> ## 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. # LigandMPNN Inverse Folding > LigandMPNN structure-conditioned protein sequence design with ligand awareness
LigandMPNN Inverse Folding
**License:** LigandMPNN is open source and free for academic and commercial use under an MIT license. Please refer to [the license](https://github.com/dauparas/LigandMPNN/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/ligandmpnn\_generator.py
View source
```bibtex theme={null} @ARTICLE{Dauparas2025-eg, title = "Atomic context-conditioned protein sequence design using {LigandMPNN}", author = "Dauparas, Justas and Lee, Gyu Rie and Pecoraro, Robert and An, Linna and Anishchenko, Ivan and Glasscock, Cameron and Baker, David", journal = "Nat. Methods", publisher = "Springer Science and Business Media LLC", volume = 22, number = 4, pages = "717--723", doi = "10.1038/s41592-025-02626-1", month = apr, year = 2025, language = "en" } ```
Copy citation
Generator contributors
Protein sequence generator using LigandMPNN inverse folding model. This generator uses LigandMPNN to design protein sequences that are predicted to fold into a given 3D backbone structure while considering ligand context. Unlike ProteinMPNN, LigandMPNN is aware of non-protein atoms (ligands, cofactors, metal ions) in the structure. LigandMPNN is particularly effective for: * Designing enzymes with specific active site geometries * Optimizing binding pockets around ligands * Creating sequences for cofactor-dependent proteins * Redesigning protein-ligand interfaces ## API Reference
ConfigLigandMPNNGeneratorConfig Source
Configuration object for LigandMPNNGenerator. This class defines configuration parameters for the LigandMPNN generator, which uses the LigandMPNN inverse folding model to design protein sequences conditioned on a given 3D backbone structure and ligand context. LigandMPNN extends ProteinMPNN to be aware of non-protein atoms (ligands, cofactors, metal ions), making it particularly effective for: * Enzyme active site design * Binding pocket optimization * Cofactor-dependent protein design Structure(s) with optional chains\_to\_redesign and fixed\_positions constraints. LigandMPNN implementation: Foundry-backed ligand\_mpnn or original LigandMPNN. Options: `ligand_mpnn`, `original` 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. Whether LigandMPNN conditions on fixed-residue sidechain atoms. Ligand-residue distance cutoff (Å) used by LigandMPNN. Optional explicit LigandMPNN checkpoint path. Optional seed passed directly to the LigandMPNN tool; None uses Proto's derived seed stream. 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 LigandMPNNGenerator, LigandMPNNGeneratorConfig >>> from proto_language.core import Segment >>> config = LigandMPNNGeneratorConfig( ... structure_inputs="/path/to/enzyme_with_ligand.pdb", ... temperature=0.1, ... ) >>> gen = LigandMPNNGenerator(config) >>> segment = Segment(length=100, sequence_type="protein") >>> gen.assign(segment) >>> gen.sample() # Generates num_proposals sequences from the backbone ``` ## Metadata | Property | Value | | ------------------------ | --------------------- | | Key | `ligandmpnn` | | Class | `LigandMPNNGenerator` | | Category | `inverse_folding` | | Input Type | `structure` | | Uses GPU | `True` | | Supported Sequence Types | `protein` | | Allows Empty Start | `False` |