---
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
```