chailisted

Structure prediction using Chai-1, a foundation model for molecular structure. Use this skill when: (1) Predicting protein-protein complex structures, (2) Validating designed binders, (3) Predicting protein-ligand complexes, (4) Using the Chai API for high-throughput prediction, (5) Need an alternative to AlphaFold2. For QC thresholds, use protein-qc. For AlphaFold2 prediction, use alphafold. For ESM-based analysis, use esm.
junior1p/ProteinClaw · ★ 10 · AI & Automation · score 58

Install: claude install-skill junior1p/ProteinClaw

# Chai-1 Structure Prediction ## Prerequisites | Requirement | Minimum | Recommended | |-------------|---------|-------------| | Python | 3.10+ | 3.11 | | CUDA | 12.0+ | 12.1+ | | GPU VRAM | 24GB | 40GB (A100) | | RAM | 32GB | 64GB | ## How to run ### Option 1: Modal ```bash cd biomodals modal run modal_chai1.py \ --input-faa complex.fasta \ --out-dir predictions/ ``` ### Option 2: Chai API (recommended) ```bash pip install chai_lab python -c " from chai_lab.chai1 import run_inference run_inference(fasta_file='complex.fasta', output_dir='predictions/', num_trunk_recycles=3) " ``` ## FASTA Format ``` >binder MKTAYIAKQRQISFVKSHFSRQLE... >target MVLSPADKTNVKAAWGKVGAHAGE... ``` ### Protein + ligand ``` >protein MKTAYIAKQRQISFVKSHFSRQLE... >ligand|smiles CCO ``` ## Key parameters | Parameter | Default | Description | |-----------|---------|-------------| | `num_trunk_recycles` | 3 | Recycles (more = better) | | `num_diffn_timesteps` | 200 | Diffusion steps | ## Output format ``` predictions/ ├── pred.model_idx_0.cif # Best model ├── scores.json # pTM, ipTM, ranking_score ├── pae.npy # PAE matrix └── plddt.npy # pLDDT values ``` ## Chai vs AF2 | Aspect | Chai-1 | AlphaFold2 | |--------|--------|------------| | MSA required | No | Yes | | Small molecules | Yes | No | | Speed | Faster | Slower | | Accuracy | Comparable | Reference | ## Typical performance | Campaign | Time (A100) | Cost (Modal) | |----------|-----------