boltzlisted

# Boltz Structure Prediction ## Prerequisites | Requirement | Minimum | Recommended | |-------------|---------|-------------| | Python | 3.10+ | 3.11 | | CUDA | 12.0+ | 12.1+ | | GPU VRAM | 24GB | 48GB (L40S) | | RAM | 32GB | 64GB | ## How to run > **First time?** See [Installation Guide](../../docs/installation.md) to set up Modal and biomodals. ### Option 1: Modal ```bash cd biomodals modal run modal_boltz.py \ --input-faa complex.fasta \ --out-dir predictions/ ``` **GPU**: L40S (48GB) | **Timeout**: 1800s default ### Option 2: Local installation ```bash pip install boltz boltz predict \ --fasta complex.fasta \ --output predictions/ ``` ## Key parameters | Parameter | Default | Range | Description | |-----------|---------|-------|-------------| | `--recycling_steps` | 3 | 1-10 | Recycling iterations | | `--sampling_steps` | 200 | 50-500 | Diffusion steps | | `--use_msa_server` | true | bool | Use MSA server | ## FASTA Format ``` >protein_A MKTAYIAKQRQISFVK... >protein_B MVLSPADKTNVKAAWG... ``` ## Output format ``` predictions/ ├── model_0.cif # Best model (CIF format) ├── confidence.json # pLDDT, pTM, ipTM └── pae.npy # PAE matrix ``` **Note**: Boltz outputs CIF format. Convert to PDB if needed: ```python from Bio.PDB import MMCIFParser, PDBIO parser = MMCIFParser() structure = parser.get_structure("model", "model_0.cif") io = PDBIO() io.set_structure(structure) io.save("model_0.pdb") ``` ## Comparison | Feature | Boltz-1 | Boltz-2