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-graded - Grade designs by confidence metrics
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.

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)

1. Create [TARGET]_protein_design_report.md first with section headers
2. Progressively update as designs are generated
3. 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

Common Parameter Mistakes

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

Evidence Grading

Completeness Checklist

• [ ] Target structure obtained (PDB or predicted)
• [ ] Binding epitope identified
• [ ] >= 5 backbones generated, top 3-5 selected
• [ ] >= 8 sequences per backbone, MPNN scores reported
• [ ] All sequences validated (ESMFold), pLDDT/pTM reported, >= 3 passing
• [ ] Developability assessed (aggregation, pI, expression)
• [ ] Ranked candidate list, FASTA file, experimental recommendations

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