable output - Provide sequences ready for experimental testing
- English-first queries - Always use English terms in tool calls
Therapeutic protein design starts with the target interaction. What binding surface do you need to cover? A small pocket = nanobody or peptide. A large flat surface = designed protein. Stability, immunogenicity, and manufacturability constrain the design space.
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- Repository
- github.com/mims-harvard/tooluniverse
- Categories
- AI & Agent Building, Sales & Marketing, Design & UI/UX
- First seen
- Jun 3, 2026
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