/alphafold

name: alphafold
description: >
  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.
license: MIT
category: design-tools
tags: [structure-prediction, validation, reference]
biomodals_script: modal_alphafold.py

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)

cd biomodals
modal run modal_colabfold.py \
  --input-faa sequences.fasta \
  --out-dir output/

**GPU**: A100 (40GB) | **Timeout**: 3600s default

Option 2: Local installation

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)

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

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

find output -name "ranked_0.pdb" | wc -l  # Should match input count

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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 |

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**Next**: `protein-qc` for filtering and ranking.