ReddRadar
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.
Install this agent skill to your Project
npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-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)
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
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.
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
FreedomIntelligence/OpenClaw-Medical-Skills
vcf-annotator
Annotate VCF variants with VEP, ClinVar, gnomAD frequencies, and ancestry-aware context. Generates prioritised variant reports.
FreedomIntelligence/OpenClaw-Medical-Skills
chemist-analyst
Analyzes events through chemistry lens using molecular structure, reaction mechanisms, thermodynamics, kinetics, and analytical techniques (spectroscopy, chromatography, mass spectrometry). Provides insights on chemical processes, material properties, reaction pathways, synthesis, and analytical methods. Use when: Chemical reactions, material analysis, synthesis planning, process optimization, environmental chemistry. Evaluates: Molecular structure, reaction mechanisms, yield, selectivity, safety, environmental impact.
FreedomIntelligence/OpenClaw-Medical-Skills
bio-alignment-io
Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO. Supports Clustal, PHYLIP, Stockholm, FASTA, Nexus, and other alignment formats for phylogenetics and conservation analysis. Use when reading, writing, or converting alignment file formats.
FreedomIntelligence/OpenClaw-Medical-Skills
sleep-analyzer
分析睡眠数据、识别睡眠模式、评估睡眠质量，并提供个性化睡眠改善建议。支持与其他健康数据的关联分析。
FreedomIntelligence/OpenClaw-Medical-Skills
metabolomics-workbench-database
Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.
FreedomIntelligence/OpenClaw-Medical-Skills
bio-hi-c-analysis-matrix-operations
Balance, normalize, and transform Hi-C contact matrices using cooler and cooltools. Apply iterative correction (ICE), compute expected values, and generate observed/expected matrices. Use when normalizing or transforming Hi-C matrices.
Didn't find tool you were looking for?