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.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill alphafold --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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_te
- Prerequisites
- How to run
- Option 1: ColabFold (recommended for multimer)
- Option 2: Local installation
- Option 3: ESMFold (fast single-chain)
- Key parameters
- Output format
- Extracting metrics
- Sample output
- Successful run
- Decision tree
- Typical performance
- Verify
- Troubleshooting
cd biomodals modal run modal_colabfold.py \ git clone https://github.com/deepmind/alphafold.git cd alphafold python run_alphafold.py \ modal run modal_esmfold.py \ find output -name "ranked_0.pdb" | wc -l # Should match input count
What does the alphafold skill do?
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.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill alphafold --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.