protpardelle / core /data.py
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"""
https://github.com/ProteinDesignLab/protpardelle
License: MIT
Author: Alex Chu
Dataloader from PDB files.
"""
import copy
import pickle
import json
import numpy as np
import torch
from torch.utils import data
from core import utils
from core import protein
from core import residue_constants
FEATURES_1D = (
"coords_in",
"torsions_in",
"b_factors",
"atom_positions",
"aatype",
"atom_mask",
"residue_index",
"chain_index",
)
FEATURES_FLOAT = (
"coords_in",
"torsions_in",
"b_factors",
"atom_positions",
"atom_mask",
"seq_mask",
)
FEATURES_LONG = ("aatype", "residue_index", "chain_index", "orig_size")
def make_fixed_size_1d(data, fixed_size=128):
data_len = data.shape[0]
if data_len >= fixed_size:
extra_len = data_len - fixed_size
start_idx = np.random.choice(np.arange(extra_len + 1))
new_data = data[start_idx : (start_idx + fixed_size)]
mask = torch.ones(fixed_size)
if data_len < fixed_size:
pad_size = fixed_size - data_len
extra_shape = data.shape[1:]
new_data = torch.cat([data, torch.zeros(pad_size, *extra_shape)], 0)
mask = torch.cat([torch.ones(data_len), torch.zeros(pad_size)], 0)
return new_data, mask
def apply_random_se3(coords_in, atom_mask=None, translation_scale=1.0):
# unbatched. center on the mean of CA coords
coords_mean = coords_in[:, 1:2].mean(-3, keepdim=True)
coords_in -= coords_mean
random_rot, _ = torch.linalg.qr(torch.randn(3, 3))
coords_in = coords_in @ random_rot
random_trans = torch.randn_like(coords_mean) * translation_scale
coords_in += random_trans
if atom_mask is not None:
coords_in = coords_in * atom_mask[..., None]
return coords_in
def get_masked_coords_array(coords, atom_mask):
ma_mask = repeat(1 - atom_mask[..., None].cpu().numpy(), "... 1 -> ... 3")
return np.ma.array(coords.cpu().numpy(), mask=ma_mask)
def make_crop_cond_mask_and_recenter_coords(
atom_mask,
atom_coords,
contiguous_prob=0.05,
discontiguous_prob=0.9,
sidechain_only_prob=0.8,
max_span_len=10,
max_discontiguous_res=8,
dist_threshold=8.0,
recenter_coords=True,
):
b, n, a = atom_mask.shape
device = atom_mask.device
seq_mask = atom_mask[..., 1]
n_res = seq_mask.sum(-1)
masks = []
for i, nr in enumerate(n_res):
nr = nr.int().item()
mask = torch.zeros((n, a), device=device)
conditioning_type = torch.distributions.Categorical(
torch.tensor(
[
contiguous_prob,
discontiguous_prob,
1.0 - contiguous_prob - discontiguous_prob,
]
)
).sample()
conditioning_type = ["contiguous", "discontiguous", "none"][conditioning_type]
if conditioning_type == "contiguous":
span_len = torch.randint(
1, min(max_span_len, nr), (1,), device=device
).item()
span_start = torch.randint(0, nr - span_len, (1,), device=device)
mask[span_start : span_start + span_len, :] = 1
elif conditioning_type == "discontiguous":
# Extract CB atoms coordinates for the i-th example
cb_atoms = atom_coords[i, :, 3]
# Pairwise distances between CB atoms
cb_distances = torch.cdist(cb_atoms, cb_atoms)
close_mask = (
cb_distances <= dist_threshold
) # Mask for selecting close CB atoms
random_residue = torch.randint(0, nr, (1,), device=device).squeeze()
cb_dist_i = cb_distances[random_residue] + 1e3 * (1 - seq_mask[i])
close_mask = cb_dist_i <= dist_threshold
n_neighbors = close_mask.sum().int()
# pick how many neighbors (up to 10)
n_sele = torch.randint(
2,
n_neighbors.clamp(min=3, max=max_discontiguous_res + 1),
(1,),
device=device,
)
# Select the indices of CB atoms that are close together
idxs = torch.arange(n, device=device)[close_mask.bool()]
idxs = idxs[torch.randperm(len(idxs))[:n_sele]]
if len(idxs) > 0:
mask[idxs] = 1
if np.random.uniform() < sidechain_only_prob:
mask[:, :5] = 0
masks.append(mask)
crop_cond_mask = torch.stack(masks)
crop_cond_mask = crop_cond_mask * atom_mask
if recenter_coords:
motif_masked_array = get_masked_coords_array(atom_coords, crop_cond_mask)
cond_coords_center = motif_masked_array.mean((1, 2))
motif_mask = torch.Tensor(1 - cond_coords_center.mask).to(crop_cond_mask)
means = torch.Tensor(cond_coords_center.data).to(atom_coords) * motif_mask
coords_out = atom_coords - rearrange(means, "b c -> b 1 1 c")
else:
coords_out = atom_coords
return coords_out, crop_cond_mask
class Dataset(data.Dataset):
"""Loads and processes PDBs into tensors."""
def __init__(
self,
pdb_path,
fixed_size,
mode="train",
overfit=-1,
short_epoch=False,
se3_data_augment=True,
):
self.pdb_path = pdb_path
self.fixed_size = fixed_size
self.mode = mode
self.overfit = overfit
self.short_epoch = short_epoch
self.se3_data_augment = se3_data_augment
with open(f"{self.pdb_path}/{mode}_pdb_keys.list") as f:
self.pdb_keys = np.array(f.read().split("\n")[:-1])
if overfit > 0:
n_data = len(self.pdb_keys)
self.pdb_keys = np.random.choice(
self.pdb_keys, min(n_data, overfit), replace=False
).repeat(n_data // overfit)
def __len__(self):
if self.short_epoch:
return min(len(self.pdb_keys), 256)
else:
return len(self.pdb_keys)
def __getitem__(self, idx):
pdb_key = self.pdb_keys[idx]
data = self.get_item(pdb_key)
# For now, replace dataloading errors with a random pdb. 10 tries
for _ in range(10):
if data is not None:
return data
pdb_key = self.pdb_keys[np.random.randint(len(self.pdb_keys))]
data = self.get_item(pdb_key)
raise Exception("Failed to load data example after 10 tries.")
def get_item(self, pdb_key):
example = {}
if self.pdb_path.endswith("cath_s40_dataset"): # CATH pdbs
data_file = f"{self.pdb_path}/dompdb/{pdb_key}"
elif self.pdb_path.endswith("ingraham_cath_dataset"): # ingraham splits
data_file = f"{self.pdb_path}/pdb_store/{pdb_key}"
else:
raise Exception("Invalid pdb path.")
try:
example = utils.load_feats_from_pdb(data_file)
coords_in = example["atom_positions"]
except FileNotFoundError:
raise Exception(f"File {pdb_key} not found. Check if dataset is corrupted?")
except RuntimeError:
return None
# Apply data augmentation
if self.se3_data_augment:
coords_in = apply_random_se3(coords_in, atom_mask=example["atom_mask"])
orig_size = coords_in.shape[0]
example["coords_in"] = coords_in
example["orig_size"] = torch.ones(1) * orig_size
fixed_size_example = {}
seq_mask = None
for k, v in example.items():
if k in FEATURES_1D:
fixed_size_example[k], seq_mask = make_fixed_size_1d(
v, fixed_size=self.fixed_size
)
else:
fixed_size_example[k] = v
if seq_mask is not None:
fixed_size_example["seq_mask"] = seq_mask
example_out = {}
for k, v in fixed_size_example.items():
if k in FEATURES_FLOAT:
example_out[k] = v.float()
elif k in FEATURES_LONG:
example_out[k] = v.long()
return example_out
def collate(self, example_list):
out = {}
for ex in example_list:
for k, v in ex.items():
out.setdefault(k, []).append(v)
return {k: torch.stack(v) for k, v in out.items()}
def sample(self, n=1, return_data=True, return_keys=False):
keys = self.pdb_keys[torch.randperm(self.__len__())[:n].long()]
if return_keys and not return_data:
return keys
if n == 1:
data = self.collate([self.get_item(keys)])
else:
data = self.collate([self.get_item(key) for key in keys])
if return_data and return_keys:
return data, keys
if return_data and not return_keys:
return data