| --- |
| tags: |
| - Biology |
| - Protein |
| - Protein Design |
| - Structure Prediction |
| license: agpl-3.0 |
| library_name: ofoldx |
| pipeline_tag: other |
| --- |
| |
| # RFdiffusion |
|
|
| Pre-trained model for protein backbone generation from contig specifications and optional structural motifs. |
|
|
| ## Disclaimer |
|
|
| This is an UNOFFICIAL implementation of the [De novo design of protein structure and function with RFdiffusion](https://doi.org/10.1038/s41586-023-06415-8) by Joseph L. Watson, et al. |
|
|
| The OFFICIAL repository of RFdiffusion is at [RosettaCommons/RFdiffusion](https://github.com/RosettaCommons/RFdiffusion). |
|
|
| **The team releasing RFdiffusion did not write this model card for this model so this model card has been written by the OFoldX team.** |
|
|
| ## Model Details |
|
|
| RFdiffusion reverses diffusion over protein residue frames with a RoseTTAFold-style denoiser and SE(3)-equivariant |
| structure updates. Its sampler couples Gaussian C-alpha translation diffusion with IGSO(3) rotation diffusion. |
| Sequence, MSA, pair, template, and timestep features provide model-specific conditioning. |
|
|
| Please refer to the [Training Details](#training-details) section for more information on the training process. |
|
|
| ### Variants |
|
|
| - **[oteam/rfdiffusion-base](https://huggingface.co/oteam/rfdiffusion-base)**: De novo generation and motif scaffolding (`Base_ckpt.pt`). |
| - **[oteam/rfdiffusion-base-epoch8](https://huggingface.co/oteam/rfdiffusion-base-epoch8)**: The earlier base checkpoint (`Base_epoch8_ckpt.pt`). |
| - **[oteam/rfdiffusion-complex-base](https://huggingface.co/oteam/rfdiffusion-complex-base)**: Complex and binder design (`Complex_base_ckpt.pt`). |
| - **[oteam/rfdiffusion-complex-beta](https://huggingface.co/oteam/rfdiffusion-complex-beta)**: Topology-diverse, beta-rich binder design (`Complex_beta_ckpt.pt`). |
| - **[oteam/rfdiffusion-complex-fold-base](https://huggingface.co/oteam/rfdiffusion-complex-fold-base)**: Fold-conditioned complex design (`Complex_Fold_base_ckpt.pt`). |
| - **[oteam/rfdiffusion-inpaint-seq](https://huggingface.co/oteam/rfdiffusion-inpaint-seq)**: Sequence-conditioned inpainting (`InpaintSeq_ckpt.pt`). |
| - **[oteam/rfdiffusion-inpaint-seq-fold](https://huggingface.co/oteam/rfdiffusion-inpaint-seq-fold)**: Fold-conditioned inpainting (`InpaintSeq_Fold_ckpt.pt`). |
| - **[oteam/rfdiffusion-active-site](https://huggingface.co/oteam/rfdiffusion-active-site)**: Active-site motif scaffolding (`ActiveSite_ckpt.pt`). |
|
|
| ### Model Specification |
|
|
| | Variant | Main Blocks | Extra Blocks | Refinement Blocks | MSA Size | Pair Size | Diffusion Timesteps | Num Parameters (M) | |
| | -------------------------------------- | ----------- | ------------ | ----------------- | -------- | --------- | ------------------- | ------------------ | |
| | Base, complex, inpaint and active-site | 32 | 4 | 4 | 256 | 128 | 50 | 59.879 | |
| | Fold-conditioned | 32 | 4 | 4 | 256 | 128 | 50 | 59.880 | |
|
|
| ### Links |
|
|
| - **Code**: [ofoldx.models.rfdiffusion](https://github.com/OTeam-AI4S/OFoldX/tree/main/ofoldx/models/rfdiffusion) |
| - **Paper**: |
| [De novo design of protein structure and function with RFdiffusion](https://doi.org/10.1038/s41586-023-06415-8) |
| - **Developed by**: Joseph L. Watson, David Juergens, Nathaniel R. Bennett, Brian L. Trippe, Jason Yim, Helen E. |
| Eisenach, Woody Ahern, Andrew J. Borst, Robert J. Ragotte, Lukas F. Milles, Basile I. M. Wicky, Nikita Hanikel, Samuel |
| J. Pellock, Alexis Courbet, William Sheffler, Jue Wang, Preetham Venkatesh, Isaac Sappington, Susana Vázquez Torres, |
| Anna Lauko, Valentin De Bortoli, Emile Mathieu, Sergey Ovchinnikov, Regina Barzilay, Tommi S. Jaakkola, Frank DiMaio, |
| Minkyung Baek, and David Baker |
| - **Model type**: SE(3)-equivariant protein backbone diffusion model |
| - **Original Repository**: [RosettaCommons/RFdiffusion](https://github.com/RosettaCommons/RFdiffusion) |
|
|
| ## Usage |
|
|
| The model file depends on the [`ofoldx`](https://ofoldx.valtech.ai/) library. You can install it using pip: |
|
|
| ```bash |
| pip install ofoldx |
| ``` |
|
|
| ### Direct Use |
|
|
| #### Backbone Design |
|
|
| You can use this model directly with a pipeline for 100-residue protein backbone generation: |
|
|
| ```python |
| import ofoldx |
| from ofoldx.models.conditions import GeneratedSegment |
| from ofoldx.models.rfdiffusion import RfDiffusionPlacements |
| |
| placement = RfDiffusionPlacements({"binder": {"binder": GeneratedSegment("protein", 100, 100)}}) |
| pipeline = ofoldx.pipeline("design", model="oteam/rfdiffusion-base") |
| result = pipeline(None, placement=placement, seed=7) |
| ``` |
|
|
| Generated residues have the unknown identity `UNK`. Use a sequence-design model such as ProteinMPNN to assign amino acids. |
|
|
| ## Training Details |
|
|
| RFdiffusion is trained to recover protein backbones from noisy coordinates with optional structural conditioning. |
|
|
| ### Training Data |
|
|
| RFdiffusion is fine-tuned from RoseTTAFold on protein structures from the [PDB](https://www.rcsb.org/), including |
| monomers and protein complexes. |
|
|
| ### Training Procedure |
|
|
| RFdiffusion learns to denoise protein residue frames with self-conditioning across diffusion steps. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{watson2023denovo, |
| author = {Watson, Joseph L. and Juergens, David and Bennett, Nathaniel R. and others}, |
| title = {De novo design of protein structure and function with RFdiffusion}, |
| journal = {Nature}, |
| volume = {620}, |
| number = {7976}, |
| pages = {1089--1100}, |
| year = {2023}, |
| doi = {10.1038/s41586-023-06415-8} |
| } |
| ``` |
|
|
| > [!NOTE] |
| > The artifacts distributed in this repository are part of the OFoldX project. |
| > If OFoldX supports your research, please cite the OFoldX project as follows: |
|
|
| ```bibtex |
| @software{chen2026ofoldx, |
| author = {Chen, Zhiyuan}, |
| title = {OFoldX}, |
| year = {2026}, |
| url = {https://github.com/OTeam-AI4S/OFoldX}, |
| } |
| ``` |
|
|
| ## Contact |
|
|
| Please use GitHub issues of [OFoldX](https://github.com/OTeam-AI4S/OFoldX/issues) for any questions or comments on the model implementation or model card. |
|
|
| Please contact the authors of the [RFdiffusion paper](https://doi.org/10.1038/s41586-023-06415-8) for questions or comments on the paper/model. |
|
|
| ## License |
|
|
| The model weights, implementation, configuration, and documentation distributed by OFoldX are licensed under the |
| [GNU Affero General Public License](https://github.com/OTeam-AI4S/OFoldX/blob/main/license.md). |
|
|
| For additional terms and clarifications, please refer to our |
| [License FAQ](https://github.com/OTeam-AI4S/OFoldX/blob/main/license-faq.md). |
|
|
| ```spdx |
| SPDX-License-Identifier: AGPL-3.0-or-later |
| ``` |
|
|
