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"""Library of scoring methods of the model outputs."""
from flax_model.alphafold3.model import protein_data_processing
import jax.numpy as jnp
import numpy as np
Array = jnp.ndarray | np.ndarray
def pseudo_beta_fn(
aatype: Array,
dense_atom_positions: Array,
dense_atom_masks: Array,
is_ligand: Array | None = None,
use_jax: bool | None = True,
) -> tuple[Array, Array] | Array:
"""Create pseudo beta atom positions and optionally mask.
Args:
aatype: [num_res] amino acid types.
dense_atom_positions: [num_res, NUM_DENSE, 3] vector of all atom positions.
dense_atom_masks: [num_res, NUM_DENSE] mask.
is_ligand: [num_res] flag if something is a ligand.
use_jax: whether to use jax for the computations.
Returns:
Pseudo beta dense atom positions and the corresponding mask.
"""
if use_jax:
xnp = jnp
else:
xnp = np
if is_ligand is None:
is_ligand = xnp.zeros_like(aatype)
pseudobeta_index_polymer = xnp.take(
protein_data_processing.RESTYPE_PSEUDOBETA_INDEX, aatype, axis=0
).astype(xnp.int32)
pseudobeta_index = xnp.where(
is_ligand,
xnp.zeros_like(pseudobeta_index_polymer),
pseudobeta_index_polymer,
)
pseudo_beta = xnp.take_along_axis(
dense_atom_positions, pseudobeta_index[..., None, None], axis=-2
)
pseudo_beta = xnp.squeeze(pseudo_beta, axis=-2)
pseudo_beta_mask = xnp.take_along_axis(
dense_atom_masks, pseudobeta_index[..., None], axis=-1
).astype(xnp.float32)
pseudo_beta_mask = xnp.squeeze(pseudo_beta_mask, axis=-1)
return pseudo_beta, pseudo_beta_mask
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