adaptyvbio/protein-design-workflow
skills/protein-design-workflow/SKILL.md
End-to-end guidance for protein design pipelines. Use this skill when: (1) Starting a new protein design project, (2) Need step-by-step workflow guidance, (3) Understanding the full design pipeline, (4) Planning compute resources and timelines, (5) Integrating multiple design tools. For tool selection, use binder-design. For QC thresholds, use protein-qc.
$ install --global
skillsauthnpx skillsauth add adaptyvbio/protein-design-skills protein-design-workflowInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- protein-design-workflow
- description:
- >
- Use this skill when:
- (1) Starting a new protein design project,
- license:
- MIT
- category:
- orchestration
- tags:
- [guidance, pipeline, workflow]
Protein Design Workflow Guide
Standard binder design pipeline
Overview
Target Preparation --> Backbone Generation --> Sequence Design
| | |
v v v
(pdb skill) (rfdiffusion) (proteinmpnn)
| |
v v
Structure Validation --> Filtering
| |
v v
(alphafold/chai) (protein-qc)
Phase 1: Target preparation
1.1 Obtain target structure
# Download from PDB
curl -o target.pdb "https://files.rcsb.org/download/XXXX.pdb"
1.2 Clean and prepare
# Extract target chain
# Remove waters, ligands if needed
# Trim to binding region + 10A buffer
1.3 Select hotspots
- Choose 3-6 exposed residues
- Prefer charged/aromatic (K, R, E, D, W, Y, F)
- Check surface accessibility
- Verify residue numbering
Output: target_prepared.pdb, hotspot list
Phase 2: Backbone generation
Option A: RFdiffusion (diverse exploration)
# RFdiffusion runs from the official repo, not biomodals
python run_inference.py \
inference.input_pdb=target_prepared.pdb \
contigmap.contigs=[A1-150/0 70-100] \
ppi.hotspot_res=[A45,A67,A89] \
inference.num_designs=500
Option B: BindCraft (end-to-end)
modal run modal_bindcraft.py \
--input-pdb target_prepared.pdb \
--target-hotspot-residues "45,67,89" \
--number-of-final-designs 100
Output: 100-500 backbone PDBs
Phase 3: Sequence design
For RFdiffusion backbones
for backbone in backbones/*.pdb; do
modal run modal_ligandmpnn.py \
--input-pdb "$backbone" \
--params-str "--number_of_batches 8 --temperature 0.1"
done
Output: 8 sequences per backbone (800-4000 total)
Phase 4: Structure validation
Predict complexes
# Prepare FASTA with binder + target
# binder:target format for multimer
modal run modal_alphafold.py \
--input-fasta all_sequences.fasta \
--out-dir predictions/
Output: AF2 predictions with pLDDT, ipTM, PAE
Phase 5: Filtering and selection
Apply standard thresholds
import pandas as pd
# Load metrics
designs = pd.read_csv('all_metrics.csv')
# Filter
filtered = designs[
(designs['pLDDT'] > 0.85) &
(designs['ipTM'] > 0.50) &
(designs['PAE_interface'] < 10) &
(designs['scRMSD'] < 2.0) &
(designs['esm2_pll'] > 0.0)
]
# Rank by composite score
filtered['score'] = (
0.3 * filtered['pLDDT'] +
0.3 * filtered['ipTM'] +
0.2 * (1 - filtered['PAE_interface'] / 20) +
0.2 * filtered['esm2_pll']
)
top_designs = filtered.nlargest(50, 'score')
Output: 50-200 filtered candidates
Resource planning
Compute requirements
| Stage | GPU | Time (100 designs) | |-------|-----|-------------------| | RFdiffusion | A10G | 30 min | | ProteinMPNN | T4 | 15 min | | Chai / AlphaFold | A100 | 4-8 hours | | Filtering | CPU | 15 min |
Total timeline
- Small campaign (100 designs): 8-12 hours
- Medium campaign (500 designs): 24-48 hours
- Large campaign (1000+ designs): 2-5 days
Quality checkpoints
After backbone generation
- [ ] Visual inspection of diverse backbones
- [ ] Secondary structure present
- [ ] No clashes with target
After sequence design
- [ ] ESM2 PLL > 0.0 for most sequences
- [ ] No unwanted cysteines (unless intentional)
- [ ] Reasonable sequence diversity
After validation
- [ ] pLDDT > 0.85
- [ ] ipTM > 0.50
- [ ] PAE_interface < 10
- [ ] Self-consistency RMSD < 2.0 A
Final selection
- [ ] Diverse sequences (cluster if needed)
- [ ] Manufacturable (no problematic motifs)
- [ ] Reasonable molecular weight
Common issues
| Problem | Solution | |---------|----------| | Low ipTM | Check hotspots, increase designs | | Poor diversity | Higher temperature, more backbones | | High scRMSD | Backbone may be unusual | | Low pLDDT | Check design quality |
Advanced workflows
Multi-tool combination
- RFdiffusion for initial backbones
- Mosaic for gradient-based refinement
- ProteinMPNN diversification
- AF2 final validation
Iterative refinement
- Run initial campaign
- Analyze failures
- Adjust hotspots/parameters
- Repeat with insights
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