Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm. For QC thresholds, use protein-qc.
| Requirement | Minimum | Recommended |
|---|---|---|
| Python | 3.8+ | 3.10 |
| CUDA | 11.0+ | 12.0+ |
| GPU VRAM | 32GB | 40GB (A100) |
| RAM | 32GB | 64GB |
| Disk | 100GB | 500GB (for databases) |
First time? See Installation Guide to set up Modal and biomodals.
cd biomodals
modal run modal_colabfold.py \
--input-faa sequences.fasta \
--out-dir output/
GPU: A100 (40GB) | Timeout: 3600s default
git clone https://github.com/deepmind/alphafold.git
cd alphafold
python run_alphafold.py \
--fasta_paths=query.fasta \
--output_dir=output/ \
--model_preset=monomer \
--max_template_date=2026-01-01
modal run modal_esmfold.py \
--sequence "MKTAYIAKQRQISFVK..."
| Parameter | Default | Options | Description |
|---|---|---|---|
--model_preset | monomer | monomer/multimer | Model type |
--num_recycle | 3 | 1-20 | Recycling iterations |
--max_template_date | - | YYYY-MM-DD | Template cutoff |
--use_templates | True | True/False | Use template search |
output/
├── ranked_0.pdb # Best model
├── ranked_1.pdb # Second best
├── ranking_debug.json # Confidence scores
├── result_model_1.pkl # Full results
├── msas/ # MSA files
└── features.pkl # Input features
import pickle
with open('result_model_1.pkl', 'rb') as f:
result = pickle.load(f)
plddt = result['plddt']
ptm = result['ptm']
iptm = result.get('iptm', None) # Multimer only
pae = result['predicted_aligned_error']
$ python run_alphafold.py --fasta_paths complex.fasta --model_preset multimer
[INFO] Running MSA search...
[INFO] Running model 1/5...
[INFO] Running model 5/5...
[INFO] Relaxing structures...
Results:
ranked_0.pdb:
pLDDT: 87.3 (mean)
pTM: 0.78
ipTM: 0.62
PAE (interface): 8.5
Saved to output/
What good output looks like:
Should I use AlphaFold?
│
├─ What are you predicting?
│ ├─ Single protein → ESMFold (faster)
│ ├─ Protein-protein complex → AlphaFold/ColabFold ✓
│ ├─ Protein + ligand → Chai or Boltz
│ └─ Batch of sequences → ColabFold ✓
│
├─ What do you need?
│ ├─ Highest accuracy → AlphaFold/ColabFold ✓
│ ├─ Fast screening → ESMFold
│ └─ MSA-free prediction → Chai or ESMFold
│
└─ Which AF2 option?
├─ Local installation → Full control, slow setup
├─ ColabFold → Easier, MSA server
└─ Modal → Recommended for batch
| Campaign Size | Time (A100) | Cost (Modal) | Notes |
|---|---|---|---|
| 100 complexes | 1-2h | ~$8 | With MSA server |
| 500 complexes | 5-10h | ~$40 | Standard campaign |
| 1000 complexes | 10-20h | ~$80 | Large campaign |
Per-complex: ~30-60s with MSA server.
find output -name "ranked_0.pdb" | wc -l # Should match input count
Low pLDDT regions: May indicate disorder or poor design Low ipTM: Interface not confident, check hotspots High PAE off-diagonal: Chains may not interact OOM errors: Use ColabFold with MSA server instead
| Error | Cause | Fix |
|---|---|---|
RuntimeError: CUDA out of memory | Sequence too long | Use A100 or split prediction |
KeyError: 'iptm' | Running monomer on complex | Use multimer preset |
FileNotFoundError: database | Missing MSA databases | Use ColabFold MSA server |
TimeoutError | MSA search slow | Reduce num_recycles |
Next: protein-qc for filtering and ranking.