alphafold

# AlphaFold2 Structure Validation ## Prerequisites | 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) | ## How to run > **First time?** See [Installation Guide](../../docs/installation.md) to set up Modal and biomodals. ### Option 1: ColabFold (recommended for multimer) ```bash cd biomodals modal run modal_colabfold.py \ --input-faa sequences.fasta \ --out-dir output/ ``` **GPU**: A100 (40GB) | **Timeout**: 3600s default ### Option 2: Local installation ```bash 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 ``` ### Option 3: ESMFold (fast single-chain) ```bash modal run modal_esmfold.py \ --sequence "MKTAYIAKQRQISFVK..." ``` ## Key parameters | 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 format ``` 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 ``` ### Extracting metrics ```python 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'] ``` ## Sample output ### Successful run ``` $ 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:** - pLDDT: > 85 (mean, on 0-100 scale) or > 0.85 (normalized) - pTM: > 0.70 - ipTM: > 0.50 for complexes - PAE_interface: < 10 ## Decision tree ``` 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 ``` ## Typical performance | 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. --- ## Verify ```bash find output -name "ranked_0.pdb" | wc -l # Should match input count ``` --- ## Troubleshooting **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 interpretation | 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.