| name | tooluniverse-protein-therapeutic-design |
| description | 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. |
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-graded - Grade designs by confidence metrics
- 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.
LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory. A database-verified answer is always more reliable than a guess.
COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
When to Use
Apply when user asks to:
- Design a protein binder, therapeutic protein, or scaffold
- Optimize a protein sequence for function
- Design a de novo enzyme
- Generate protein variants for target binding
Workflow Overview
Phase 1: Target Characterization
Get structure (PDB, EMDB cryo-EM, AlphaFold), identify binding epitope
Phase 2: Backbone Generation (RFdiffusion)
Define constraints, generate >= 5 backbones, filter by geometry
Phase 3: Sequence Design (ProteinMPNN)
Design >= 8 sequences per backbone, sample with temperature control
Phase 4: Structure Validation (ESMFold/AlphaFold2)
Predict structure, compare to backbone, assess pLDDT/pTM
Phase 5: Developability Assessment
Aggregation, pI, expression prediction
Phase 6: Report Synthesis
Ranked candidates, FASTA, experimental recommendations
Critical Requirements
Report-First Approach (MANDATORY)
- Create
[TARGET]_protein_design_report.md first with section headers
- Progressively update as designs are generated
- Output
[TARGET]_designed_sequences.fasta and [TARGET]_top_candidates.csv
Design Documentation (MANDATORY)
Every design MUST include: Sequence, Length, Target, Method, and Quality Metrics (pLDDT, pTM, MPNN score, binding prediction).
NVIDIA NIM Tools
| Tool | Purpose | Key Parameter |
|---|
NvidiaNIM_rfdiffusion (requires NVIDIA_API_KEY env var; free key at build.nvidia.com) | Backbone generation | diffusion_steps (NOT num_steps) |
NvidiaNIM_proteinmpnn (requires NVIDIA_API_KEY env var; free key at build.nvidia.com) | Sequence design | pdb_string (NOT pdb) |
ESMFold_predict_structure | Fast validation | sequence (NOT seq) |
NvidiaNIM_alphafold2 (requires NVIDIA_API_KEY env var; free key at build.nvidia.com) | High-accuracy structure inference from sequence | sequence, algorithm |
NvidiaNIM_esm2_650m (requires NVIDIA_API_KEY env var; free key at build.nvidia.com) | Sequence embeddings | sequences, format |
Common Parameter Mistakes
| Tool | Wrong | Correct |
|---|
NvidiaNIM_rfdiffusion (requires NVIDIA_API_KEY) | num_steps=50 | diffusion_steps=50 |
NvidiaNIM_proteinmpnn (requires NVIDIA_API_KEY) | pdb=content | pdb_string=content |
ESMFold_predict_structure | seq="MVLS..." | sequence="MVLS..." |
NvidiaNIM_alphafold2 (requires NVIDIA_API_KEY) | seq="MVLS..." | sequence="MVLS..." |
NVIDIA NIM Requirements
- API Key:
NVIDIA_API_KEY environment variable required
- Rate limits: 40 RPM (1.5 second minimum between calls)
- AlphaFold2 may return 202 (polling required); RFdiffusion and ESMFold are synchronous
Supporting Tools
| Tool | Purpose | Key Parameters |
|---|
PDBe_get_uniprot_mappings | Find PDB structures | uniprot_id |
RCSBData_get_entry | Download PDB file | pdb_id |
alphafold_get_prediction | Get AlphaFold DB structure | accession |
EMDB_search_structures | Search cryo-EM maps | query |
EMDB_get_structure | Get entry details | entry_id |
UniProt_get_entry_by_accession | Get target sequence | accession |
InterPro_get_protein_domains | Get domains | accession |
Evidence Grading
| Tier | Criteria |
|---|
| T1 (best) | pLDDT >85, pTM >0.8, low aggregation, neutral pI |
| T2 | pLDDT >75, pTM >0.7, acceptable developability |
| T3 | pLDDT >70, pTM >0.65, developability concerns |
| T4 | Failed validation or major developability issues |
Completeness Checklist
Reference Files
- DESIGN_PROCEDURES.md - Phase-by-phase code examples, sampling parameters, fallback chains
- TOOLS_REFERENCE.md - Complete tool documentation with code examples
- EXAMPLES.md - Sample design workflows and outputs
- CHECKLIST.md - Detailed phase checklists and quality metrics
- design_templates.md - Report templates and output format examples
