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alphafold_optimized_monomer.py
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/
alphafold_optimized_monomer.py
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# Copyright 2021 Google LLC
#
# 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.
"""Monomer-optimized Alphafold Inference Pipeline."""
from google_cloud_pipeline_components.v1.custom_job import create_custom_training_job_from_component
from kfp.v2 import dsl
import config as config
from components import aggregate_features as AggregateOp
from components import configure_run as ConfigureRunOp
from components import hhblits
from components import hhsearch
from components import jackhmmer
from components import predict as PredictOp
from components import relax as RelaxOp
JackhmmerOp = create_custom_training_job_from_component(
jackhmmer,
display_name='Jackhmmer',
machine_type=config.JACKHMMER_MACHINE_TYPE,
nfs_mounts=[dict(
server=config.NFS_SERVER,
path=config.NFS_PATH,
mountPoint=config.NFS_MOUNT_POINT)],
network=config.NETWORK
)
HHblitsOp = create_custom_training_job_from_component(
hhblits,
display_name='HHblits',
machine_type=config.HHBLITS_MACHINE_TYPE,
nfs_mounts=[dict(
server=config.NFS_SERVER,
path=config.NFS_PATH,
mountPoint=config.NFS_MOUNT_POINT)],
network=config.NETWORK
)
HHsearchOp = create_custom_training_job_from_component(
hhsearch,
display_name='HHsearch',
machine_type=config.HHSEARCH_MACHINE_TYPE,
nfs_mounts=[dict(
server=config.NFS_SERVER,
path=config.NFS_PATH,
mountPoint=config.NFS_MOUNT_POINT)],
network=config.NETWORK
)
JobPredictOp = create_custom_training_job_from_component(
PredictOp,
display_name = 'Predict',
machine_type = config.PREDICT_MACHINE_TYPE,
accelerator_type = config.PREDICT_ACCELERATOR_TYPE,
accelerator_count = config.PREDICT_ACCELERATOR_COUNT
)
JobRelaxOp = create_custom_training_job_from_component(
RelaxOp,
display_name = 'Relax',
machine_type = config.RELAX_MACHINE_TYPE,
accelerator_type = config.RELAX_ACCELERATOR_TYPE,
accelerator_count = config.RELAX_ACCELERATOR_COUNT
)
@dsl.pipeline(
name='alphafold-monomer-optimized',
description='AlphaFold monomer inference using parallized MSA search.'
)
def alphafold_monomer_pipeline(
sequence_path: str,
project: str,
region: str,
max_template_date: str,
uniref_max_hits: int = config.UNIREF_MAX_HITS,
mgnify_max_hits: int = config.MGNIFY_MAX_HITS,
is_run_relax: str = 'relax'
):
"""Monomer-optimized Alphafold Inference Pipeline."""
run_config = ConfigureRunOp(
sequence_path=sequence_path,
model_preset='monomer',
).set_display_name('Configure Pipeline Run')
model_parameters = dsl.importer(
artifact_uri=config.MODEL_PARAMS_GCS_LOCATION,
artifact_class=dsl.Artifact,
reimport=True)
model_parameters.set_display_name('Model parameters')
reference_databases = dsl.importer(
artifact_uri=config.NFS_MOUNT_POINT,
artifact_class=dsl.Dataset,
reimport=False,
metadata={
'uniref90': config.UNIREF90_PATH,
'mgnify': config.MGNIFY_PATH,
'bfd': config.BFD_PATH,
'small_bfd': config.SMALL_BFD_PATH,
'uniref30': config.UNIREF30_PATH,
'pdb70': config.PDB70_PATH,
'pdb_mmcif': config.PDB_MMCIF_PATH,
'pdb_obsolete': config.PDB_OBSOLETE_PATH,
'pdb_seqres': config.PDB_SEQRES_PATH,
'uniprot': config.UNIPROT_PATH,
}
).set_display_name('Reference databases')
search_uniref = JackhmmerOp(
project=project,
location=region,
database='uniref90',
ref_databases=reference_databases.output,
sequence=run_config.outputs['sequence'],
maxseq=uniref_max_hits,
)
search_uniref.set_display_name('Search Uniref')
search_mgnify = JackhmmerOp(
project=project,
location=region,
database='mgnify',
ref_databases=reference_databases.output,
sequence=run_config.outputs['sequence'],
maxseq=mgnify_max_hits
)
search_mgnify.set_display_name('Search Mgnify')
search_uniclust = HHblitsOp(
project=project,
location=region,
databases=['uniref30'],
ref_databases=reference_databases.output,
sequence=run_config.outputs['sequence'],
)
search_uniclust.set_display_name('Search Uniclust')
search_bfd = HHblitsOp(
project=project,
location=region,
databases=['bfd'],
ref_databases=reference_databases.output,
sequence=run_config.outputs['sequence'],
)
search_bfd.set_display_name('Search BFD')
search_pdb = HHsearchOp(
project=project,
location=region,
template_dbs=['pdb70'],
mmcif_db='pdb_mmcif',
obsolete_db='pdb_obsolete',
max_template_date=max_template_date,
ref_databases=reference_databases.output,
sequence=run_config.outputs['sequence'],
msa=search_uniref.outputs['msa'],
)
search_pdb.set_display_name('Search Pdb')
aggregate_features = AggregateOp(
sequence=run_config.outputs['sequence'],
msa1=search_uniref.outputs['msa'],
msa2=search_mgnify.outputs['msa'],
msa3=search_bfd.outputs['msa'],
msa4=search_uniclust.outputs['msa'],
template_features=search_pdb.outputs['template_features'],
)
aggregate_features.set_display_name('Aggregate features')
with dsl.ParallelFor(run_config.outputs['model_runners']) as model_runner:
model_predict = JobPredictOp(
project=project,
location=region,
model_features=aggregate_features.outputs['features'],
model_params=model_parameters.output,
model_name=model_runner.model_name,
prediction_index=model_runner.prediction_index,
run_multimer_system=run_config.outputs['run_multimer_system'],
num_ensemble=run_config.outputs['num_ensemble'],
random_seed=model_runner.random_seed,
tf_force_unified_memory=config.TF_FORCE_UNIFIED_MEMORY,
xla_python_client_mem_fraction=config.XLA_PYTHON_CLIENT_MEM_FRACTION
)
model_predict.set_display_name('Predict')
with dsl.Condition(is_run_relax == 'relax'):
relax_protein = JobRelaxOp(
project=project,
location=region,
unrelaxed_protein=model_predict.outputs['unrelaxed_protein'],
use_gpu=True,
tf_force_unified_memory=config.TF_FORCE_UNIFIED_MEMORY,
xla_python_client_mem_fraction=config.XLA_PYTHON_CLIENT_MEM_FRACTION
)
relax_protein.set_display_name('Relax protein')