Agent skill

alphafold

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.

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Install this agent skill to your Project

npx add-skill https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/alphafold

SKILL.md

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 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.

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