tt-bio 0.2.2

Boltz-2, ESMFold2, and Protenix-v2 structure prediction, BoltzGen binder design, and ESMC protein embeddings for inference on Tenstorrent Blackhole and Wormhole

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[!IMPORTANT] TT-Boltz is now TT-Bio

TT-Bio runs Boltz-2, ESMFold2, and Protenix-v2 structure prediction, BoltzGen binder design, and ESMC protein embeddings on Tenstorrent Blackhole and Wormhole, supporting single-card and multi-card configurations (e.g. QuietBox with 4 cards or Galaxy server with 32 cards). Multiple machines can also be combined into a single prediction run.

Installation

Create a Python virtual environment with Python 3.10 or 3.12, install, then install the matching Tenstorrent system dependencies.

python3.10 -m venv env
source env/bin/activate
pip install tt-bio
tt-bio install-deps

tt-bio install-deps installs the SFPI compiler version that matches the installed ttnn wheel and clears stale TT-Metal kernel cache entries. It may ask for your sudo password.

From GitHub / source

Pin to a tagged release, track nightly main (may be untested), or work from an editable clone:

pip install "tt-bio @ git+https://github.com/moritztng/tt-bio.git@v0.2.1"   # pinned release — see Releases for the latest
pip install "tt-bio @ git+https://github.com/moritztng/tt-bio.git@main"     # nightly
# or
git clone https://github.com/moritztng/tt-bio.git
cd tt-bio
pip install -e .
tt-bio install-deps

Optional: Build TT-Metal / TT-NN from Source

If you need to build from source, follow the Tenstorrent Installation Guide.

Verify Installation

tt-bio --help
tt-bio predict --help
tt-bio msa --help

Basic Usage

Structure Prediction

tt-bio predict examples/prot.yaml --model boltz2 --override

Every command names its model with --model:

• boltz2 — folds complexes of proteins, DNA, RNA, and ligands and predicts binding affinity. MSA-dependent (uses an MSA by default).
• esmfold2 / esmfold2-fast — fold a single protein sequence on-device, no MSA required (esmfold2-fast is the lighter, faster checkpoint):
• protenix-v2 — folds complexes of proteins, RNA, DNA, and ligands (an AlphaFold3-family model, the Protenix reproduction); MSA-dependent for proteins (uses an MSA by default), and also emits a PAE/PDE matrix with --write_pae:

tt-bio predict examples/prot.fasta --model esmfold2-fast --fast
tt-bio predict examples/prot.yaml --model protenix-v2   # MSA on by default; NA/ligand chains are single-sequence

Feature	Boltz-2	ESMFold2	Protenix-v2
Input	protein/DNA/RNA/ligand complex	single protein	protein/DNA/RNA/ligand complex
MSA	MSA-dependent (on by default)	single-sequence	proteins MSA-dependent (on by default), NA/ligand single-sequence
Affinity / potentials / templates	yes	no	no
Pocket / contact constraints	yes	no	no
Covalent bond constraints	yes	no	yes
PAE/PDE output (--write_pae/--write_pde)	no	no	yes

Shared across every model: --fast, --recycling_steps, --sampling_steps, --diffusion_samples, --output_format, the MSA flags, and the multi-card / multi-machine flags. Each model downloads its weights automatically on first use.

Boltz-2 and Protenix-v2 are MSA-dependent and use an MSA by default — a local ColabFold DB (~/.boltz/msa_db) if one is set up (see Offline MSA), otherwise the online ColabFold server. Sending sequences to the online server (api.colabfold.com) leaves your machine; a one-line notice is printed when that fallback is used. Pass --msa_db_path for a private offline database, or --single_sequence to deliberately fold without an MSA (lower accuracy; for batch-screening orphan sequences). ESMFold2 is single-sequence.

--fast makes some operations use block-fp8, a lower-precision numeric format that runs faster. Accuracy is typically very close.

predict accepts either a single YAML/FASTA file or a directory containing many input files.

A live display shows the progress of each protein. On a multi-card machine such as a QuietBox or Galaxy server, every card is used in parallel and labelled in the display (quietbox:tt0, quietbox:tt1, ...). Models load once per card and stay resident, so jobs flow through without per-protein reloads:

tt-bio predict proteins/ --model boltz2 --out_dir results --fast

If you have additional machines with Tenstorrent cards, you can add them to a single run — see Optional: Multi-Machine Prediction.

Protein Embeddings (ESMC)

Turn protein sequences into ESMC language-model embeddings on-device — no folding, no MSA. Point tt-bio embed at a FASTA file (or a directory of them):

tt-bio embed proteins.fasta --model esmc-600m --out_dir embeddings

--model selects the ESMC variant (esmc-300m, esmc-600m, esmc-6b). For each sequence you get its per-residue embeddings (one vector per amino acid) and a pooled whole-sequence vector (--pool mean/max/cls). Output is one <id>.npz per sequence (--format npz, default) or a single embeddings.parquet of the pooled vectors (--format parquet). Add --logits to also write the per-residue amino-acid predictions (300M/600M), and --fast for the block-fp8 weight path. Weights download automatically on first use.

Sequences batch automatically on 300M/600M (--batch_size, default 8) — a padded, length-bucketed device forward per batch, masked so results are identical to running each sequence alone.

To embed a large FASTA faster, shard it across several cards with --devices 0,1,2,3 — one worker per card, results reassembled in input order and identical to a single-card run:

tt-bio embed proteins.fasta --model esmc-600m --devices 0,1,2,3

The same capability is available from Python:

from tt_bio import esmc

emb = esmc.embed("MQIFVKTLTGKTITLEV...", model="esmc-600m")[0]
emb.per_residue   # [L, d_model]
emb.pooled        # [d_model]

# Shard a large set across cards (data-parallel, order preserved):
embs = esmc.embed(sequences, model="esmc-600m", devices=[0, 1, 2, 3])

Offline MSA (Optional)

Use this if you have enough disk and RAM and want local MSA. This avoids external MSA server calls and is faster for repeated runs.

tt-bio msa
tt-bio predict examples/prot.yaml --model boltz2 --override

tt-bio msa downloads UniRef30 to ~/.boltz/msa_db (~100GB download, ~500GB on disk after indexing). predict auto-detects this path.

To add EnvDB and use it in prediction: EnvDB can improve MSA coverage when UniRef30 hits are weak, at higher disk/RAM cost.

tt-bio msa --db all
tt-bio predict examples/prot.yaml --model boltz2 --use_envdb --override

Key Options:

• --override: Re-run from scratch, ignoring cached files
• --use_msa_server: Generate MSA via ColabFold API
• --msa_db_path: Use a local database at a custom path (e.g. --msa_db_path /data/colabfold_db)
• --use_envdb: Include EnvDB in offline MSA (tt-bio msa --db all)
• --accelerator=tenstorrent: Use Tenstorrent hardware (default, or use cpu/gpu)
• --fast: Makes some operations use block-fp8, a lower-precision numeric format that runs faster; accuracy is typically very close
• --debug: Show all raw output from the hardware and libraries instead of the progress display
• --debug --log: Same as --debug, but also print what each device is currently working on

Shared MSA Server (Optional)

Host the database on one machine and let others fetch MSAs from it over HTTP, so each prediction machine need not keep its own ~500GB copy.

# On the machine with the database:
tt-bio msa-server --listen 0.0.0.0:8765

# On any other machine (no local database needed):
tt-bio predict examples/prot.yaml --model protenix-v2 --msa_endpoint http://HOST:8765

The server runs the same offline colabfold_search and serves unpaired {hash}.a3m, with a shared cache and a search-concurrency cap (--max_concurrent). Add --token to require Authorization: Bearer <token>. --msa_endpoint applies to --model esmfold2/protenix-v2.

Binding Affinity Prediction (Boltz-2)

Predict binding affinity for protein-ligand complexes:

tt-bio predict examples/affinity.yaml --model boltz2 --use_msa_server --override --affinity_mw_correction

The --affinity_mw_correction flag applies molecular weight correction for more accurate predictions.

Input Format

ESMFold2 takes a plain protein FASTA or a YAML with one or more protein chains. The richer inputs below — ligands, affinity, DNA/RNA, constraints, and templates — are Boltz-2 features.

Create a YAML file describing your complex:

version: 1
sequences:
  - protein:
      id: A
      sequence: MVTPEGNVSLVDESLLVGVTDEDRAVRSAHQFYERLIGLWAPAVMEAAHELGVFAALAEAPADSGELARRLDCDARAMRVLLDALYAYDVIDRIHDTNGFRYLLSAEARECLLPGTLFSLVGKFMHDINVAWPAWRNLAEVVRHGARDTSGAESPNGIAQEDYESLVGGINFWAPPIVTTLSRKLRASGRSGDATASVLDVGCGTGLYSQLLLREFPRWTATGLDVERIATLANAQALRLGVEERFATRAGDFWRGGWGTGYDLVLFANIFHLQTPASAVRLMRHAAACLAPDGLVAVVDQIVDADREPKTPQDRFALLFAASMTNTGGGDAYTFQEYEEWFTAAGLQRIETLDTPMHRILLARRATEPSAVPEGQASENLYFQ
  - ligand:
      id: B
      smiles: 'N[C@@H](Cc1ccc(O)cc1)C(=O)O'
properties:
  - affinity:
      binder: B

Entity Types:

• Polymers: protein, dna, rna — provide sequence
• Ligands: ligand — provide smiles or ccd code

Multiple Identical Chains:

- protein:
    id: [A, B]  # Two identical chains
    sequence: ...

Understanding Results

Output Structure

boltz_results_prot/
├── structures/
│   ├── prot.cif                      # Best-ranked predicted structure
│   └── prot_model_1.cif              # Additional samples (if diffusion_samples > 1)
├── results.json                      # One entry per target with confidence/affinity metrics
├── power_profile.csv                 # (optional, --report-energy)
├── power_profile.png                 # (optional, --report-energy)
├── prot_pae.npz                      # (optional, --write_pae)
├── prot_pde.npz                      # (optional, --write_pde)
└── prot_embeddings.npz               # (optional, --write_embeddings)

MSA results are cached in <out_dir>/msa/ (default ./msa/), keyed by sequence hash. The same protein sequence is never searched twice, even across different input files or runs. The MSA search uses all available CPU threads and keeps the database index memory-mapped for maximum speed.

Confidence Scores

Each target entry in results.json contains confidence metrics. The fields below are Boltz-2's; Protenix-v2 reports the same confidence_score / ptm / iptm / plddt (and all_runs when --diffusion_samples > 1, ranked best-first), while an ESMFold2 entry instead carries plddt (mean, 0-1), ptm when available, and n_residues / n_chains.

{
    "id": "prot",
    "status": "ok",
    "confidence_score": 0.84,
    "ptm": 0.84,
    "iptm": 0.82,
    "complex_plddt": 0.84,
    "chains_ptm": {
        "0": 0.85,
        "1": 0.83
    },
    "pair_chains_iptm": {
        "0": {"0": 0.85, "1": 0.72},
        "1": {"0": 0.82, "1": 0.83}
    }
}

• confidence_score: Overall confidence (0-1, higher is better), calculated as 0.8 × complex_plddt + 0.2 × iptm. Models are ranked by this score
• ptm: Predicted TM-score for complex (0-1)
• iptm: Interface TM-score (0-1)
• complex_plddt: Average per-residue confidence (0-1)
• chains_ptm: Per-chain TM-scores (0-1)
• pair_chains_iptm: Per-chain-pair interface TM-scores (0-1)

Affinity Predictions

For affinity targets, the same results.json entry also contains:

{
    "affinity_pred_value": 2.47,
    "affinity_probability_binary": 0.41,
    "affinity_pred_value1": 2.55,
    "affinity_pred_value2": 2.19,
    "affinity_probability_binary1": 0.50,
    "affinity_probability_binary2": 0.42
}

• affinity_probability_binary: Probability of binding (0-1). Use for hit discovery (higher = more likely to bind)
• affinity_pred_value: Predicted binding affinity as log10(IC50) in μM. Use for ligand optimization (lower = stronger binding). Only compare between known active molecules
• affinity_pred_value1, affinity_pred_value2: Individual model predictions for binding affinity
• affinity_probability_binary1, affinity_probability_binary2: Individual model predictions for binding probability

Advanced Usage

Input Format Details

Proteins with Custom MSA

- protein:
    id: A
    sequence: MVTPEGNVSLVDES...
    msa: ./path/to/msa.a3m

Proteins with Modifications

- protein:
    id: A
    sequence: MVTPEGNVSLVDES...
    modifications:
      - position: 5
        ccd: PTR  # Modified residue code

Ligands

- ligand:
    id: B
    smiles: 'CC1=CC=CC=C1'  # SMILES string
    # OR
    ccd: ATP                # CCD code

Constraints

Pocket and contact constraints are Boltz-2 only (they need a trained constraint embedder). Covalent bond constraints work with Boltz-2 and Protenix-v2.

Pocket Constraints (binding site):

constraints:
  - pocket:
      binder: B              # Ligand chain
      contacts: [[A, 10], [A, 11], [A, 12]]  # Binding site residues
      max_distance: 6.0      # Angstroms (4-20A, default 6A)
      force: false           # Use potential to enforce (default: false)

Contact Constraints:

constraints:
  - contact:
      token1: [A, 10]
      token2: [A, 50]
      max_distance: 8.0
      force: false

Bond Constraints (covalent link — e.g. a covalent inhibitor, glycosylation, or disulfide; works with Boltz-2 and Protenix-v2):

constraints:
  - bond:
      atom1: [A, 10, SG]     # [chain, residue, atom]
      atom2: [B, 1, C12]     # ligand atom by name; polymer atoms by residue

Templates

Use experimental structures as templates:

templates:
  - cif: ./template.cif
    chain_id: A
    template_id: A
    force: true              # Enforce template alignment
    threshold: 2.0           # Max deviation in Angstroms

Command-Line Options

Options apply to every model unless tagged (Boltz-2).

Common Options:

Option	Default	Description
--model	boltz2	boltz2, esmfold2, esmfold2-fast (single-sequence ESMFold2), or protenix-v2 (AlphaFold3-family folder; protein / RNA / DNA / ligand complexes)
--out_dir	./	Output directory
--cache	~/.boltz	(Boltz-2) model cache directory; ESMFold2 uses the Hugging Face cache
--accelerator	tenstorrent	(Boltz-2) tenstorrent, cpu, or gpu; ESMFold2 always runs on Tenstorrent
--recycling_steps	3	Number of recycling iterations
--sampling_steps	200	Diffusion sampling steps
--diffusion_samples	1	Number of structure samples
--output_format	cif	cif or pdb
--override	False	Re-run from scratch
--use_msa_server	auto	Use online ColabFold API for MSAs. Auto-enabled for Boltz-2/Protenix-v2 when no local DB is found; ignored by ESMFold2 unless opted in
--single_sequence	False	(Boltz-2/Protenix-v2) Fold without an MSA (skips local DB and online server); lower accuracy
--msa_endpoint	—	(ESMFold2/Protenix-v2) Fetch MSAs from a tt-bio msa-server at this URL instead of searching locally
--use_potentials	False	(Boltz-2) Apply physical constraints
--affinity_mw_correction	False	(Boltz-2) Apply MW correction to affinity
--num_devices	0	Number of TT devices (0=all available)
--device_ids	—	Comma-separated TT device IDs (e.g. 0,2)
--fast	False	Makes some operations use block-fp8, a lower-precision numeric format that runs faster; accuracy is typically very close
--listen	—	Accept worker connections from other machines; see Multi-Machine Prediction
--report-energy	False	(Boltz-2) Enables optional energy profiling for one TT device (requires tt-mgmt add-on); writes power_profile.csv and power_profile.png
--energy-metric	both	(Boltz-2) Choose power channel(s): tdp, input, or both
--energy-sample-hz	20.0	(Boltz-2) Sampling rate in Hz for both power_w and input_power_w channels

Affinity-Specific Options (Boltz-2):

Option	Default	Description
--sampling_steps_affinity	200	Sampling steps for affinity
--diffusion_samples_affinity	5	Number of affinity samples

MSA Options (Boltz-2 / Protenix-v2 use an MSA by default; ESMFold2 only when you opt in):

Option	Default	Description
--msa_db_path	auto-detect	Path to local ColabFold database (~/.boltz/msa_db if present)
--use_envdb	False	Also search environmental database
--use_msa_server	auto	Use ColabFold API for MSA (auto-enabled when no local DB is found)
--single_sequence	False	Fold without an MSA (Boltz-2/Protenix-v2)
--msa_server_url	https://api.colabfold.com	MSA server URL
--msa_pairing_strategy	greedy	greedy or complete
--max_msa_seqs	8192	Maximum MSA sequences
--subsample_msa	False	Subsample MSA
--num_subsampled_msa	1024	Number of subsampled sequences

MSA Database Setup Options:

Option	Default	Description
--db	uniref30	uniref30 (~500GB), envdb (~800GB), or all
--path	~/.boltz/msa_db	Where to store the databases
--install-tools	True	Auto-install missing mmseqs/colabfold_search

MSA Server Authentication

For --use_msa_server:

Basic Authentication:

export BOLTZ_MSA_USERNAME=myuser
export BOLTZ_MSA_PASSWORD=mypassword
tt-bio predict ... --model boltz2 --use_msa_server

API Key Authentication:

export MSA_API_KEY_VALUE=your-api-key
tt-bio predict ... --model boltz2 --use_msa_server

Optional: Multi-Machine Prediction

Combine the cards across any mix of Tenstorrent machines — a workstation, one or more QuietBoxes, one or more Galaxy servers — into a single run.

On the machine driving the run:

tt-bio predict ./proteins --model boltz2 --listen 8765 --use_msa_server --fast

On every additional machine, replace HOST with the driving machine's hostname or IP:

tt-bio worker --connect http://HOST:8765

Optional: Energy Measurement (Boltz-2)

Use --report-energy to profile energy during prediction:

tt-bio predict examples/686.yaml --model boltz2 --override --device_ids 0 --report-energy --energy-metric both --energy-sample-hz 5

Behavior:

• Select metric channel(s) with --energy-metric (tdp, input, both)
• Uses one sampling rate (--energy-sample-hz, default 20 Hz)
• Supports only Tenstorrent runs with one selected device
• Records two power channels when available:
  • power_w: tt-mgmt UMD telemetry power (TDP channel)
  • input_power_w: tt-mgmt UMD telemetry input power
• Requires optional tt-mgmt installation:
  • git clone --recursive https://github.com/aperezvicente-TT/tt-mgmt.git
  • pip install -e ./tt-mgmt
• Prints energy summary metrics for selected channels
• Always writes:
  • power_profile.csv
  • power_profile.png

BoltzGen

BoltzGen designs protein binders against a target. The pipeline runs design → inverse folding → folding → analysis → filtering and writes the top-ranked binders to <output>/final_ranked_designs/.

tt-bio gen run examples/binder.yaml --num_designs 10

This automatically uses every available card (splitting the designs across them and merging the results) and writes to ./binder/. Add --device_ids 0,2 to run on specific cards only.

Input Format

entities:
  - protein:
      id: B
      sequence: 80..120         # designed chain, sampled length per design
  - file:
      path: target.cif          # target structure (path relative to this yaml)
      include:
        - chain:
            id: A

80..120 randomises the binder length per design; a fixed integer pins it. Ligand, DNA, and RNA targets use the same YAML grammar as tt-bio predict. See the BoltzGen examples for binding sites, scaffolds, and residue constraints.

Protocols

--protocol sets defaults appropriate for the binder type.

Protocol	Use for
protein-anything (default)	de-novo protein binder
peptide-anything	peptide binder
nanobody-anything	nanobody / VHH
antibody-anything	antibody
protein-small_molecule	binder against a small-molecule target (adds affinity step)
protein-redesign	re-design existing residues (e.g. symmetric dimers)

Running a Subset

--steps restricts the pipeline.

tt-bio gen run examples/binder.yaml --steps design --num_designs 10
tt-bio gen run examples/binder.yaml --output existing/ --steps analysis filtering

Command-Line Options

Option	Default	Description
--protocol	protein-anything	Protocol; sets defaults appropriate for the binder type
--num_designs	10000	Number of binders to generate
--budget	30	Number of top designs kept after filtering
--output	./<basename>/	Output directory
--steps	(all)	Run only specific stages
--config STEP key=val	—	Override per-stage config (e.g. --config design sampling_steps=200)
--device_ids	all cards	Restrict to specific cards (e.g. 0,2)
--fast	False	Use block-fp8 for some ops (slightly lower precision, faster)
--cache	~/.boltz/boltzgen	Cache for downloaded weights
--debug	False	Disable live display; show raw stage output
--debug --log	False	Add per-stage progress markers

Cite

If you use this code or the models in your research, please cite the following papers:

@article{passaro2025boltz2,
  author = {Passaro, Saro and Corso, Gabriele and Wohlwend, Jeremy and Reveiz, Mateo and Thaler, Stephan and Somnath, Vignesh Ram and Getz, Noah and Portnoi, Tally and Roy, Julien and Stark, Hannes and Kwabi-Addo, David and Beaini, Dominique and Jaakkola, Tommi and Barzilay, Regina},
  title = {Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction},
  year = {2025},
  doi = {10.1101/2025.06.14.659707},
  journal = {bioRxiv}
}

@article{stark2025boltzgen,
  author = {Stark, Hannes and Faltings, Felix and Choi, MinGyu and Xie, Yuxin and Hur, Eunsu and O'Donnell, Timothy John and Bushuiev, Anton and U{\c c}ar, Talip and Passaro, Saro and Mao, Weian and Reveiz, Mateo and Bushuiev, Roman and Pluskal, Tom{\'a}{\v s} and Sivic, Josef and Kreis, Karsten and Vahdat, Arash and Ray, Shamayeeta and Goldstein, Jonathan T. and Savinov, Andrew and Hambalek, Jacob A. and Gupta, Anshika and Taquiri-Diaz, Diego A. and Zhang, Yaotian and Hatstat, A. Katherine and Arada, Angelika and Kim, Nam Hyeong and Tackie-Yarboi, Ethel and Boselli, Dylan and Schnaider, Lee and Liu, Chang C. and Li, Gene-Wei and Hnisz, Denes and Sabatini, David M. and DeGrado, William F. and Wohlwend, Jeremy and Corso, Gabriele and Barzilay, Regina and Jaakkola, Tommi},
  title = {BoltzGen: Toward Universal Binder Design},
  year = {2025},
  doi = {10.1101/2025.11.20.689494},
  journal = {bioRxiv}
}

@article{wohlwend2024boltz1,
  author = {Wohlwend, Jeremy and Corso, Gabriele and Passaro, Saro and Getz, Noah and Reveiz, Mateo and Leidal, Ken and Swiderski, Wojtek and Atkinson, Liam and Portnoi, Tally and Chinn, Itamar and Silterra, Jacob and Jaakkola, Tommi and Barzilay, Regina},
  title = {Boltz-1: Democratizing Biomolecular Interaction Modeling},
  year = {2024},
  doi = {10.1101/2024.11.19.624167},
  journal = {bioRxiv}
}

@misc{candido2026language,
  author = {Candido, Salvatore and Hayes, Thomas and Derry, Alexander and Rao, Roshan and Lin, Zeming and Verkuil, Robert and others},
  title = {Language Modeling Materializes a World Model of Protein Biology},
  year = {2026},
  url = {https://biohub.ai/papers/esm_protein.pdf},
  note = {Preprint; ESMC / ESMFold2}
}

@misc{protenix2025,
  author = {{ByteDance AML AI4Science Team}},
  title = {Protenix: An AlphaFold3 Reproduction for Biomolecular Structure Prediction},
  year = {2025},
  url = {https://github.com/bytedance/Protenix}
}

In addition if you use the automatic MSA generation, please cite:

@article{mirdita2022colabfold,
  title={ColabFold: making protein folding accessible to all},
  author={Mirdita, Milot and Sch{\"u}tze, Konstantin and Moriwaki, Yoshitaka and Heo, Lim and Ovchinnikov, Sergey and Steinegger, Martin},
  journal={Nature methods},
  year={2022}
}

License

tt-bio is released under the MIT License (see LICENSE) and is built on the MIT-licensed Boltz-2 / Boltz-1 code. It bundles third-party code, each under its upstream license: the ESMFold2 host-side reference under tt_bio/_vendor/ (the esm pipeline, MIT, © Chan Zuckerberg Biohub; and the HuggingFace ESMFold2 model definition, Apache-2.0) and the BoltzGen binder-design source under tt_bio/boltzgen/ (MIT, © Hannes Stärk). Protenix-v2 is an independent ttnn reimplementation — no upstream code is vendored — and its weights download from ByteDance's Hugging Face mirror under Apache-2.0. See NOTICE for sources, versions, and modifications.