solublempnn-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 SolubleMPNN sequence-design checkpoint for soluble protein backbones.
Model Provenance
- Upstream Project: SolubleMPNN
- Source Release: https://github.com/dauparas/LigandMPNN
- Primary Paper: Computational design of soluble and functional membrane protein analogues
- Upstream License: MIT for upstream ProteinMPNN and LigandMPNN code/model parameters
Model Specification
| Field | Value |
|---|
| Repository | oteam/solublempnn-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
Usage
The artifact depends on the
ofoldx library. Install it with pip:
Pipeline Usage
Load the artifact from oteam/solublempnn-noise020 with the OFoldX task pipeline. Use AutoModel or AutoProcessor only when you need lower-level control:
1from ofoldx.pipelines import Pipeline
2
3pipeline = Pipeline.from_pretrained("oteam/solublempnn-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
The SolubleMPNN work trains like ProteinMPNN on PDB assemblies as of 2021-08-02, filtered to X-ray/cryo-EM structures better than 3.5 A and fewer than 10,000 residues, while excluding annotated transmembrane PDB entries. OFoldX does not redistribute the training set.
Training Procedure
Upstream SolubleMPNN follows ProteinMPNN-style fixed-backbone inverse-folding training with 48-neighbor noisy-backbone checkpoint variants. OFoldX converts released SolubleMPNN checkpoints into model.safetensors; it does not run SolubleMPNN training.
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 SolubleMPNN work for the source checkpoint. If OFoldX supports your work, please also cite or link the OFoldX project repository.
1@article{solublempnn2024membrane,
2 title = {Computational design of soluble and functional membrane protein analogues},
3 journal = {Nature},
4 year = {2024},
5 doi = {10.1038/s41586-024-07601-y}
6}
Contact
Please use
OFoldX GitHub 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.
