SuperAPI AI Directory
Partner with us中文AI newsFavorites
Skillstooluniverse-protein-therapeutic-design

tooluniverse-protein-therapeutic-design

Agents & models

View repository

Updated 10-16 01:16

About this skill

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.

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

For iterative campaigns, conflicting predictions, glycosylated receptor checks, conditional pH binding, or competition preparation, use Protein Design Campaign alongside this workflow. Verify installed tool schemas before executing examples; model confidence and successful execution do not establish binding.

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 available Quality Metrics (pLDDT, pTM, MPNN score, binding prediction). Mark unavailable metrics unmeasured or uncomputed; do not fabricate binding predictions or experimental values.

NVIDIA NIM Tools

Tool Purpose Key Parameter
NvidiaNIM_rfdiffusion (requires NVIDIA_API_KEY env var; free key at build.nvidia.com) Backbone generation contigs + input_pdb (both required), diffusion_steps (NOT num_steps; default 15)
NvidiaNIM_proteinmpnn (requires NVIDIA_API_KEY env var; free key at build.nvidia.com) Sequence design input_pdb (required; NOT pdb or pdb_string), num_seq_per_target, sampling_temp (a list)
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 (plus required contigs and input_pdb)
NvidiaNIM_proteinmpnn (requires NVIDIA_API_KEY) pdb=content or pdb_string=content input_pdb=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

Folding and Developability Screening

These are illustrative computational screening tiers, not binding-evidence grades or calibrated success probabilities. Benchmark screening with appropriate controls. Monomer pLDDT/pTM cannot establish a complex pose, affinity, cross-species binding or pH selectivity. Report those dimensions separately, including missing evidence.

Tier Criteria
T1 (strongest in this screen) 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

  • 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