MPNN Perplexity

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/constraint/sequence_scoring/mpnn_perplexity_constraint.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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Constraint contributors

Score proposals by ProteinMPNN perplexity against the configured backbone.

API Reference

ConfigMpnnPerplexityConfig Source

Configuration for ProteinMPNN perplexity scoring.

InverseFoldingStructureInput

required

Backbone structure, optional chains_to_redesign, and fixed positions for direct ProteinMPNN scoring.

enum

default:"proteinmpnn"

Weights: proteinmpnn (=v_48_020), v_48_{002,010,030} noise variants, abmpnn, soluble.Options: proteinmpnn, v_48_002, v_48_010, v_48_030, abmpnn, soluble

number

default:"1.0"

Softmax temperature for relaxing optimizer logits before ProteinMPNN scoring.

boolean

default:"True"

Hard one-hot forward pass with soft-probability gradients.

string

default:"cuda"

Device for ProteinMPNN execution, e.g. ‘cuda’ or ‘cuda:0’.

integer

Seed for ProteinMPNN decoding-order sampling. None lets proto-tools choose a fresh seed.

enum

default:"ppl"

Return ProteinMPNN perplexity by default, or raw mean NLL when set to ‘nll’.Options: nll, ppl

number

default:"1.0"

Pre-scale raw logits before ProteinMPNN; gradients are scaled back by the same factor.

SequenceLogitBiasConfig

Declarative sequence-symbol bias (canonical 20-AA protein) added before ProteinMPNN.

Usage

python

from proto_language.core import Constraint
from proto_language.constraint import mpnn_perplexity_constraint, MpnnPerplexityConfig

constraint = Constraint(
    inputs=[segment],
    function=mpnn_perplexity_constraint,
    function_config=MpnnPerplexityConfig(
        # Configure parameters here
    ),
)

scores = constraint.evaluate()

Metadata

Property	Value
Key	`mpnn-perplexity`
Function	`mpnn_perplexity_constraint`
Category	`sequence_scoring`
Mode	`dual`
Uses GPU	`True`
Supported Types	`protein`

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MPNN Perplexity

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