Boltz-2

Boltz-2 is a diffusion-based protein structure prediction model with built-in MSA (Multiple Sequence Alignment) support. It produces high-quality 3D structures with comprehensive confidence metrics and supports MSA caching for faster repeated predictions.

Quick example

from boileroom import Boltz2

model = Boltz2(backend="modal")
result = model.fold("MKTVRQERLKSIVRI", options={"include_fields": ["plddt", "confidence"]})

print(result.atom_array)  # list of Biotite AtomArray objects
print(result.plddt)       # per-residue confidence scores
print(result.confidence)  # comprehensive confidence metrics

Methods

`.fold()`

Predict the 3D structure of one or more protein sequences.

result = model.fold(sequences, options=None)

str | Sequence[str]

required

A single amino acid sequence string or a list of sequences. Use ":" to separate chains in a multimer (e.g., "CHAIN_A:CHAIN_B").

dict | None

default:"None"

Per-call configuration overrides. Only dynamic config keys can be set here — static keys raise ValueError. See Configuration.

Returns: Boltz2Output (see Output below)

Output

The Boltz2Output dataclass returned by .fold().

Always included

PredictionMetadata

Prediction metadata with timing information. See PredictionMetadata.

list[AtomArray] | None

List of Biotite AtomArray objects (one per diffusion sample). Always generated.

Confidence metrics

list[dict] | None

Comprehensive confidence metrics (one dict per sample). Each dict may contain keys like confidence_score, ptm, iptm, ligand_iptm, protein_iptm, complex_plddt, complex_iplddt, complex_pde, complex_ipde, pair_chains_iptm, and chains_ptm.

list[np.ndarray] | None

Per-residue pLDDT scores (one 1D array per sample).

list[np.ndarray] | None

Predicted aligned error matrices (one 2D square matrix per sample).

list[np.ndarray] | None

Predicted distance error matrices (one 2D square matrix per sample).

Structure representations

list[str] | None

PDB-formatted structure strings. Only generated when include_fields contains "pdb" or "*".

list[str] | None

mmCIF-formatted structure strings. Only generated when include_fields contains "cif" or "*".

Configuration

These keys can be set via config={} at initialization or options={} per call (unless marked static).

Core settings

Key	Type	Default	Static	Description
`device`	`str`	`"cuda:0"`	Yes	GPU device identifier
`cache_dir`	`str \| None`	`None`	Yes	Directory for model weights and resources. Defaults to `{MODEL_DIR}/boltz`
`no_kernels`	`bool`	`False`	Yes	Disable custom CUDA kernels (use for CPU or debugging)
`seed`	`int \| None`	`None`	No	Random seed for reproducibility
`override`	`bool`	`False`	No	Override existing output files
`num_workers`	`int`	`2`	No	Number of data loading workers
`include_fields`	`list[str] \| None`	`None`	No	Which output fields to return. Use `["pdb"]` or `["cif"]` to include structure strings. Use `["*"]` for everything

Diffusion settings

Key	Type	Default	Static	Description
`recycling_steps`	`int`	`3`	No	Number of recycling iterations
`sampling_steps`	`int`	`200`	No	Number of diffusion sampling steps
`diffusion_samples`	`int`	`1`	No	Number of structure samples to generate
`max_parallel_samples`	`int`	`5`	No	Maximum number of samples to run in parallel
`step_scale`	`float`	`1.5`	No	Step scale factor for diffusion

MSA settings

Key	Type	Default	Static	Description
`use_msa_server`	`bool`	`True`	No	Whether to query the MSA server. Set to `False` for single-sequence mode
`msa_server_url`	`str`	`"https://api.colabfold.com"`	No	URL of the MSA server
`msa_pairing_strategy`	`str`	`"greedy"`	No	Strategy for pairing MSA sequences
`max_msa_seqs`	`int`	`8192`	No	Maximum number of MSA sequences to use
`subsample_msa`	`bool`	`True`	No	Whether to subsample MSAs
`num_subsampled_msa`	`int`	`1024`	No	Number of subsampled MSA sequences

Output settings

Key	Type	Default	Static	Description
`write_full_pae`	`bool`	`True`	No	Compute and include full PAE matrix
`write_full_pde`	`bool`	`True`	No	Compute and include full PDE matrix
`write_embeddings`	`bool`	`False`	No	Include model embeddings in output

MSA caching

Key	Type	Default	Static	Description
`msa_cache_enabled`	`bool`	`True`	No	Enable or disable MSA caching

MSA caching

Boltz-2 automatically caches MSA results to avoid redundant server queries. When you fold a sequence for the first time, the MSA is fetched from the server and stored locally. Subsequent predictions for the same sequence reuse the cached MSA. Cache files are stored in a hash-prefixed directory structure under {cache_dir}/boltz/msa_cache/. An index file (msa_index.json) tracks cached entries with metadata including creation time, last access time, and file size. To disable caching, set config={"msa_cache_enabled": False}.

Multimer prediction

Separate chains with ":" in the sequence string:

result = model.fold(
    "MKTVRQERLKSIVRI:LERSKEPVSGAQLAEE",
    options={"include_fields": ["confidence"]},
)

print(result.atom_array)   # structure with both chains
print(result.confidence)   # includes inter-chain metrics

Single-sequence mode

To run without MSA (faster but potentially lower quality):

model = Boltz2(backend="modal", config={"use_msa_server": False})
result = model.fold("MKTVRQERLKSIVRI")

Overview

Folding Models

Embedding Models

Reference

Development

Quick example

Methods

`.fold()`

Output

Always included

Confidence metrics

Structure representations

Configuration

Core settings

Diffusion settings

MSA settings

Output settings

MSA caching

MSA caching

Multimer prediction

Single-sequence mode

​Quick example

​Methods

​.fold()

​Output

​Always included

​Confidence metrics

​Structure representations

​Configuration

​Core settings

​Diffusion settings

​MSA settings

​Output settings

​MSA caching

​MSA caching

​Multimer prediction

​Single-sequence mode

Quick example

Methods

`.fold()`

Output

Always included

Confidence metrics

Structure representations

Configuration

Core settings

Diffusion settings

MSA settings

Output settings

MSA caching

MSA caching

Multimer prediction

Single-sequence mode