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

How to run

First time? See Installation Guide 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

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

Per-complex: ~30-60s with MSA server.

Verify

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

Next: protein-qc for filtering and ranking.