RoseTTAFold3 (RF3)

RoseTTAFold3 (RF3) is an all-atom biomolecular structure prediction model from the Institute for Protein Design at the University of Washington, distributed as part of the RosettaCommons Foundry framework. It predicts the joint 3D structure of complexes that combine proteins, DNA, RNA, and small-molecule ligands, with first-class support for chirality. This toolkit runs RF3 structure prediction from sequences, SMILES, and CCD codes, with optional ColabFold multiple-sequence alignments.

Institute for Protein Design

License: RoseTTAFold3 (RF3) is open source and free for academic and commercial use under a BSD-3-Clause license and may require explicit attribution when utilized. Please refer to the license for full terms.

Proto is not affiliated with Institute for Protein Design. This toolkit is open source and builds on the implementation produced by this organization. Product names, logos, and trademarks are the property of their respective owners.

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RosettaCommons/foundry

Central repository for biomolecular foundation models with shared trainers and pipeline components

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Accelerating Biomolecular Modeling with AtomWorks and RF3

Nathaniel Corley, Simon Mathis, … Frank DiMaio

bioRxiv (2025)

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@article{corley2025rf3,
  title={Accelerating Biomolecular Modeling with AtomWorks and RF3},
  author={Corley, Nathaniel and Mathis, Simon and Krishna, Rohith and Bauer, Magnus S. and Thompson, Tuscan R. and Ahern, Woody and Kazman, Maxwell W. and Brent, Rafael I. and Didi, Kieran and Kubaney, Andrew and McHugh, Lilian and Nagle, Arnav and Favor, Andrew and Kshirsagar, Meghana and Sturmfels, Pascal and Li, Yanjing and Butcher, Jasper and Qiang, Bo and Schaaf, Lars L. and Mitra, Raktim and Campbell, Katelyn and Zhang, Odin and Weissman, Roni and Humphreys, Ian R. and Cong, Qian and Funk, Jonathan and Sonthalia, Shreyash and Liò, Pietro and Baker, David and DiMaio, Frank},
  journal={bioRxiv},
  year={2025},
  doi={10.1101/2025.08.14.670328},
  publisher={Cold Spring Harbor Laboratory},
  url={https://www.biorxiv.org/content/10.1101/2025.08.14.670328v2}
}

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proto-bio/proto-tools/proto_tools/tools/structure_prediction/rf3

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Function	Description
run_rf3_prediction()	All-atom structure prediction with explicit chirality (RoseTTAFold3) (GPU)	Docs Source

Background

RoseTTAFold3 (Corley et al., 2025) predicts the joint 3D structure of a biomolecular assembly from the sequences and chemical components it contains. It is the latest entry in the RoseTTAFold lineage from the Baker and DiMaio labs at the Institute for Protein Design, and like AlphaFold3 and Boltz-2 it folds proteins, nucleic acids, and small-molecule ligands within a single model. The preprint reports that an improved treatment of chirality narrows the performance gap between RF3 and the closed-source AlphaFold3 on biomolecular benchmarks.

Architecturally RF3 builds on the new AtomWorks data framework introduced alongside it in the preprint, and uses an AlphaFold3 style trunk together with a diffusion module that samples several candidate structures per complex from random noise. The best sample is selected by a composite ranking score that combines interface pTM, overall pTM, and a clash penalty. Alongside the predicted coordinates, RF3 reports per-residue and overall pLDDT, per-chain confidence, predicted aligned error (PAE) and predicted distance error (PDE), chain-pair PAE and PDE matrices for multi-chain inputs, and a boolean flag for steric clashes.

The reference implementation is open-sourced as part of the RosettaCommons/foundry monorepo under the BSD-3-Clause license, with model weights served openly from the IPD file server. It was developed at the Institute for Protein Design (UW).

Tools

RoseTTAFold3 Prediction (rf3-prediction)

Predicts the 3D structure of a biomolecular complex. Each input complex can combine protein, DNA, RNA, and ligand chains. The assembly is folded by RF3 and returned as a predicted Structure per complex with confidence metrics, including average pLDDT, pTM, interface pTM for multi-chain inputs, per-chain pTM, an overall and chain-pair PAE and PDE in angstroms, a composite ranking score, and a steric-clash flag.

API Reference

Source

Input: RF3Input

complexes · List[Complex] · required

List of complexes to predict structures for. Inherited from StructurePredictionInput. Each complex can contain multiple chains of proteins, DNA, RNA, and/or ligands.

Show Complex

chains · List[Chain | Fragment] · required

Chains in the complex, in input order.

msas · array

Pre-computed MSAs, one entry per complex. Each entry is a ComplexMSAs (per-chain MSAs keyed by chain index); paired=True marks rows taxonomy-aligned across chains. Populated by preprocess() or supplied directly. Default: None.

Source

Config: RF3Config

n_recycles · integer · default: "10"

Iterative refinement passes through the network. Higher = more accurate but slower. Default 10 (upstream default).

diffusion_batch_size · integer · default: "5"

Independent diffusion samples drawn per complex; the best by ranking_score is returned. Default 5.

num_steps · integer · default: "50"

Denoising steps in the diffusion process. Default 50.

cyclic_chains · List[string]

Chain IDs (e.g. ["A"]) to mark as cyclic. Default [].

verbose · integer · default: "0"

Verbosity level (0=quiet, 1=info, 2=debug, 3=raw subprocess stderr). Inherited from BaseConfig. Default 0.

device · string · default: "cuda"

"cuda" or "cpu". Inherited. Default "cuda".

timeout · integer · default: "1800"

Maximum execution time in seconds. RF3 is heavier than Boltz2; the default is set accordingly. Default 1800.

seed · integer

Inherited. Default None.

include_pae_matrix · boolean · default: "False"

Inherited. Must remain False for RF3 (no per-token PAE matrix is emitted).

use_msa · boolean · default: "True"

Generate MSAs for protein chains via MMseqs2 homology search. Inherited from MSAStructurePredictionConfig. Default True.

msa_search_config · Mmseqs2HomologySearchConfig

Inherited. Default None.

pair_heterocomplex_msas · boolean · default: "True"

Use taxonomy-paired MSA generation for heterocomplex protein chains. Inherited. Default True.

Source

Output: RF3Output

structures · List[Structure] · required

Predicted structures, each carrying a :class:RF3Metrics instance on .metrics.

Show Structure

structure · string · required

Raw structure content in PDB or CIF format.

structure_format · string

Format of the content string (auto-detected if omitted).

b_factor_type · BFactorType

What the B-factor column represents.

source · string

Optional source identifier (filepath or tool name).

metrics · Metrics

Associated metrics (e.g., pLDDT, pTM scores, per-chain lists, pairwise matrices). None values are stripped at construction.

Metrics (one set per structures item)

Metric	Type	Range	Availability
avg_plddt	float	0.0 to 1.0	always
ptm	float	0.0 to 1.0	always
iptm	float	0.0 to 1.0	multi-chain input only
avg_pae	float	0.0 to 32.0	always
pde	float	0.0 to 32.0	always
ranking_score	float	unbounded	always
chain_ptm	list[float]	0.0 to 1.0	always
chain_pair_pae	list[list[float]]	0.0 to 32.0	always (empty list for single-chain inputs)
chain_pair_pae_min	list[list[float]]	0.0 to 32.0	always (empty list for single-chain inputs)
chain_pair_pde	list[list[float]]	0.0 to 32.0	always (empty list for single-chain inputs)
chain_pair_pde_min	list[list[float]]	0.0 to 32.0	always (empty list for single-chain inputs)
has_clash	bool	unbounded	always

Applications

This tool predicts the structure of multi-component assemblies such as protein-DNA and protein-RNA complexes or protein-ligand binding poses. Within this toolkit it is also the model whose architecture has explicit chirality representations built in, which is relevant when modelling chiral small molecules, D-amino-acid residues, or peptides where stereochemistry matters. For multi-chain inputs the reported chain-pair PAE and PDE matrices together with interface pTM estimate how confidently the components are placed relative to each other, useful for ranking or filtering predicted interfaces.

Usage Tips

• use_msa defaults to True. A ColabFold search generates an MSA for each protein chain. Set it False for single-sequence prediction, or attach precomputed MSAs to the input.
• Diffusion samples are ranked by ranking_score. diffusion_batch_size (default 5) independent samples are drawn per complex. The best by ranking_score = 0.8*iptm + 0.2*ptm - 100*has_clash is returned, with num_steps (default 50) controlling the denoising step count.
• n_recycles (default 10) trades accuracy for time. More recycling iterations refine the prediction at higher runtime. Leave the upstream default of 10 unless you have a specific reason to lower it.
• Cyclic chains. Mark chains as cyclic (head-to-tail) with cyclic_chains=["A", ...].
• No template or conformer conditioning. RF3 can condition on input coordinates (templates, holo ligand conformers), but this wrapper accepts only sequences, SMILES, and CCD codes — no coordinate input — so those upstream knobs are not exposed.
• No per-token PAE matrix. Unlike Boltz-2 and AlphaFold3, RF3 emits only chain-pair PAE aggregates (avg_pae, chain_pair_pae, chain_pair_pae_min) and a separate pde (predicted distance error). The inherited include_pae_matrix toggle is rejected by RF3Config.
• Multi-modal inputs. Protein, DNA, RNA, and ligand entities are supported.

Toolkit Notes

These apply to every RF3 tool in this toolkit (rf3-prediction).

• Requires a GPU. RF3 runs through a PyTorch backend and needs an NVIDIA GPU. CPU execution is not practical.
• Open weights. The RF3 checkpoint is downloaded automatically from the IPD file server during environment setup and lands in the proto-tools weights cache. No request form or token is required.
• Predictions are stochastic. Structures come from a diffusion process, so repeated runs vary unless sampling is seeded. The wrapper advances the seed per complex within a batch so duplicate inputs in one call still diversify.

Example notebook: See the full working example for a copy-paste-ready walkthrough.

Infrastructure Guides

The following guides cover how to run tools efficiently and at scale.

Tool Persistence

Keep a tool’s model warm across calls instead of reloading it every invocation.

Device Management

How GPUs are allocated to tools and how to target specific devices.

Parallel Execution

Fan a batch of inputs out across multiple GPUs.

Cloud Inference

Run tools on managed cloud infrastructure with no local setup.