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mindsponge.metrics.local_distance_difference_test

mindsponge.metrics.local_distance_difference_test(predicted_points, true_points, true_points_mask, cutoff=15, per_residue=False)

Compute true and predicted distance matrices for $C\alpha$. First calculate the distance matrix of true and predicted $C\alpha$ atoms $D=(((x[None,:]-x[:,None]{)}^2).sum(-1){)}^{0.5}$ then compute the rate that difference is smaller than fixed value: $lddt=(rate(abs(D_{true}-D_{pred})<0.5)+rate(abs(D_{true}-D_{pred})<1.0)+rate(abs(D_{true}-D_{pred})<2.0)+rate(abs(D_{true}-D_{pred})<4.0))/4$ Jumper et al. (2021) Suppl. Alg. 29 "predictPerResidueLDDT_Ca".

Parameters

• predicted_points (Tensor) – The prediction Ca atoms position tensor of shape $(1,N_{res},3)$ with $N_{res}$ the number of residues in protein.

• true_points (Tensor) – The ground truth Ca atoms position tensor of shape $(1,N_{res},3)$

• true_points_mask (Tensor) – The binary mask for predicted_points of shape $(1,N_{res},1)$

• cutoff (float) – The cutoff value for lddt to stop gradient, Default: 15.

• per_residue (bool) – The indicator if local distance difference is averaged, set True to return local distance difference per residue. Default: False.

Returns

• score (Tensor) - Local distance difference score, the shape is $(1,)$ if per_residue is set to False, $(1,N_{res})$ otherwise.

Supported Platforms:

Ascend GPU

Examples

>>> import numpy as np
>>> np.random.seed(0)
>>> from mindsponge.metrics import local_distance_difference_test
>>> from mindspore import dtype as mstype
>>> from mindspore import Tensor
>>> predicted_points = Tensor(np.random.rand(1, 256, 3)).astype(mstype.float32)
>>> true_points = Tensor(np.random.rand(1, 256, 3)).astype(mstype.float32)
>>> true_points_mask = Tensor(np.random.rand(1, 256, 1)).astype(mstype.float32)
>>> lddt = local_distance_difference_test(predicted_points, true_points, true_points_mask,
...                                       cutoff=15, per_residue=False)
>>> print(lddt)
[0.9554313]