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b140e2c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 | import os
import math
import numpy as np
from Bio.PDB.Polypeptide import one_to_index
import torch
from torch.utils.data import Dataset
class Collate_Protein_Batch():
@staticmethod
def collate(p_batch):
batch_names = []
batch_aas = []
batch_coords = []
batch_seq_pos = []
batch_axes = []
batch_instance = []
batch_labels = []
batch_weights = []
cur_iter = 0
for protA, protB, label, w in p_batch:
for chain in (protA, protB):
if chain:
batch_names.append(chain[0])
batch_aas.append(chain[1])
batch_coords.append(chain[2])
batch_seq_pos.append(chain[3])
batch_axes.append(chain[4])
batch_instance.append(np.ones_like(chain[1])*cur_iter)
cur_iter += 1
batch_labels.append(label)
batch_weights.append(w)
batch_labels = list(filter(lambda l: l is not None, batch_labels))
return batch_names,\
torch.as_tensor(np.concatenate(batch_aas, axis=0)),\
torch.as_tensor(np.concatenate(batch_coords, axis=0)),\
torch.as_tensor(np.concatenate(batch_seq_pos, axis=0)),\
torch.as_tensor(np.concatenate(batch_axes, axis=0)),\
torch.as_tensor(np.concatenate(batch_instance, axis=0)).to(torch.int32),\
torch.as_tensor(batch_weights),\
torch.as_tensor(batch_labels)
# AA Letter to id
AA1 = "ACDEFGHIKLMNPQRSTVWYX"
AA_TO_ID = {}
for i in range(0, 21):
AA_TO_ID[AA1[i]] = i
def create_datapoint(pdb_code: str, seq: str, coords, w: float = 1):
return (
(
pdb_code,
[AA_TO_ID[aa] for aa in seq],
coords,
list(range(len(seq))),
[],
[]
), None, None, w
)
def collate_batch(p_batch):
return Collate_Protein_Batch.collate(p_batch)
class EnzymeClassDataset(Dataset):
def __init__(
self,
p_path = 'data',
p_data_path = 'chains',
p_dataset = 'training',
p_fastafile = 'chain_list_pdb.fasta',
p_random_seed = None,
p_fold: str = None, # particular fold from 1 to N
p_train_mode = False, # to select all but the given fold (for training)
p_data_aug = False,
p_batch_pairs = False,
p_load_data = False
):
if p_fold is not None and int(p_fold) < 1:
raise Exception("Fold for CV should be a positive integer! Got: " + str(p_fold))
# Random state.
self.random_state_ = np.random.RandomState(p_random_seed)
# Save the data augmentation parameters.
self.data_augment_ = p_data_aug
self.batch_pairs_ = p_batch_pairs
# Get the paths.
self.pdb_folder_ = os.path.join(os.path.join(p_path, p_data_path))
pdb_fasta_file = os.path.join(p_path, p_fastafile)
# Load the sequences from the fasta file
self.list_chains_ = {}
def process_fasta_file(fasta_file, folder):
with open(fasta_file, 'r') as my_fasta_file:
chain_name = ''
for cur_line in my_fasta_file.readlines():
if cur_line.startswith('>'):
chain_name = cur_line.rstrip()[1:]
else:
cur_chain = cur_line.rstrip()
cur_chain_ids = []
for cur_aa in cur_chain:
cur_chain_ids.append(AA_TO_ID[cur_aa])
self.list_chains_[chain_name] = (np.array(cur_chain_ids), folder)
process_fasta_file(pdb_fasta_file, self.pdb_folder_)
# load datapoints
self.datapoints_ = []
with open(os.path.join(p_path, p_dataset+'.csv'), 'r') as labels_map_file:
for cur_line in labels_map_file:
line_split = cur_line.rstrip().split(',')
line_split[2] = float(line_split[2])
line_split[3] = float(line_split[3]) if line_split[3] else 1 # set default weight if not available
# Cross-validation row selection
if p_fold and (line_split[4] == p_fold) == p_train_mode:
continue # do not include this fold
self.datapoints_.append(line_split[:4]) # orig_pdb, mut_pdb, label, weight
if p_load_data:
self.data_ = []
print()
for cur_iter, cur_chain in enumerate(self.list_chains_):
cur_path = os.path.join(cur_chain[2], cur_chain[0]+".npy")
cur_pos_seq_path = os.path.join(cur_chain[2], cur_chain[0]+"_seq_pos.npy")
# cur_axes_path = os.path.join(cur_chain[2], cur_chain[0]+"_axes.npy")
cur_aces_path = []
self.data_.append((np.load(cur_path), np.load(cur_pos_seq_path), np.load(cur_axes_path)))
if cur_iter%100==0:
print("\r Loading {:6d}/{:6d}".format(cur_iter, len(self.list_chains_)), end ="")
print()
else:
self.data_ = None
def __len__(self):
return len(self.datapoints_)
def __getitem__(self, idx):
orig_pdb, mut_pdb, label, weight = self.datapoints_[idx]
orig_path = os.path.join(self.list_chains_[orig_pdb][1], orig_pdb +".npy")
mut_path = os.path.join(self.list_chains_[mut_pdb][1], mut_pdb + ".npy")
# cur_pos_seq_path = os.path.join(self.list_chains_[idx][2], self.list_chains_[idx][0]+"_seq_pos.npy")
# cur_axes_path = os.path.join(self.list_chains_[idx][2], self.list_chains_[idx][0]+"_axes.npy")
cur_axes_path = []
noise = None
def get_pdb(idx, cur_path, label: int):
nonlocal noise
cur_aa_ids = self.list_chains_[idx][0]
if self.data_ is None:
cur_pos = np.load(cur_path)
# cur_seq_pos = np.load(cur_pos_seq_path)
cur_seq_pos = list(range(len(cur_aa_ids)))
cur_axes = []
else:
cur_pos = self.data_[idx][0]
cur_seq_pos = self.data_[idx][1]
cur_axes = self.data_[idx][2]
cur_min = np.amin(cur_pos, axis=0, keepdims=True)
cur_max = np.amax(cur_pos, axis=0, keepdims=True)
center = (cur_max + cur_min)*0.5
cur_pos = cur_pos - center
if self.data_augment_:
if noise is None or not self.batch_pairs_:
noise = self.random_state_.normal(0.0, 0.05, cur_pos.shape)
assert cur_pos.shape == noise.shape
# print(cur_pos)
cur_pos = cur_pos + noise
# print(cur_pos)
return idx, cur_aa_ids, cur_pos, cur_seq_pos, cur_axes
return get_pdb(orig_pdb, orig_path, label), get_pdb(mut_pdb, mut_path, label), label, weight
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