score

score#

Score a given sequence on a structure using the ProteinMPNN model.

prxteinmpnn.scoring.score.make_score_sequence(model_parameters, decoding_order_fn=<PjitFunction of <function random_decoding_order>>, num_encoder_layers=3, num_decoder_layers=3)[source]#

Create a function to score a sequence on a structure.

Return type:

Callable[[Union[Key[Array, ''], UInt32[Array, '2']], Int[Array, 'num_residues'], Float[Array, 'num_residues num_atoms 3'], Int[Array, 'num_residues num_atoms'], Int[Array, 'num_residues'], Int[Array, 'num_residues'], int, Float[Array, 'n'] | None, Bool[Array, 'num_residues num_residues'] | None], tuple[Float, Float[Array, 'num_residues num_classes'], Int[Array, 'num_residues']]]

Parameters:

  • model_parameters (PyTree[str, 'P'])

  • decoding_order_fn (Callable[[Unpack[tuple[Key[Array, ''] | UInt32[Array, '2'], Int]]], tuple[Int[Array, 'num_residues'], Key[Array, ''] | UInt32[Array, '2']]])

  • num_encoder_layers (int)

  • num_decoder_layers (int)

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