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# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import itertools
from functools import reduce, wraps
from operator import add
import numpy as np
import torch
from . import residue_constants as rc
from .rigid_utils import Rotation, Rigid
from .tensor_utils import (
tree_map,
tensor_tree_map,
batched_gather,
)
MSA_FEATURE_NAMES = [
"msa",
"deletion_matrix",
"msa_mask",
"msa_row_mask",
"bert_mask",
"true_msa",
]
def cast_to_64bit_ints(protein):
# We keep all ints as int64
for k, v in protein.items():
if v.dtype == torch.int32:
protein[k] = v.type(torch.int64)
return protein
def make_one_hot(x, num_classes):
x_one_hot = torch.zeros(*x.shape, num_classes, device=x.device)
x_one_hot.scatter_(-1, x.unsqueeze(-1), 1)
return x_one_hot
def make_seq_mask(protein):
protein["seq_mask"] = torch.ones(
protein["aatype"].shape, dtype=torch.float32
)
return protein
def make_template_mask(protein):
protein["template_mask"] = torch.ones(
protein["template_aatype"].shape[0], dtype=torch.float32
)
return protein
def curry1(f):
"""Supply all arguments but the first."""
@wraps(f)
def fc(*args, **kwargs):
return lambda x: f(x, *args, **kwargs)
return fc
def make_all_atom_aatype(protein):
protein["all_atom_aatype"] = protein["aatype"]
return protein
def fix_templates_aatype(protein):
# Map one-hot to indices
num_templates = protein["template_aatype"].shape[0]
if(num_templates > 0):
protein["template_aatype"] = torch.argmax(
protein["template_aatype"], dim=-1
)
# Map hhsearch-aatype to our aatype.
new_order_list = rc.MAP_HHBLITS_AATYPE_TO_OUR_AATYPE
new_order = torch.tensor(
new_order_list, dtype=torch.int64, device=protein["aatype"].device,
).expand(num_templates, -1)
protein["template_aatype"] = torch.gather(
new_order, 1, index=protein["template_aatype"]
)
return protein
def correct_msa_restypes(protein):
"""Correct MSA restype to have the same order as rc."""
new_order_list = rc.MAP_HHBLITS_AATYPE_TO_OUR_AATYPE
new_order = torch.tensor(
[new_order_list] * protein["msa"].shape[1],
device=protein["msa"].device,
).transpose(0, 1)
protein["msa"] = torch.gather(new_order, 0, protein["msa"])
perm_matrix = np.zeros((22, 22), dtype=np.float32)
perm_matrix[range(len(new_order_list)), new_order_list] = 1.0
for k in protein:
if "profile" in k:
num_dim = protein[k].shape.as_list()[-1]
assert num_dim in [
20,
21,
22,
], "num_dim for %s out of expected range: %s" % (k, num_dim)
protein[k] = torch.dot(protein[k], perm_matrix[:num_dim, :num_dim])
return protein
def squeeze_features(protein):
"""Remove singleton and repeated dimensions in protein features."""
protein["aatype"] = torch.argmax(protein["aatype"], dim=-1)
for k in [
"domain_name",
"msa",
"num_alignments",
"seq_length",
"sequence",
"superfamily",
"deletion_matrix",
"resolution",
"between_segment_residues",
"residue_index",
"template_all_atom_mask",
]:
if k in protein:
final_dim = protein[k].shape[-1]
if isinstance(final_dim, int) and final_dim == 1:
if torch.is_tensor(protein[k]):
protein[k] = torch.squeeze(protein[k], dim=-1)
else:
protein[k] = np.squeeze(protein[k], axis=-1)
for k in ["seq_length", "num_alignments"]:
if k in protein:
protein[k] = protein[k][0]
return protein
@curry1
def randomly_replace_msa_with_unknown(protein, replace_proportion):
"""Replace a portion of the MSA with 'X'."""
msa_mask = torch.rand(protein["msa"].shape) < replace_proportion
x_idx = 20
gap_idx = 21
msa_mask = torch.logical_and(msa_mask, protein["msa"] != gap_idx)
protein["msa"] = torch.where(
msa_mask,
torch.ones_like(protein["msa"]) * x_idx,
protein["msa"]
)
aatype_mask = torch.rand(protein["aatype"].shape) < replace_proportion
protein["aatype"] = torch.where(
aatype_mask,
torch.ones_like(protein["aatype"]) * x_idx,
protein["aatype"],
)
return protein
@curry1
def sample_msa(protein, max_seq, keep_extra, seed=None):
"""Sample MSA randomly, remaining sequences are stored are stored as `extra_*`."""
num_seq = protein["msa"].shape[0]
g = torch.Generator(device=protein["msa"].device)
if seed is not None:
g.manual_seed(seed)
shuffled = torch.randperm(num_seq - 1, generator=g) + 1
index_order = torch.cat(
(torch.tensor([0], device=shuffled.device), shuffled),
dim=0
)
num_sel = min(max_seq, num_seq)
sel_seq, not_sel_seq = torch.split(
index_order, [num_sel, num_seq - num_sel]
)
for k in MSA_FEATURE_NAMES:
if k in protein:
if keep_extra:
protein["extra_" + k] = torch.index_select(
protein[k], 0, not_sel_seq
)
protein[k] = torch.index_select(protein[k], 0, sel_seq)
return protein
@curry1
def add_distillation_flag(protein, distillation):
protein['is_distillation'] = distillation
return protein
@curry1
def sample_msa_distillation(protein, max_seq):
if(protein["is_distillation"] == 1):
protein = sample_msa(max_seq, keep_extra=False)(protein)
return protein
@curry1
def crop_extra_msa(protein, max_extra_msa):
num_seq = protein["extra_msa"].shape[0]
num_sel = min(max_extra_msa, num_seq)
select_indices = torch.randperm(num_seq)[:num_sel]
for k in MSA_FEATURE_NAMES:
if "extra_" + k in protein:
protein["extra_" + k] = torch.index_select(
protein["extra_" + k], 0, select_indices
)
return protein
def delete_extra_msa(protein):
for k in MSA_FEATURE_NAMES:
if "extra_" + k in protein:
del protein["extra_" + k]
return protein
# Not used in inference
@curry1
def block_delete_msa(protein, config):
num_seq = protein["msa"].shape[0]
block_num_seq = torch.floor(
torch.tensor(num_seq, dtype=torch.float32, device=protein["msa"].device)
* config.msa_fraction_per_block
).to(torch.int32)
if config.randomize_num_blocks:
nb = torch.distributions.uniform.Uniform(
0, config.num_blocks + 1
).sample()
else:
nb = config.num_blocks
del_block_starts = torch.distributions.Uniform(0, num_seq).sample(nb)
del_blocks = del_block_starts[:, None] + torch.range(block_num_seq)
del_blocks = torch.clip(del_blocks, 0, num_seq - 1)
del_indices = torch.unique(torch.sort(torch.reshape(del_blocks, [-1])))[0]
# Make sure we keep the original sequence
combined = torch.cat((torch.range(1, num_seq)[None], del_indices[None]))
uniques, counts = combined.unique(return_counts=True)
difference = uniques[counts == 1]
intersection = uniques[counts > 1]
keep_indices = torch.squeeze(difference, 0)
for k in MSA_FEATURE_NAMES:
if k in protein:
protein[k] = torch.gather(protein[k], keep_indices)
return protein
@curry1
def nearest_neighbor_clusters(protein, gap_agreement_weight=0.0):
weights = torch.cat(
[
torch.ones(21, device=protein["msa"].device),
gap_agreement_weight * torch.ones(1, device=protein["msa"].device),
torch.zeros(1, device=protein["msa"].device)
],
0,
)
# Make agreement score as weighted Hamming distance
msa_one_hot = make_one_hot(protein["msa"], 23)
sample_one_hot = protein["msa_mask"][:, :, None] * msa_one_hot
extra_msa_one_hot = make_one_hot(protein["extra_msa"], 23)
extra_one_hot = protein["extra_msa_mask"][:, :, None] * extra_msa_one_hot
num_seq, num_res, _ = sample_one_hot.shape
extra_num_seq, _, _ = extra_one_hot.shape
# Compute tf.einsum('mrc,nrc,c->mn', sample_one_hot, extra_one_hot, weights)
# in an optimized fashion to avoid possible memory or computation blowup.
agreement = torch.matmul(
torch.reshape(extra_one_hot, [extra_num_seq, num_res * 23]),
torch.reshape(
sample_one_hot * weights, [num_seq, num_res * 23]
).transpose(0, 1),
)
# Assign each sequence in the extra sequences to the closest MSA sample
protein["extra_cluster_assignment"] = torch.argmax(agreement, dim=1).to(
torch.int64
)
return protein
def unsorted_segment_sum(data, segment_ids, num_segments):
"""
Computes the sum along segments of a tensor. Similar to
tf.unsorted_segment_sum, but only supports 1-D indices.
:param data: A tensor whose segments are to be summed.
:param segment_ids: The 1-D segment indices tensor.
:param num_segments: The number of segments.
:return: A tensor of same data type as the data argument.
"""
assert (
len(segment_ids.shape) == 1 and
segment_ids.shape[0] == data.shape[0]
)
segment_ids = segment_ids.view(
segment_ids.shape[0], *((1,) * len(data.shape[1:]))
)
segment_ids = segment_ids.expand(data.shape)
shape = [num_segments] + list(data.shape[1:])
tensor = (
torch.zeros(*shape, device=segment_ids.device)
.scatter_add_(0, segment_ids, data.float())
)
tensor = tensor.type(data.dtype)
return tensor
@curry1
def summarize_clusters(protein):
"""Produce profile and deletion_matrix_mean within each cluster."""
num_seq = protein["msa"].shape[0]
def csum(x):
return unsorted_segment_sum(
x, protein["extra_cluster_assignment"], num_seq
)
mask = protein["extra_msa_mask"]
mask_counts = 1e-6 + protein["msa_mask"] + csum(mask) # Include center
msa_sum = csum(mask[:, :, None] * make_one_hot(protein["extra_msa"], 23))
msa_sum += make_one_hot(protein["msa"], 23) # Original sequence
protein["cluster_profile"] = msa_sum / mask_counts[:, :, None]
del msa_sum
del_sum = csum(mask * protein["extra_deletion_matrix"])
del_sum += protein["deletion_matrix"] # Original sequence
protein["cluster_deletion_mean"] = del_sum / mask_counts
del del_sum
return protein
def make_msa_mask(protein):
"""Mask features are all ones, but will later be zero-padded."""
protein["msa_mask"] = torch.ones(protein["msa"].shape, dtype=torch.float32)
protein["msa_row_mask"] = torch.ones(
(protein["msa"].shape[0]), dtype=torch.float32
)
return protein
def pseudo_beta_fn(aatype, all_atom_positions, all_atom_mask):
"""Create pseudo beta features."""
is_gly = torch.eq(aatype, rc.restype_order["G"])
ca_idx = rc.atom_order["CA"]
cb_idx = rc.atom_order["CB"]
pseudo_beta = torch.where(
torch.tile(is_gly[..., None], [1] * len(is_gly.shape) + [3]),
all_atom_positions[..., ca_idx, :],
all_atom_positions[..., cb_idx, :],
)
if all_atom_mask is not None:
pseudo_beta_mask = torch.where(
is_gly, all_atom_mask[..., ca_idx], all_atom_mask[..., cb_idx]
)
return pseudo_beta, pseudo_beta_mask
else:
return pseudo_beta
@curry1
def make_pseudo_beta(protein, prefix=""):
"""Create pseudo-beta (alpha for glycine) position and mask."""
assert prefix in ["", "template_"]
(
protein[prefix + "pseudo_beta"],
protein[prefix + "pseudo_beta_mask"],
) = pseudo_beta_fn(
protein["template_aatype" if prefix else "aatype"],
protein[prefix + "all_atom_positions"],
protein["template_all_atom_mask" if prefix else "all_atom_mask"],
)
return protein
@curry1
def add_constant_field(protein, key, value):
protein[key] = torch.tensor(value, device=protein["msa"].device)
return protein
def shaped_categorical(probs, epsilon=1e-10):
ds = probs.shape
num_classes = ds[-1]
distribution = torch.distributions.categorical.Categorical(
torch.reshape(probs + epsilon, [-1, num_classes])
)
counts = distribution.sample()
return torch.reshape(counts, ds[:-1])
def make_hhblits_profile(protein):
"""Compute the HHblits MSA profile if not already present."""
if "hhblits_profile" in protein:
return protein
# Compute the profile for every residue (over all MSA sequences).
msa_one_hot = make_one_hot(protein["msa"], 22)
protein["hhblits_profile"] = torch.mean(msa_one_hot, dim=0)
return protein
@curry1
def make_masked_msa(protein, config, replace_fraction):
"""Create data for BERT on raw MSA."""
# Add a random amino acid uniformly.
random_aa = torch.tensor(
[0.05] * 20 + [0.0, 0.0],
dtype=torch.float32,
device=protein["aatype"].device
)
categorical_probs = (
config.uniform_prob * random_aa
+ config.profile_prob * protein["hhblits_profile"]
+ config.same_prob * make_one_hot(protein["msa"], 22)
)
# Put all remaining probability on [MASK] which is a new column
pad_shapes = list(
reduce(add, [(0, 0) for _ in range(len(categorical_probs.shape))])
)
pad_shapes[1] = 1
mask_prob = (
1.0 - config.profile_prob - config.same_prob - config.uniform_prob
)
assert mask_prob >= 0.0
categorical_probs = torch.nn.functional.pad(
categorical_probs, pad_shapes, value=mask_prob
)
sh = protein["msa"].shape
mask_position = torch.rand(sh) < replace_fraction
bert_msa = shaped_categorical(categorical_probs)
bert_msa = torch.where(mask_position, bert_msa, protein["msa"])
# Mix real and masked MSA
protein["bert_mask"] = mask_position.to(torch.float32)
protein["true_msa"] = protein["msa"]
protein["msa"] = bert_msa
return protein
@curry1
def make_fixed_size(
protein,
shape_schema,
msa_cluster_size,
extra_msa_size,
num_res=0,
num_templates=0,
):
"""Guess at the MSA and sequence dimension to make fixed size."""
pad_size_map = {
NUM_RES: num_res,
NUM_MSA_SEQ: msa_cluster_size,
NUM_EXTRA_SEQ: extra_msa_size,
NUM_TEMPLATES: num_templates,
}
for k, v in protein.items():
# Don't transfer this to the accelerator.
if k == "extra_cluster_assignment":
continue
shape = list(v.shape)
schema = shape_schema[k]
msg = "Rank mismatch between shape and shape schema for"
assert len(shape) == len(schema), f"{msg} {k}: {shape} vs {schema}"
pad_size = [
pad_size_map.get(s2, None) or s1 for (s1, s2) in zip(shape, schema)
]
padding = [(0, p - v.shape[i]) for i, p in enumerate(pad_size)]
padding.reverse()
padding = list(itertools.chain(*padding))
if padding:
protein[k] = torch.nn.functional.pad(v, padding)
protein[k] = torch.reshape(protein[k], pad_size)
return protein
@curry1
def make_msa_feat(protein):
"""Create and concatenate MSA features."""
# Whether there is a domain break. Always zero for chains, but keeping for
# compatibility with domain datasets.
has_break = torch.clip(
protein["between_segment_residues"].to(torch.float32), 0, 1
)
aatype_1hot = make_one_hot(protein["aatype"], 21)
target_feat = [
torch.unsqueeze(has_break, dim=-1),
aatype_1hot, # Everyone gets the original sequence.
]
msa_1hot = make_one_hot(protein["msa"], 23)
has_deletion = torch.clip(protein["deletion_matrix"], 0.0, 1.0)
deletion_value = torch.atan(protein["deletion_matrix"] / 3.0) * (
2.0 / np.pi
)
msa_feat = [
msa_1hot,
torch.unsqueeze(has_deletion, dim=-1),
torch.unsqueeze(deletion_value, dim=-1),
]
if "cluster_profile" in protein:
deletion_mean_value = torch.atan(
protein["cluster_deletion_mean"] / 3.0
) * (2.0 / np.pi)
msa_feat.extend(
[
protein["cluster_profile"],
torch.unsqueeze(deletion_mean_value, dim=-1),
]
)
if "extra_deletion_matrix" in protein:
protein["extra_has_deletion"] = torch.clip(
protein["extra_deletion_matrix"], 0.0, 1.0
)
protein["extra_deletion_value"] = torch.atan(
protein["extra_deletion_matrix"] / 3.0
) * (2.0 / np.pi)
protein["msa_feat"] = torch.cat(msa_feat, dim=-1)
protein["target_feat"] = torch.cat(target_feat, dim=-1)
return protein
@curry1
def select_feat(protein, feature_list):
return {k: v for k, v in protein.items() if k in feature_list}
@curry1
def crop_templates(protein, max_templates):
for k, v in protein.items():
if k.startswith("template_"):
protein[k] = v[:max_templates]
return protein
def make_atom14_masks(protein):
"""Construct denser atom positions (14 dimensions instead of 37)."""
restype_atom14_to_atom37 = []
restype_atom37_to_atom14 = []
restype_atom14_mask = []
for rt in rc.restypes:
atom_names = rc.restype_name_to_atom14_names[rc.restype_1to3[rt]]
restype_atom14_to_atom37.append(
[(rc.atom_order[name] if name else 0) for name in atom_names]
)
atom_name_to_idx14 = {name: i for i, name in enumerate(atom_names)}
restype_atom37_to_atom14.append(
[
(atom_name_to_idx14[name] if name in atom_name_to_idx14 else 0)
for name in rc.atom_types
]
)
restype_atom14_mask.append(
[(1.0 if name else 0.0) for name in atom_names]
)
# Add dummy mapping for restype 'UNK'
restype_atom14_to_atom37.append([0] * 14)
restype_atom37_to_atom14.append([0] * 37)
restype_atom14_mask.append([0.0] * 14)
restype_atom14_to_atom37 = torch.tensor(
restype_atom14_to_atom37,
dtype=torch.int32,
device=protein["aatype"].device,
)
restype_atom37_to_atom14 = torch.tensor(
restype_atom37_to_atom14,
dtype=torch.int32,
device=protein["aatype"].device,
)
restype_atom14_mask = torch.tensor(
restype_atom14_mask,
dtype=torch.float32,
device=protein["aatype"].device,
)
protein_aatype = protein['aatype'].to(torch.long)
# create the mapping for (residx, atom14) --> atom37, i.e. an array
# with shape (num_res, 14) containing the atom37 indices for this protein
residx_atom14_to_atom37 = restype_atom14_to_atom37[protein_aatype]
residx_atom14_mask = restype_atom14_mask[protein_aatype]
protein["atom14_atom_exists"] = residx_atom14_mask
protein["residx_atom14_to_atom37"] = residx_atom14_to_atom37.long()
# create the gather indices for mapping back
residx_atom37_to_atom14 = restype_atom37_to_atom14[protein_aatype]
protein["residx_atom37_to_atom14"] = residx_atom37_to_atom14.long()
# create the corresponding mask
restype_atom37_mask = torch.zeros(
[21, 37], dtype=torch.float32, device=protein["aatype"].device
)
for restype, restype_letter in enumerate(rc.restypes):
restype_name = rc.restype_1to3[restype_letter]
atom_names = rc.residue_atoms[restype_name]
for atom_name in atom_names:
atom_type = rc.atom_order[atom_name]
restype_atom37_mask[restype, atom_type] = 1
residx_atom37_mask = restype_atom37_mask[protein_aatype]
protein["atom37_atom_exists"] = residx_atom37_mask
return protein
def make_atom14_masks_np(batch):
batch = tree_map(
lambda n: torch.tensor(n, device=batch["aatype"].device),
batch,
np.ndarray
)
out = make_atom14_masks(batch)
out = tensor_tree_map(lambda t: np.array(t), out)
return out
def make_atom14_positions(protein):
"""Constructs denser atom positions (14 dimensions instead of 37)."""
residx_atom14_mask = protein["atom14_atom_exists"]
residx_atom14_to_atom37 = protein["residx_atom14_to_atom37"]
# Create a mask for known ground truth positions.
residx_atom14_gt_mask = residx_atom14_mask * batched_gather(
protein["all_atom_mask"],
residx_atom14_to_atom37,
dim=-1,
no_batch_dims=len(protein["all_atom_mask"].shape[:-1]),
)
# Gather the ground truth positions.
residx_atom14_gt_positions = residx_atom14_gt_mask[..., None] * (
batched_gather(
protein["all_atom_positions"],
residx_atom14_to_atom37,
dim=-2,
no_batch_dims=len(protein["all_atom_positions"].shape[:-2]),
)
)
protein["atom14_atom_exists"] = residx_atom14_mask
protein["atom14_gt_exists"] = residx_atom14_gt_mask
protein["atom14_gt_positions"] = residx_atom14_gt_positions
# As the atom naming is ambiguous for 7 of the 20 amino acids, provide
# alternative ground truth coordinates where the naming is swapped
restype_3 = [rc.restype_1to3[res] for res in rc.restypes]
restype_3 += ["UNK"]
# Matrices for renaming ambiguous atoms.
all_matrices = {
res: torch.eye(
14,
dtype=protein["all_atom_mask"].dtype,
device=protein["all_atom_mask"].device,
)
for res in restype_3
}
for resname, swap in rc.residue_atom_renaming_swaps.items():
correspondences = torch.arange(
14, device=protein["all_atom_mask"].device
)
for source_atom_swap, target_atom_swap in swap.items():
source_index = rc.restype_name_to_atom14_names[resname].index(
source_atom_swap
)
target_index = rc.restype_name_to_atom14_names[resname].index(
target_atom_swap
)
correspondences[source_index] = target_index
correspondences[target_index] = source_index
renaming_matrix = protein["all_atom_mask"].new_zeros((14, 14))
for index, correspondence in enumerate(correspondences):
renaming_matrix[index, correspondence] = 1.0
all_matrices[resname] = renaming_matrix
renaming_matrices = torch.stack(
[all_matrices[restype] for restype in restype_3]
)
# Pick the transformation matrices for the given residue sequence
# shape (num_res, 14, 14).
renaming_transform = renaming_matrices[protein["aatype"]]
# Apply it to the ground truth positions. shape (num_res, 14, 3).
alternative_gt_positions = torch.einsum(
"...rac,...rab->...rbc", residx_atom14_gt_positions, renaming_transform
)
protein["atom14_alt_gt_positions"] = alternative_gt_positions
# Create the mask for the alternative ground truth (differs from the
# ground truth mask, if only one of the atoms in an ambiguous pair has a
# ground truth position).
alternative_gt_mask = torch.einsum(
"...ra,...rab->...rb", residx_atom14_gt_mask, renaming_transform
)
protein["atom14_alt_gt_exists"] = alternative_gt_mask
# Create an ambiguous atoms mask. shape: (21, 14).
restype_atom14_is_ambiguous = protein["all_atom_mask"].new_zeros((21, 14))
for resname, swap in rc.residue_atom_renaming_swaps.items():
for atom_name1, atom_name2 in swap.items():
restype = rc.restype_order[rc.restype_3to1[resname]]
atom_idx1 = rc.restype_name_to_atom14_names[resname].index(
atom_name1
)
atom_idx2 = rc.restype_name_to_atom14_names[resname].index(
atom_name2
)
restype_atom14_is_ambiguous[restype, atom_idx1] = 1
restype_atom14_is_ambiguous[restype, atom_idx2] = 1
# From this create an ambiguous_mask for the given sequence.
protein["atom14_atom_is_ambiguous"] = restype_atom14_is_ambiguous[
protein["aatype"]
]
return protein
def atom37_to_frames(protein, eps=1e-8):
aatype = protein["aatype"]
all_atom_positions = protein["all_atom_positions"]
all_atom_mask = protein["all_atom_mask"]
batch_dims = len(aatype.shape[:-1])
restype_rigidgroup_base_atom_names = np.full([21, 8, 3], "", dtype=object)
restype_rigidgroup_base_atom_names[:, 0, :] = ["C", "CA", "N"]
restype_rigidgroup_base_atom_names[:, 3, :] = ["CA", "C", "O"]
for restype, restype_letter in enumerate(rc.restypes):
resname = rc.restype_1to3[restype_letter]
for chi_idx in range(4):
if rc.chi_angles_mask[restype][chi_idx]:
names = rc.chi_angles_atoms[resname][chi_idx]
restype_rigidgroup_base_atom_names[
restype, chi_idx + 4, :
] = names[1:]
restype_rigidgroup_mask = all_atom_mask.new_zeros(
(*aatype.shape[:-1], 21, 8),
)
restype_rigidgroup_mask[..., 0] = 1
restype_rigidgroup_mask[..., 3] = 1
restype_rigidgroup_mask[..., :20, 4:] = all_atom_mask.new_tensor(
rc.chi_angles_mask
)
lookuptable = rc.atom_order.copy()
lookuptable[""] = 0
lookup = np.vectorize(lambda x: lookuptable[x])
restype_rigidgroup_base_atom37_idx = lookup(
restype_rigidgroup_base_atom_names,
)
restype_rigidgroup_base_atom37_idx = aatype.new_tensor(
restype_rigidgroup_base_atom37_idx,
)
restype_rigidgroup_base_atom37_idx = (
restype_rigidgroup_base_atom37_idx.view(
*((1,) * batch_dims), *restype_rigidgroup_base_atom37_idx.shape
)
)
residx_rigidgroup_base_atom37_idx = batched_gather(
restype_rigidgroup_base_atom37_idx,
aatype,
dim=-3,
no_batch_dims=batch_dims,
)
base_atom_pos = batched_gather(
all_atom_positions,
residx_rigidgroup_base_atom37_idx,
dim=-2,
no_batch_dims=len(all_atom_positions.shape[:-2]),
)
gt_frames = Rigid.from_3_points(
p_neg_x_axis=base_atom_pos[..., 0, :],
origin=base_atom_pos[..., 1, :],
p_xy_plane=base_atom_pos[..., 2, :],
eps=eps,
)
group_exists = batched_gather(
restype_rigidgroup_mask,
aatype,
dim=-2,
no_batch_dims=batch_dims,
)
gt_atoms_exist = batched_gather(
all_atom_mask,
residx_rigidgroup_base_atom37_idx,
dim=-1,
no_batch_dims=len(all_atom_mask.shape[:-1]),
)
gt_exists = torch.min(gt_atoms_exist, dim=-1)[0] * group_exists
rots = torch.eye(3, dtype=all_atom_mask.dtype, device=aatype.device)
rots = torch.tile(rots, (*((1,) * batch_dims), 8, 1, 1))
rots[..., 0, 0, 0] = -1
rots[..., 0, 2, 2] = -1
rots = Rotation(rot_mats=rots)
gt_frames = gt_frames.compose(Rigid(rots, None))
restype_rigidgroup_is_ambiguous = all_atom_mask.new_zeros(
*((1,) * batch_dims), 21, 8
)
restype_rigidgroup_rots = torch.eye(
3, dtype=all_atom_mask.dtype, device=aatype.device
)
restype_rigidgroup_rots = torch.tile(
restype_rigidgroup_rots,
(*((1,) * batch_dims), 21, 8, 1, 1),
)
for resname, _ in rc.residue_atom_renaming_swaps.items():
restype = rc.restype_order[rc.restype_3to1[resname]]
chi_idx = int(sum(rc.chi_angles_mask[restype]) - 1)
restype_rigidgroup_is_ambiguous[..., restype, chi_idx + 4] = 1
restype_rigidgroup_rots[..., restype, chi_idx + 4, 1, 1] = -1
restype_rigidgroup_rots[..., restype, chi_idx + 4, 2, 2] = -1
residx_rigidgroup_is_ambiguous = batched_gather(
restype_rigidgroup_is_ambiguous,
aatype,
dim=-2,
no_batch_dims=batch_dims,
)
residx_rigidgroup_ambiguity_rot = batched_gather(
restype_rigidgroup_rots,
aatype,
dim=-4,
no_batch_dims=batch_dims,
)
residx_rigidgroup_ambiguity_rot = Rotation(
rot_mats=residx_rigidgroup_ambiguity_rot
)
alt_gt_frames = gt_frames.compose(
Rigid(residx_rigidgroup_ambiguity_rot, None)
)
gt_frames_tensor = gt_frames.to_tensor_4x4()
alt_gt_frames_tensor = alt_gt_frames.to_tensor_4x4()
protein["rigidgroups_gt_frames"] = gt_frames_tensor
protein["rigidgroups_gt_exists"] = gt_exists
protein["rigidgroups_group_exists"] = group_exists
protein["rigidgroups_group_is_ambiguous"] = residx_rigidgroup_is_ambiguous
protein["rigidgroups_alt_gt_frames"] = alt_gt_frames_tensor
return protein
def get_chi_atom_indices():
"""Returns atom indices needed to compute chi angles for all residue types.
Returns:
A tensor of shape [residue_types=21, chis=4, atoms=4]. The residue types are
in the order specified in rc.restypes + unknown residue type
at the end. For chi angles which are not defined on the residue, the
positions indices are by default set to 0.
"""
chi_atom_indices = []
for residue_name in rc.restypes:
residue_name = rc.restype_1to3[residue_name]
residue_chi_angles = rc.chi_angles_atoms[residue_name]
atom_indices = []
for chi_angle in residue_chi_angles:
atom_indices.append([rc.atom_order[atom] for atom in chi_angle])
for _ in range(4 - len(atom_indices)):
atom_indices.append(
[0, 0, 0, 0]
) # For chi angles not defined on the AA.
chi_atom_indices.append(atom_indices)
chi_atom_indices.append([[0, 0, 0, 0]] * 4) # For UNKNOWN residue.
return chi_atom_indices
@curry1
def atom37_to_torsion_angles(
protein,
prefix="",
):
"""
Convert coordinates to torsion angles.
This function is extremely sensitive to floating point imprecisions
and should be run with double precision whenever possible.
Args:
Dict containing:
* (prefix)aatype:
[*, N_res] residue indices
* (prefix)all_atom_positions:
[*, N_res, 37, 3] atom positions (in atom37
format)
* (prefix)all_atom_mask:
[*, N_res, 37] atom position mask
Returns:
The same dictionary updated with the following features:
"(prefix)torsion_angles_sin_cos" ([*, N_res, 7, 2])
Torsion angles
"(prefix)alt_torsion_angles_sin_cos" ([*, N_res, 7, 2])
Alternate torsion angles (accounting for 180-degree symmetry)
"(prefix)torsion_angles_mask" ([*, N_res, 7])
Torsion angles mask
"""
aatype = protein[prefix + "aatype"]
all_atom_positions = protein[prefix + "all_atom_positions"]
all_atom_mask = protein[prefix + "all_atom_mask"]
aatype = torch.clamp(aatype, max=20)
pad = all_atom_positions.new_zeros(
[*all_atom_positions.shape[:-3], 1, 37, 3]
)
prev_all_atom_positions = torch.cat(
[pad, all_atom_positions[..., :-1, :, :]], dim=-3
)
pad = all_atom_mask.new_zeros([*all_atom_mask.shape[:-2], 1, 37])
prev_all_atom_mask = torch.cat([pad, all_atom_mask[..., :-1, :]], dim=-2)
pre_omega_atom_pos = torch.cat(
[prev_all_atom_positions[..., 1:3, :], all_atom_positions[..., :2, :]],
dim=-2,
)
phi_atom_pos = torch.cat(
[prev_all_atom_positions[..., 2:3, :], all_atom_positions[..., :3, :]],
dim=-2,
)
psi_atom_pos = torch.cat(
[all_atom_positions[..., :3, :], all_atom_positions[..., 4:5, :]],
dim=-2,
)
pre_omega_mask = torch.prod(
prev_all_atom_mask[..., 1:3], dim=-1
) * torch.prod(all_atom_mask[..., :2], dim=-1)
phi_mask = prev_all_atom_mask[..., 2] * torch.prod(
all_atom_mask[..., :3], dim=-1, dtype=all_atom_mask.dtype
)
psi_mask = (
torch.prod(all_atom_mask[..., :3], dim=-1, dtype=all_atom_mask.dtype)
* all_atom_mask[..., 4]
)
chi_atom_indices = torch.as_tensor(
get_chi_atom_indices(), device=aatype.device
)
atom_indices = chi_atom_indices[..., aatype, :, :]
chis_atom_pos = batched_gather(
all_atom_positions, atom_indices, -2, len(atom_indices.shape[:-2])
)
chi_angles_mask = list(rc.chi_angles_mask)
chi_angles_mask.append([0.0, 0.0, 0.0, 0.0])
chi_angles_mask = all_atom_mask.new_tensor(chi_angles_mask)
chis_mask = chi_angles_mask[aatype, :]
chi_angle_atoms_mask = batched_gather(
all_atom_mask,
atom_indices,
dim=-1,
no_batch_dims=len(atom_indices.shape[:-2]),
)
chi_angle_atoms_mask = torch.prod(
chi_angle_atoms_mask, dim=-1, dtype=chi_angle_atoms_mask.dtype
)
chis_mask = chis_mask * chi_angle_atoms_mask
torsions_atom_pos = torch.cat(
[
pre_omega_atom_pos[..., None, :, :],
phi_atom_pos[..., None, :, :],
psi_atom_pos[..., None, :, :],
chis_atom_pos,
],
dim=-3,
)
torsion_angles_mask = torch.cat(
[
pre_omega_mask[..., None],
phi_mask[..., None],
psi_mask[..., None],
chis_mask,
],
dim=-1,
)
torsion_frames = Rigid.from_3_points(
torsions_atom_pos[..., 1, :],
torsions_atom_pos[..., 2, :],
torsions_atom_pos[..., 0, :],
eps=1e-8,
)
fourth_atom_rel_pos = torsion_frames.invert().apply(
torsions_atom_pos[..., 3, :]
)
torsion_angles_sin_cos = torch.stack(
[fourth_atom_rel_pos[..., 2], fourth_atom_rel_pos[..., 1]], dim=-1
)
denom = torch.sqrt(
torch.sum(
torch.square(torsion_angles_sin_cos),
dim=-1,
dtype=torsion_angles_sin_cos.dtype,
keepdims=True,
)
+ 1e-8
)
torsion_angles_sin_cos = torsion_angles_sin_cos / denom
torsion_angles_sin_cos = torsion_angles_sin_cos * all_atom_mask.new_tensor(
[1.0, 1.0, -1.0, 1.0, 1.0, 1.0, 1.0],
)[((None,) * len(torsion_angles_sin_cos.shape[:-2])) + (slice(None), None)]
chi_is_ambiguous = torsion_angles_sin_cos.new_tensor(
rc.chi_pi_periodic,
)[aatype, ...]
mirror_torsion_angles = torch.cat(
[
all_atom_mask.new_ones(*aatype.shape, 3),
1.0 - 2.0 * chi_is_ambiguous,
],
dim=-1,
)
alt_torsion_angles_sin_cos = (
torsion_angles_sin_cos * mirror_torsion_angles[..., None]
)
protein[prefix + "torsion_angles_sin_cos"] = torsion_angles_sin_cos
protein[prefix + "alt_torsion_angles_sin_cos"] = alt_torsion_angles_sin_cos
protein[prefix + "torsion_angles_mask"] = torsion_angles_mask
return protein
def get_backbone_frames(protein):
# DISCREPANCY: AlphaFold uses tensor_7s here. I don't know why.
protein["backbone_rigid_tensor"] = protein["rigidgroups_gt_frames"][
..., 0, :, :
]
protein["backbone_rigid_mask"] = protein["rigidgroups_gt_exists"][..., 0]
return protein
def get_chi_angles(protein):
dtype = protein["all_atom_mask"].dtype
protein["chi_angles_sin_cos"] = (
protein["torsion_angles_sin_cos"][..., 3:, :]
).to(dtype)
protein["chi_mask"] = protein["torsion_angles_mask"][..., 3:].to(dtype)
return protein
@curry1
def random_crop_to_size(
protein,
crop_size,
max_templates,
shape_schema,
subsample_templates=False,
seed=None,
):
"""Crop randomly to `crop_size`, or keep as is if shorter than that."""
# We want each ensemble to be cropped the same way
g = torch.Generator(device=protein["seq_length"].device)
if seed is not None:
g.manual_seed(seed)
seq_length = protein["seq_length"]
if "template_mask" in protein:
num_templates = protein["template_mask"].shape[-1]
else:
num_templates = 0
# No need to subsample templates if there aren't any
subsample_templates = subsample_templates and num_templates
num_res_crop_size = min(int(seq_length), crop_size)
def _randint(lower, upper):
return int(torch.randint(
lower,
upper + 1,
(1,),
device=protein["seq_length"].device,
generator=g,
)[0])
if subsample_templates:
templates_crop_start = _randint(0, num_templates)
templates_select_indices = torch.randperm(
num_templates, device=protein["seq_length"].device, generator=g
)
else:
templates_crop_start = 0
num_templates_crop_size = min(
num_templates - templates_crop_start, max_templates
)
n = seq_length - num_res_crop_size
if "use_clamped_fape" in protein and protein["use_clamped_fape"] == 1.:
right_anchor = n
else:
x = _randint(0, n)
right_anchor = n - x
num_res_crop_start = _randint(0, right_anchor)
for k, v in protein.items():
if k not in shape_schema or (
"template" not in k and NUM_RES not in shape_schema[k]
):
continue
# randomly permute the templates before cropping them.
if k.startswith("template") and subsample_templates:
v = v[templates_select_indices]
slices = []
for i, (dim_size, dim) in enumerate(zip(shape_schema[k], v.shape)):
is_num_res = dim_size == NUM_RES
if i == 0 and k.startswith("template"):
crop_size = num_templates_crop_size
crop_start = templates_crop_start
else:
crop_start = num_res_crop_start if is_num_res else 0
crop_size = num_res_crop_size if is_num_res else dim
slices.append(slice(crop_start, crop_start + crop_size))
protein[k] = v[slices]
protein["seq_length"] = protein["seq_length"].new_tensor(num_res_crop_size)
return protein
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