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# Copyright Generate Biomedicines, Inc.
#
# 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.
"""Layers for building graph representations of protein structure, all-atom.
This module contains pytorch layers for representing protein structure as a
graph with node and edge features based on geometric information. The graph
features are differentiable with respect to input coordinates and can be used
for building protein scoring functions and optimizing protein geometries
natively in pytorch.
"""
import numpy as np
import torch
import torch.nn as nn
from chroma.layers import graph
from chroma.layers.structure import geometry, sidechain
class NodeChiRBF(nn.Module):
"""Layers for featurizing chi angles with a smooth binning
Args:
num_chi_bins (int): Number of bins for discretizing chi angles.
num_chi (int): Number of chi angles.
dim_out (int): Number of output feature dimensions.
bin_scale (float, optional): Scaling parameter that sets bin smoothing.
Input:
chi (Tensor): Chi angles with shape `(num_batch, num_residues, num_chi)`.
Output:
h_chi (Tensor): Chi angle features with shape
`(num_batch, num_residues, num_chi * num_chi_bins)`.
"""
def __init__(self, dim_out, num_chi, num_chi_bins, bin_scale=2.0):
super(NodeChiRBF, self).__init__()
self.dim_out = dim_out
self.num_chi = num_chi
self.num_chi_bins = num_chi_bins
self.bin_scale = bin_scale
self.embed = nn.Linear(self.num_chi * self.num_chi_bins, dim_out)
def _featurize(self, chi, mask_chi=None):
num_batch, num_residues, _ = chi.shape
chi_bin_center = (
torch.arange(0, self.num_chi_bins, device=chi.device)
* 2.0
* np.pi
/ self.num_chi_bins
)
chi_bin_center = chi_bin_center.reshape([1, 1, 1, -1])
# Set smoothing length scale based on ratio beteen adjacent bin centers
# bin_i / bin_i+1 = 1 / scale
delta_adjacent = np.cos(0.0) - np.cos(2.0 * np.pi / self.num_chi_bins)
cosine = torch.cos(chi.unsqueeze(-1) - chi_bin_center)
chi_features = torch.exp((cosine - 1.0) * self.bin_scale / delta_adjacent)
if mask_chi is not None:
chi_features = mask_chi.unsqueeze(-1) * chi_features
chi_features = chi_features.reshape(
[num_batch, num_residues, self.num_chi * self.num_chi_bins]
)
return chi_features
def forward(self, chi, mask_chi=None):
chi_features = self._featurize(chi, mask_chi=mask_chi)
h_chi = self.embed(chi_features)
return h_chi
class EdgeSidechainsDirect(nn.Module):
"""Layers for direct encoding of side chain geometries.
Args:
dim_out (int): Number of output hidden dimensions.
max_D (float, optional): Maximum distance cutoff for encoding
of edges.
Input:
X (Tensor): All atom coordinates with shape
`(num_batch, num_residues, 14, 3)`.
C (LongTensor): Chain map with shape `(num_batch, num_residues)`.
S (LongTensor): Sequence tensor with shape
`(num_batch, num_residues)`.
edge_idx (Tensor): Graph indices for expansion with shape
`(num_batch, num_residues_out, num_neighbors)`. The dimension
of output variables `num_residues_out` must either equal
`num_residues` or 1, the latter of which can be useful for sequential
decoding.
Output:
h (Tensor): Features with shape
`(num_batch, num_residues_out, num_neighbors, num_hidden)`.
"""
def __init__(
self,
dim_out,
length_scale=7.5,
distance_eps=0.1,
num_fourier=30,
fourier_order=2,
basis_type="rff",
):
super(EdgeSidechainsDirect, self).__init__()
self.dim_out = dim_out
self.length_scale = length_scale
self.distance_eps = distance_eps
# self.embed = nn.Linear(14 * 3 , dim_out)
self.num_fourier = num_fourier
self.rff = torch.nn.Parameter(
2.0 * np.pi / self.length_scale * torch.randn((3, self.num_fourier))
)
self.basis_type = basis_type
if self.basis_type == "rff":
self.embed = nn.Linear(14 * self.num_fourier * 2, dim_out)
elif self.basis_type == "spherical":
self.fourier_order = fourier_order
self.embed = nn.Linear(14 * (self.fourier_order * 2) ** 3, dim_out)
def _local_coordinates(self, X, C, S, edge_idx):
num_batch, num_residues, num_neighbors = edge_idx.shape
# Mask and transform into features
mask_atoms = sidechain.atom_mask(C, S)
mask_atoms_j = graph.collect_neighbors(mask_atoms, edge_idx)
mask_i = (C > 0).float().reshape([num_batch, num_residues, 1, 1])
mask_atoms_ij = mask_i * mask_atoms_j
# Build conditioning mask
R_i, CA = geometry.frames_from_backbone(X[:, :, :4, :])
# Transform neighbor X coordinates into local frames
X_flat = X.reshape([num_batch, num_residues, -1])
X_j_flat = graph.collect_neighbors(X_flat, edge_idx)
X_j = X_j_flat.reshape([num_batch, num_residues, num_neighbors, 14, 3])
dX_ij = X_j - CA.reshape([num_batch, num_residues, 1, 1, 3])
U_ij = torch.einsum("niab,nijma->nijmb", R_i, dX_ij)
return U_ij, mask_atoms_ij
def _local_coordinates_t(self, t, X, C, S, edge_idx_t):
num_batch, _, num_neighbors = edge_idx_t.shape
num_residues = X.shape[1]
# Make a mask that
C_i = C[:, t].unsqueeze(1)
# S_i = S[:,t].unsqueeze(1)
# mask_atoms_i = sidechain.atom_mask(C_i, S_i)
C_j = graph.collect_neighbors(C.unsqueeze(-1), edge_idx_t).reshape(
[num_batch, num_neighbors]
)
S_j = graph.collect_neighbors(S.unsqueeze(-1), edge_idx_t).reshape(
[num_batch, num_neighbors]
)
mask_atoms_j = sidechain.atom_mask(C_j, S_j).unsqueeze(1)
mask_i = (C_i > 0).float().reshape([num_batch, 1, 1, 1])
mask_atoms_ij = mask_i * mask_atoms_j
# Build conditioning mask
X_bb_i = X[:, t, :4, :].unsqueeze(1)
R_i, CA = geometry.frames_from_backbone(X_bb_i)
# Transform neighbor X coordinates into local frames
X_flat = X.reshape([num_batch, num_residues, -1])
X_j_flat = graph.collect_neighbors(X_flat, edge_idx_t)
X_j = X_j_flat.reshape([num_batch, 1, num_neighbors, 14, 3])
dX_ij = X_j - CA.reshape([num_batch, 1, 1, 1, 3])
U_ij = torch.einsum("niab,nijma->nijmb", R_i, dX_ij)
return U_ij, mask_atoms_ij
def _fourier_expand(self, h, order):
k = torch.arange(order, device=h.device)
k = k.reshape([1 for i in h.shape] + [-1])
return torch.cat(
[torch.sin(h.unsqueeze(-1) * (k + 1)), torch.cos(h.unsqueeze(-1) * k)],
dim=-1,
)
def _featurize(self, U_ij, mask_atoms_ij):
if self.basis_type == "rff":
# Random fourier features
U_ij = mask_atoms_ij.unsqueeze(-1) * U_ij
U_ff = torch.einsum("nijax,xy->nijay", U_ij, self.rff)
U_ff = torch.concat([torch.cos(U_ff), torch.sin(U_ff)], -1)
# Gaussian RBF envelope
D_ij = torch.sqrt((U_ij ** 2).sum(-1) + self.distance_eps)
magnitude = torch.exp(-D_ij * D_ij / (2 * self.length_scale ** 2))
U_ff = magnitude.unsqueeze(-1) * U_ff
U_ff = U_ff.reshape(list(D_ij.shape)[:3] + [-1])
h = mask_atoms_ij[:, :, :, 0].unsqueeze(-1) * self.embed(U_ff)
elif self.basis_type == "spherical":
# Convert to spherical coordinates
r_ij = torch.sqrt((U_ij ** 2).sum(-1) + self.distance_eps)
r_ij_scale = r_ij * 2.0 * np.pi / self.length_scale
x, y, z = U_ij.unbind(-1)
theta_ij = torch.acos(z / r_ij)
phi_ij = torch.atan2(y, x)
# Build Fourier expansions of each coordinate
r_ff, theta_ff, phi_ff = [
self._fourier_expand(h, self.fourier_order)
for h in [r_ij_scale, theta_ij, phi_ij]
]
# Radial envelope function
r_envelope = mask_atoms_ij * torch.exp(
-r_ij * r_ij / (2 * self.length_scale ** 2)
)
# Tensor outer product
bf_ij = torch.einsum(
"bika,bikar,bikat,bikap->bikartp", r_envelope, r_ff, theta_ff, phi_ff
).reshape(list(r_ij.shape)[:3] + [-1])
h = mask_atoms_ij[:, :, :, 0].unsqueeze(-1) * self.embed(bf_ij)
return h
def forward(self, X, C, S, edge_idx):
U_ij, mask_atoms_ij = self._local_coordinates(X, C, S, edge_idx)
h = self._featurize(U_ij, mask_atoms_ij)
return h
def step(self, t, X, C, S, edge_idx_t):
U_ij, mask_atoms_ij = self._local_coordinates_t(t, X, C, S, edge_idx_t)
h = self._featurize(U_ij, mask_atoms_ij)
return h
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