ProteinMPNN Inverse Folding

License: ProteinMPNN is open source and free for academic and commercial use under an MIT license. Please refer to the license for full terms.

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evo-design/proto-language/proto_language/generator/proteinmpnn_generator.py

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@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}
}

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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.

enum

default:"proteinmpnn"

ProteinMPNN weights: ‘proteinmpnn’ (general), ‘abmpnn’ (antibody), or ‘soluble’ (soluble proteins).Options: proteinmpnn, abmpnn, soluble

array

Structure(s) with optional chains_to_redesign and fixed_positions constraints.

string

When sampling a multi-chain structure, write only this chain’s sequence to the target segment.

number

default:"0.1"

Randomness of sampling (0-1). Near 0 is deterministic; near 1 is proportional to model probs.

array

Single-letter amino-acid codes to forbid in the designed sequence (e.g. ‘C’ to avoid disulfides).

integer

default:"1"

Number of sequences to process simultaneously on GPU

string

default:"cuda"

GPU device for inference (e.g. ‘cuda’ or ‘cuda:0’).

boolean

default:"False"

Whether to print status messages during execution.

Usage

python

>>> 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`

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ProteinMPNN Inverse Folding

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