← ClaudeAtlas

alphafoldlisted

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.
BioTender-max/awesome-bio-agent-skills · ★ 58 · AI & Automation · score 80
Install: claude install-skill BioTender-max/awesome-bio-agent-skills
# 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_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 ├─