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tooluniverse-protein-therapeutic-design

AI-guided de novo protein design — RFdiffusion backbone generation, ProteinMPNN sequence design, structure validation (pLDDT, pTM, MPNN scores). Use for designing therapeutic protein binders, novel scaffolds, enzyme variants, and miniprotein/protein-interface design before experimental validation.

plugins/tooluniverse/skills/tooluniverse-protein-therapeutic-design/SKILL.md

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Repository stars (not skill ratings): 1,779
Repository last updated: Nov 8, 2026
License: Apache-2.0

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CHECKLIST.mddoc · 6,775 bytes
DESIGN_PROCEDURES.mddoc · 4,117 bytes
EXAMPLES.mddoc · 10,976 bytes
SKILL.mdcore · 7,395 bytes
TOOLS_REFERENCE.mddoc · 16,043 bytes
design_templates.mddoc · 2,906 bytes

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# 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**:
1. **Structure-first** - Generate backbone geometry before sequence
2. **Target-guided** - Design binders with target structure in mind
3. **Iterative validation** - Predict structure to validate designs
4. **Developability-aware** - Consider aggregation, immunogenicity, expression
5. **Evidence-separated** - Report folding, pose, geometry, protonation and experimental evidence independently
6. **Actionable output** - Provide sequences ready for experimental testing
7. **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.