> ## Documentation Index > Fetch the complete documentation index at: https://proto.evodesign.org/docs/llms.txt > Use this file to discover all available pages before exploring further. # ProteinMPNN Inverse Folding > ProteinMPNN structure-conditioned protein sequence design
ProteinMPNN Inverse Folding
**License:** ProteinMPNN is open source and free for academic and commercial use under an MIT license. Please refer to [the license](https://github.com/dauparas/ProteinMPNN/blob/main/LICENSE) for full terms.

This generator is open source. Any third-party models, product names, or trademarks referenced are the property of their respective owners, and Proto is not affiliated with them.

Go to Tool Page
evo-design/proto-language/proto\_language/generator/proteinmpnn\_generator.py
View source
```bibtex theme={null} @article{dauparas2022proteinmpnn, title={Robust deep learning--based protein sequence design using ProteinMPNN}, author={Dauparas, Justas and Anishchenko, Ivan and Bennett, Nathaniel and Bai, Hua and Ragotte, Robert J and Milles, Lukas F and Wicky, Basile IM and Courber, Alexis and de Haas, Rob J and Bethel, Neville and others}, journal={Science}, volume={378}, number={6615}, pages={49--56}, year={2022}, publisher={American Association for the Advancement of Science}, doi={10.1126/science.add2187} } ```
Copy citation
Generator contributors
Protein sequence generator using ProteinMPNN inverse folding model. This generator uses ProteinMPNN to design protein sequences that are predicted to fold into a given 3D backbone structure. Unlike mutation-based generators that refine existing sequences, ProteinMPNN generates sequences directly from structural information. ProteinMPNN is particularly effective for: * Redesigning existing proteins while maintaining fold * Designing sequences for computationally generated backbones * Creating sequence diversity for experimental screening * Stabilizing protein structures through sequence optimization ## API Reference
ConfigProteinMPNNGeneratorConfig Source
Configuration object for ProteinMPNNGenerator. This class defines configuration parameters for the ProteinMPNN generator, which uses the ProteinMPNN inverse folding model to design protein sequences conditioned on a given 3D backbone structure. ProteinMPNN is a message-passing neural network that predicts amino acid sequences likely to fold into a specified protein backbone structure. It excels at redesigning existing proteins while maintaining structural compatibility. ProteinMPNN weights: 'proteinmpnn' (general), 'abmpnn' (antibody), or 'soluble' (soluble proteins). Options: `proteinmpnn`, `abmpnn`, `soluble` Structure(s) with optional chains\_to\_redesign and fixed\_positions constraints. When sampling a multi-chain structure, write only this chain's sequence to the target segment. Randomness of sampling (0-1). Near 0 is deterministic; near 1 is proportional to model probs. Single-letter amino-acid codes to forbid in the designed sequence (e.g. 'C' to avoid disulfides). Number of sequences to process simultaneously on GPU GPU device for inference (e.g. 'cuda' or 'cuda:0'). Whether to print status messages during execution.
## Usage ```python python icon="python" theme={null} >>> from proto_language.generator import ProteinMPNNGenerator, ProteinMPNNGeneratorConfig >>> from proto_language.core import Segment >>> config = ProteinMPNNGeneratorConfig( ... structure_inputs="/path/to/backbone.pdb", ... temperature=0.1, ... ) >>> gen = ProteinMPNNGenerator(config) >>> segment = Segment(length=100, sequence_type="protein") >>> gen.assign(segment) >>> gen.sample() # Generates num_proposals sequences from the backbone ``` ## Metadata | Property | Value | | ------------------------ | ---------------------- | | Key | `proteinmpnn` | | Class | `ProteinMPNNGenerator` | | Category | `inverse_folding` | | Input Type | `structure` | | Uses GPU | `True` | | Supported Sequence Types | `protein` | | Allows Empty Start | `False` |