--- library_name: "ofoldx" tags: - "biology" - "biomolecular-design" - "protein" - "rna" - "dna" - "pipeline" - "proteinmpnn" - "design-generation" - "protein-design" artifact_kind: "pipeline" repo_id: "oteam/proteinmpnn-membrane-global-noise020" license: "mit" pipeline_tag: "other" task: "design_generation" model-index: - name: "proteinmpnn-membrane-global-noise020" results: [] widget: - pipeline_tag: "other" task: "design_generation" example_title: "Backbone sequence design" text: "input_structure: backbone.cif\ndesign_chains: A" input_format: "structure_path" - pipeline_tag: "other" task: "design_generation" example_title: "Binder design" text: "target_structure: target.cif\ntarget_chains: A\ndesign_chains: B" input_format: "structure_path" --- # proteinmpnn-membrane-global-noise020 OFoldX `pipeline` artifact for biomolecular design generation, using the `proteinmpnn` architecture. ## Disclaimer This model card was generated by the OFoldX team for an OFoldX `pipeline` artifact. The upstream model authors did not write this card unless explicitly stated otherwise. OFoldX is pre-alpha research software. Check the source checkpoint, upstream release, and local validation before using the artifact for scientific or operational decisions. ## Model Details ProteinMPNN sequence-design model for protein backbones, including soluble and membrane variants. Converted membrane ProteinMPNN checkpoint with a global soluble/membrane label. ### Model Provenance - **Upstream Project**: Membrane ProteinMPNN - **Source Release**: [https://github.com/dauparas/LigandMPNN](https://github.com/dauparas/LigandMPNN) - **Primary Paper**: [Robust deep learning-based protein sequence design using ProteinMPNN](https://doi.org/10.1126/science.add2187) - **Upstream License**: MIT for upstream ProteinMPNN and LigandMPNN code/model parameters ### Model Specification | Field | Value | | ----- | ----- | | Repository | `oteam/proteinmpnn-membrane-global-noise020` | | Artifact Kind | `pipeline` | | Task | `design_generation` | | Architecture | `proteinmpnn` | | Entrypoint | `ofoldx.pipelines.design.DesignPipeline` | > [!NOTE] > Checkpoint metadata: `k_neighbors=48`; the `noiseXXX` suffix identifies the training-noise variant. ### Links - **Hub repository**: [oteam/proteinmpnn-membrane-global-noise020](https://huggingface.co/oteam/proteinmpnn-membrane-global-noise020) - **Upstream paper**: [Robust deep learning-based protein sequence design using ProteinMPNN](https://doi.org/10.1126/science.add2187) - **Upstream repository**: [Membrane ProteinMPNN](https://github.com/dauparas/LigandMPNN) - **Source checkpoint release**: [https://github.com/dauparas/LigandMPNN](https://github.com/dauparas/LigandMPNN) - **Code**: [`ofoldx/pipelines/design.py`](https://github.com/OTeam-AI4S/OFoldX/tree/main/ofoldx/pipelines/design.py) - **Project repository**: [https://github.com/OTeam-AI4S/OFoldX](https://github.com/OTeam-AI4S/OFoldX) - **Issues**: [https://github.com/OTeam-AI4S/OFoldX/issues](https://github.com/OTeam-AI4S/OFoldX/issues) ## Usage The artifact depends on the [`ofoldx`](https://github.com/OTeam-AI4S/OFoldX) library. Install it with pip: ```bash pip install ofoldx ``` ### Pipeline Usage Load the artifact from `oteam/proteinmpnn-membrane-global-noise020` with the OFoldX task pipeline. Use `AutoModel` or `AutoProcessor` only when you need lower-level control: ```python from ofoldx.pipelines import Pipeline pipeline = Pipeline.from_pretrained("oteam/proteinmpnn-membrane-global-noise020") ``` When a matching processor is available, load it with `AutoProcessor.from_pretrained(...)` and pass the processed batch to the model. ### Interface - **Task**: `design_generation` - **Artifact kind**: `pipeline` - **Architecture**: `proteinmpnn` - **Runtime files**: `manifest.json`, `config.json`, and `model.safetensors` when present ## Training Details OFoldX did not train these weights. This repository contains a converted checkpoint and OFoldX runtime metadata for loading it. ### Training Data This checkpoint is distributed in the LigandMPNN parameter release. The global-label model adds a binary soluble/membrane feature to the ProteinMPNN-style input representation. OFoldX does not redistribute the training set. ### Training Procedure Upstream training follows the ProteinMPNN fixed-backbone inverse-folding setup with a global membrane label and a 0.20 Angstrom noisy-backbone checkpoint. OFoldX only converts the released checkpoint. ## Evaluation OFoldX conversion reports and contract tests validate artifact structure and checkpoint loading. Task-level scientific evaluation should be checked against the corresponding upstream model release or paper. ## Limitations - This artifact is distributed for research use. - Inputs must match the model-specific processor and expected biomolecular representation. - OFoldX is pre-alpha, so APIs and artifact metadata may still change before a stable release. ## Citation Please cite the upstream Membrane ProteinMPNN work for the source checkpoint. If OFoldX supports your work, please also cite or link the OFoldX project repository. ```bibtex @article{dauparas2022robust, author = {Dauparas, Justas and Anishchenko, Ivan and Bennett, Nathaniel and Bai, Hua and Ragotte, Robert J. and Milles, Lukas F. and Wicky, Basile I. M. and Courbet, Alexis and de Haas, Rob J. and Bethel, Neville and others}, title = {Robust deep learning-based protein sequence design using ProteinMPNN}, journal = {Science}, volume = {378}, number = {6615}, pages = {49--56}, year = {2022}, doi = {10.1126/science.add2187} } ``` ## Contact Please use [OFoldX GitHub issues](https://github.com/OTeam-AI4S/OFoldX/issues) for questions or comments about this model card. ## License The Hub `license` metadata, when present, reflects the source checkpoint or upstream project license. The OFoldX project license is not yet finalized. The source checkpoint is associated with the upstream license noted above: MIT for upstream ProteinMPNN and LigandMPNN code/model parameters. Review both OFoldX and upstream terms before redistribution or production use.
