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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 argparse | |
from datetime import date | |
import logging | |
import numpy as np | |
import os | |
import pickle | |
import random | |
import sys | |
import time | |
import torch | |
from openfold.config import model_config | |
from openfold.data import templates, feature_pipeline, data_pipeline | |
from openfold.model.model import AlphaFold | |
from openfold.model.torchscript import script_preset_ | |
from openfold.np import residue_constants, protein | |
import openfold.np.relax.relax as relax | |
from openfold.utils.import_weights import ( | |
import_jax_weights_, | |
) | |
from openfold.utils.tensor_utils import ( | |
tensor_tree_map, | |
) | |
from scripts.utils import add_data_args | |
def main(args): | |
config = model_config(args.model_name) | |
model = AlphaFold(config) | |
model = model.eval() | |
import_jax_weights_(model, args.param_path, version=args.model_name) | |
#script_preset_(model) | |
model = model.to(args.model_device) | |
# template_featurizer = templates.TemplateHitFeaturizer( | |
# mmcif_dir=args.template_mmcif_dir, | |
# max_template_date=args.max_template_date, | |
# max_hits=config.data.predict.max_templates, | |
# kalign_binary_path=args.kalign_binary_path, | |
# release_dates_path=args.release_dates_path, | |
# obsolete_pdbs_path=args.obsolete_pdbs_path | |
# ) | |
template_featurizer = None | |
use_small_bfd=(args.bfd_database_path is None) | |
data_processor = data_pipeline.DataPipeline( | |
template_featurizer=template_featurizer, | |
) | |
output_dir_base = args.output_dir | |
random_seed = args.data_random_seed | |
if random_seed is None: | |
random_seed = random.randrange(sys.maxsize) | |
feature_processor = feature_pipeline.FeaturePipeline(config.data) | |
if not os.path.exists(output_dir_base): | |
os.makedirs(output_dir_base) | |
if(args.use_precomputed_alignments is None): | |
alignment_dir = os.path.join(output_dir_base, "alignments") | |
else: | |
alignment_dir = args.use_precomputed_alignments | |
# Gather input sequences | |
with open(args.fasta_path, "r") as fp: | |
data = fp.read() | |
lines = [ | |
l.replace('\n', '') | |
for prot in data.split('>') for l in prot.strip().split('\n', 1) | |
][1:] | |
tags, seqs = lines[::2], lines[1::2] | |
for tag, seq in zip(tags, seqs): | |
fasta_path = os.path.join(args.output_dir, "tmp.fasta") | |
with open(fasta_path, "w") as fp: | |
fp.write(f">{tag}\n{seq}") | |
logging.info("Generating features...") | |
local_alignment_dir = os.path.join(alignment_dir, tag) | |
if(args.use_precomputed_alignments is None): | |
if not os.path.exists(local_alignment_dir): | |
os.makedirs(local_alignment_dir) | |
alignment_runner = data_pipeline.AlignmentRunner( | |
jackhmmer_binary_path=args.jackhmmer_binary_path, | |
hhblits_binary_path=args.hhblits_binary_path, | |
hhsearch_binary_path=args.hhsearch_binary_path, | |
uniref90_database_path=args.uniref90_database_path, | |
mgnify_database_path=args.mgnify_database_path, | |
bfd_database_path=args.bfd_database_path, | |
uniclust30_database_path=args.uniclust30_database_path, | |
pdb70_database_path=args.pdb70_database_path, | |
use_small_bfd=use_small_bfd, | |
no_cpus=args.cpus, | |
) | |
alignment_runner.run( | |
fasta_path, local_alignment_dir | |
) | |
feature_dict = data_processor.process_fasta( | |
fasta_path=fasta_path, alignment_dir=local_alignment_dir | |
) | |
import pickle | |
with open('template_feature_7ku7.pkl', 'rb') as f: | |
template_feature = pickle.load(f) | |
feature_dict = {**feature_dict, **template_feature} | |
# Remove temporary FASTA file | |
os.remove(fasta_path) | |
processed_feature_dict = feature_processor.process_features( | |
feature_dict, mode='predict', | |
) | |
logging.info("Executing model...") | |
batch = processed_feature_dict | |
with torch.no_grad(): | |
batch = { | |
k:torch.as_tensor(v, device=args.model_device) | |
for k,v in batch.items() | |
} | |
t = time.perf_counter() | |
out = model(batch) | |
logging.info(f"Inference time: {time.perf_counter() - t}") | |
# Toss out the recycling dimensions --- we don't need them anymore | |
batch = tensor_tree_map(lambda x: np.array(x[..., -1].cpu()), batch) | |
out = tensor_tree_map(lambda x: np.array(x.cpu()), out) | |
plddt = out["plddt"] | |
mean_plddt = np.mean(plddt) | |
print("Mean plddt for {} is {:.2f}".format(args.model_name, mean_plddt)) | |
plddt_b_factors = np.repeat( | |
plddt[..., None], residue_constants.atom_type_num, axis=-1 | |
) | |
unrelaxed_protein = protein.from_prediction( | |
features=batch, | |
result=out, | |
b_factors=plddt_b_factors | |
) | |
# Save the unrelaxed PDB. | |
unrelaxed_output_path = os.path.join( | |
args.output_dir, f'{tag}_{args.model_name}_unrelaxed.pdb' | |
) | |
with open(unrelaxed_output_path, 'w') as f: | |
f.write(protein.to_pdb(unrelaxed_protein)) | |
if __name__ == "__main__": | |
parser = argparse.ArgumentParser() | |
parser.add_argument( | |
"fasta_path", type=str, | |
) | |
parser.add_argument( | |
"template_mmcif_dir", type=str, | |
) | |
parser.add_argument( | |
"--use_precomputed_alignments", type=str, default=None, | |
help="""Path to alignment directory. If provided, alignment computation | |
is skipped and database path arguments are ignored.""" | |
) | |
parser.add_argument( | |
"--output_dir", type=str, default=os.getcwd(), | |
help="""Name of the directory in which to output the prediction""", | |
) | |
parser.add_argument( | |
"--model_device", type=str, default="cpu", | |
help="""Name of the device on which to run the model. Any valid torch | |
device name is accepted (e.g. "cpu", "cuda:0")""" | |
) | |
parser.add_argument( | |
"--model_name", type=str, default="model_1", | |
help="""Name of a model config. Choose one of model_{1-5} or | |
model_{1-5}_ptm, as defined on the AlphaFold GitHub.""" | |
) | |
parser.add_argument( | |
"--param_path", type=str, default=None, | |
help="""Path to model parameters. If None, parameters are selected | |
automatically according to the model name from | |
openfold/resources/params""" | |
) | |
parser.add_argument( | |
"--save_outputs", type=bool, default=False, | |
help="Whether to save all model outputs, including embeddings, etc." | |
) | |
parser.add_argument( | |
"--cpus", type=int, default=4, | |
help="""Number of CPUs with which to run alignment tools""" | |
) | |
parser.add_argument( | |
'--preset', type=str, default='full_dbs', | |
choices=('reduced_dbs', 'full_dbs') | |
) | |
parser.add_argument( | |
'--data_random_seed', type=str, default=None | |
) | |
add_data_args(parser) | |
args = parser.parse_args() | |
if(args.param_path is None): | |
args.param_path = os.path.join( | |
"openfold", "resources", "params", | |
"params_" + args.model_name + ".npz" | |
) | |
if(args.model_device == "cpu" and torch.cuda.is_available()): | |
logging.warning( | |
"""The model is being run on CPU. Consider specifying | |
--model_device for better performance""" | |
) | |
main(args) |
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