| --- |
| tags: |
| - Biology |
| - Protein Design |
| - Side-Chain Packing |
| - Protein-Ligand |
| license: agpl-3.0 |
| library_name: ofoldx |
| pipeline_tag: other |
| --- |
| |
| # MPNN Packer |
|
|
| Pre-trained model for protein side-chain packing conditioned on a fixed backbone, sequence, and optional atomic context. |
|
|
| ## Disclaimer |
|
|
| This is an UNOFFICIAL implementation of the side-chain packer released with [Atomic context-conditioned protein sequence design using LigandMPNN](https://doi.org/10.1038/s41592-025-02626-1) by Justas Dauparas, et al. |
|
|
| The OFFICIAL repository of MPNN Packer is at [dauparas/LigandMPNN](https://github.com/dauparas/LigandMPNN). |
|
|
| **The team releasing MPNN Packer did not write this model card for this model so this model card has been written by the OFoldX team.** |
|
|
| ## Model Details |
|
|
| MPNN Packer predicts protein side-chain conformations on a fixed backbone. It uses LigandMPNN's local atomic-context |
| representation to predict a three-component von Mises mixture for each side-chain chi angle, followed by iterative |
| coordinate refinement. The backbone and sequence remain fixed while selected side chains are rebuilt. |
|
|
| Please refer to the [Training Details](#training-details) section for more information on the training process. |
|
|
| ### Model Specification |
|
|
| | Hidden Size | Encoder Blocks | Atom-Context Blocks | Context Blocks | Decoder Blocks | Neighbors | Context Atoms | Mixture Components | Num Parameters (M) | |
| | ----------- | -------------- | ------------------- | -------------- | -------------- | --------- | ------------- | ------------------ | ------------------ | |
| | 128 | 3 | 2 | 2 | 3 | 32 | 16 | 3 | 3.591 | |
|
|
| ### Links |
|
|
| - **Code**: [ofoldx.models.mpnn_packer](https://github.com/OTeam-AI4S/OFoldX/tree/main/ofoldx/models/mpnn_packer) |
| - **Paper**: |
| [Atomic context-conditioned protein sequence design using LigandMPNN](https://doi.org/10.1038/s41592-025-02626-1) |
| - **Developed by**: Justas Dauparas, Gyu Rie Lee, Robert Pecoraro, Linna An, Ivan Anishchenko, Cameron Glasscock, and |
| David Baker |
| - **Model type**: Message-passing side-chain torsion predictor with iterative coordinate refinement |
| - **Original Repository**: [dauparas/LigandMPNN](https://github.com/dauparas/LigandMPNN) |
|
|
| ## 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 |
|
|
| #### Side-Chain Packing |
|
|
| You can use this model directly with a pipeline for protein side-chain packing: |
|
|
| ```python |
| import ofoldx |
| from ofoldx.io import read_structure |
| |
| structure, _ = read_structure("backbone.cif") |
| pipeline = ofoldx.pipeline("pack", model="oteam/mpnn-packer") |
| result = pipeline(structure) |
| ``` |
|
|
| Each prediction contains the packed structure and chi log-density metrics. To pack a designed sequence, pass the |
| candidate's final `target`. |
|
|
| ## Training Details |
|
|
| MPNN Packer is trained to model side-chain torsion distributions conditioned on protein structure and sequence. |
|
|
| ### Training Data |
|
|
| MPNN Packer was trained on protein structures with observed side-chain conformations and optional local non-protein atom |
| context. |
|
|
| ### Training Procedure |
|
|
| The model predicts mixture distributions over side-chain chi angles and applies iterative coordinate refinement. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{dauparas2025atomic, |
| author = {Dauparas, Justas and Lee, Gyu Rie and Pecoraro, Robert and others}, |
| title = {Atomic context-conditioned protein sequence design using LigandMPNN}, |
| journal = {Nature Methods}, |
| year = {2025}, |
| doi = {10.1038/s41592-025-02626-1} |
| } |
| ``` |
|
|
| > [!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 [LigandMPNN paper](https://doi.org/10.1038/s41592-025-02626-1) 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 |
| ``` |
|
|
