def verify_prefix(verify_subdir=True, **config):
"""
Check if configuration contains a prefix,
and that prefix is a valid directory we
can write to on the filesystem
Parameters
----------
verify_subdir : bool, optional (default: True)
Check if we can create subdirectory containing
full prefix. Set this to False for outer evcouplings
app loop.
**config
Input configuration for pipeline
Returns
-------
prefix : str
Verified prefix
"""
# check we have a prefix entry, otherwise all hope is lost...
try:
prefix = config["global"]["prefix"]
except KeyError:
raise InvalidParameterError(
"Configuration does not include 'prefix' setting in "
"'global' section")
# make sure prefix is also specified
if prefix is None:
raise InvalidParameterError(
"'prefix' must be specified and cannot be None")
# verify that prefix is workable in terms
# of filesystem
try:
# make prefix folder
create_prefix_folders(prefix)
# try if we can write in the folder
with open(prefix + ".test__", "w") as f:
pass
# get rid of the file again
os.remove(prefix + ".test__")
if verify_subdir:
# make sure we can create a subdirectory
sub_prefix = insert_dir(prefix, "test__")
create_prefix_folders(sub_prefix)
# remove again
os.rmdir(path.dirname(sub_prefix))
except OSError as e:
raise InvalidParameterError(
"Not a valid prefix: {}".format(prefix)) from e
return prefix
def complex(**kwargs):
"""
Protocol:
Compare ECs for a complex to
3D structure
Parameters
----------
Mandatory kwargs arguments:
See list below in code where calling check_required
Returns
-------
outcfg : dict
Output configuration of the pipeline, including
the following fields:
* ec_file_compared_all
* ec_file_compared_all_longrange
* pdb_structure_hits
* distmap_monomer
* distmap_multimer
* contact_map_files
* remapped_pdb_files
"""
check_required(kwargs, [
"prefix", "ec_file", "min_sequence_distance", "pdb_mmtf_dir",
"atom_filter", "first_compare_multimer", "second_compare_multimer",
"distance_cutoff", "first_sequence_id", "second_sequence_id",
"first_sequence_file", "second_sequence_file", "first_segments",
"second_segments", "first_target_sequence_file",
"second_target_sequence_file", "scale_sizes"
])
prefix = kwargs["prefix"]
outcfg = {
# initialize output EC files
"ec_compared_all_file": prefix + "_CouplingScoresCompared_all.csv",
"ec_compared_longrange_file":
prefix + "_CouplingScoresCompared_longrange.csv",
"ec_compared_inter_file": prefix + "_CouplingScoresCompared_inter.csv",
# initialize output inter distancemap files
"distmap_inter": prefix + "_distmap_inter",
"inter_contacts_file": prefix + "_inter_contacts_file"
}
# Add PDB comparison files for first and second monomer
for monomer_prefix in ["first", "second"]:
outcfg = {
**outcfg,
monomer_prefix + "_pdb_structure_hits_file":
"{}_{}_structure_hits.csv".format(prefix, monomer_prefix),
monomer_prefix + "_pdb_structure_hits_unfiltered_file":
"{}_{}_structure_hits_unfitered.csv".format(
prefix, monomer_prefix),
monomer_prefix + "_distmap_monomer":
"{}_{}_distance_map_monomer".format(prefix, monomer_prefix),
monomer_prefix + "_distmap_multimer":
"{}_{}_distance_map_multimer".format(prefix, monomer_prefix),
}
# make sure EC file exists
verify_resources("EC file does not exist", kwargs["ec_file"])
# make sure output directory exists
create_prefix_folders(prefix)
# store auxiliary files here (too much for average user)
aux_prefix = insert_dir(prefix, "aux", rootname_subdir=False)
create_prefix_folders(aux_prefix)
# store auxiliary files here (too much for average user)
first_aux_prefix = insert_dir(aux_prefix,
"first_monomer",
rootname_subdir=False)
create_prefix_folders(first_aux_prefix)
# store auxiliary files here (too much for average user)
second_aux_prefix = insert_dir(aux_prefix,
"second_monomer",
rootname_subdir=False)
create_prefix_folders(second_aux_prefix)
# Step 1: Identify 3D structures for comparison
def _identify_monomer_structures(name_prefix, outcfg, aux_prefix):
# create a dictionary with kwargs for just the current monomer
# remove the "prefix" kwargs so that we can replace with the
# aux prefix when calling _identify_structures
# only replace first occurrence of name_prefix
monomer_kwargs = {
k.replace(name_prefix + "_", "", 1): v
for k, v in kwargs.items() if "prefix" not in k
}
# this field needs to be set explicitly else it gets overwritten by concatenated file
monomer_kwargs["alignment_file"] = kwargs[name_prefix +
"_alignment_file"]
monomer_kwargs["raw_focus_alignment_file"] = kwargs[
name_prefix + "_raw_focus_alignment_file"]
# identify structures for that monomer
sifts_map, sifts_map_full = _identify_structures(**monomer_kwargs,
prefix=aux_prefix)
# save selected PDB hits
sifts_map.hits.to_csv(outcfg[name_prefix + "_pdb_structure_hits_file"],
index=False)
# also save full list of hits
sifts_map_full.hits.to_csv(
outcfg[name_prefix + "_pdb_structure_hits_unfiltered_file"],
index=False)
return outcfg, sifts_map
outcfg, first_sifts_map = _identify_monomer_structures(
"first", outcfg, first_aux_prefix)
outcfg, second_sifts_map = _identify_monomer_structures(
"second", outcfg, second_aux_prefix)
# get the segment names from the kwargs
segment_list = kwargs["segments"]
# Make sure user provided exactly two segments
if len(segment_list) != 2:
raise InvalidParameterError(
"Compare stage for protein complexes requires exactly two segments"
)
first_segment_name = kwargs["segments"][0][0]
second_segment_name = kwargs["segments"][1][0]
# Step 2: Compute distance maps
def _compute_monomer_distance_maps(sifts_map, name_prefix, chain_name):
# prepare a sequence map to remap the structures we have found
verify_resources("Target sequence file does not exist",
kwargs[name_prefix + "_target_sequence_file"])
# create target sequence map for remapping structure
with open(kwargs[name_prefix + "_target_sequence_file"]) as f:
header, seq = next(read_fasta(f))
# create target sequence map for remapping structure
seq_id, seq_start, seq_end = parse_header(header)
seqmap = dict(zip(range(seq_start, seq_end + 1), seq))
# compute distance maps and save
# (but only if we found some structure)
if len(sifts_map.hits) > 0:
d_intra = intra_dists(sifts_map,
structures,
atom_filter=kwargs["atom_filter"],
output_prefix=aux_prefix + "_" +
name_prefix + "_distmap_intra")
d_intra.to_file(outcfg[name_prefix + "_distmap_monomer"])
# save contacts to separate file
outcfg[
name_prefix +
"_monomer_contacts_file"] = prefix + "_" + name_prefix + "_contacts_monomer.csv"
d_intra.contacts(kwargs["distance_cutoff"]).to_csv(
outcfg[name_prefix + "_monomer_contacts_file"], index=False)
# compute multimer distances, if requested;
# note that d_multimer can be None if there
# are no structures with multiple chains
if kwargs[name_prefix + "_compare_multimer"]:
d_multimer = multimer_dists(sifts_map,
structures,
atom_filter=kwargs["atom_filter"],
output_prefix=aux_prefix + "_" +
name_prefix + "_distmap_multimer")
else:
d_multimer = None
# if we have a multimer contact map, save it
if d_multimer is not None:
d_multimer.to_file(outcfg[name_prefix + "_distmap_multimer"])
outcfg[
name_prefix +
"_multimer_contacts_file"] = prefix + name_prefix + "_contacts_multimer.csv"
# save contacts to separate file
d_multimer.contacts(kwargs["distance_cutoff"]).to_csv(
outcfg[name_prefix + "_multimer_contacts_file"],
index=False)
else:
outcfg[name_prefix + "_distmap_multimer"] = None
# create remapped structures (e.g. for
# later comparison of folding results)
# remap structures, swap mapping index and filename in
# dictionary so we have a list of files in the dict keys
outcfg[name_prefix + "_remapped_pdb_files"] = {
filename: mapping_index
for mapping_index, filename in remap_chains(
sifts_map,
aux_prefix,
seqmap,
chain_name=chain_name,
raise_missing=kwargs["raise_missing"]).items()
}
else:
# if no structures, cannot compute distance maps
d_intra = None
d_multimer = None
outcfg[name_prefix + "_distmap_monomer"] = None
outcfg[name_prefix + "_distmap_multimer"] = None
outcfg[name_prefix + "remapped_pdb_files"] = None
return d_intra, d_multimer, seqmap
# load all structures for both monomers
all_structures = set(first_sifts_map.hits.pdb_id).union(
set(second_sifts_map.hits.pdb_id))
structures = load_structures(all_structures,
kwargs["pdb_mmtf_dir"],
raise_missing=False)
d_intra_i, d_multimer_i, seqmap_i = _compute_monomer_distance_maps(
first_sifts_map, "first", "A")
d_intra_j, d_multimer_j, seqmap_j = _compute_monomer_distance_maps(
second_sifts_map, "second", "B")
# compute inter distance map if sifts map for each monomer exists
if len(first_sifts_map.hits) > 0 and len(second_sifts_map.hits) > 0:
d_inter = inter_dists(first_sifts_map,
second_sifts_map,
raise_missing=kwargs["raise_missing"])
# if there were overlapping PDBs, save the results
if d_inter is not None:
d_inter.to_file(outcfg["distmap_inter"])
# save contacts to separate file
d_inter.contacts(kwargs["distance_cutoff"]).to_csv(
outcfg["inter_contacts_file"], index=False)
else:
outcfg["inter_contacts_file"] = None
d_inter = None
# # Step 3: Compare ECs to distance maps
ec_table = pd.read_csv(kwargs["ec_file"])
for out_file, min_seq_dist in [
("ec_compared_longrange_file", kwargs["min_sequence_distance"]),
("ec_compared_all_file", 0),
]:
# compare ECs only if we have an intra distance map
# for at least one monomer - inter can't exist unless
# we have both monomers
if (d_intra_i is not None) or (d_intra_j is not None):
# compare distances individually for each segment pair
ecs_intra_i = ec_table.query(
"segment_i == segment_j == @first_segment_name")
if d_intra_i is not None:
ecs_intra_i_compared = coupling_scores_compared(
ecs_intra_i,
d_intra_i,
d_multimer_i,
dist_cutoff=kwargs["distance_cutoff"],
output_file=None,
min_sequence_dist=min_seq_dist)
else:
# If no distance map, the distance is saved as np.nan
ecs_intra_i_compared = ecs_intra_i.assign(dist=np.nan)
ecs_intra_j = ec_table.query(
"segment_i == segment_j == @second_segment_name")
if d_intra_j is not None:
ecs_intra_j_compared = coupling_scores_compared(
ecs_intra_j,
d_intra_j,
d_multimer_j,
dist_cutoff=kwargs["distance_cutoff"],
output_file=None,
min_sequence_dist=min_seq_dist)
else:
ecs_intra_j_compared = ecs_intra_j.assign(dist=np.nan)
ecs_inter = ec_table.query("segment_i != segment_j")
if d_inter is not None:
ecs_inter_compared = coupling_scores_compared(
ecs_inter,
d_inter,
dist_map_multimer=None,
dist_cutoff=kwargs["distance_cutoff"],
output_file=None,
min_sequence_dist=
None # does not apply for inter-protein ECs
)
else:
ecs_inter_compared = ecs_inter.assign(dist=np.nan)
# combine the tables
ec_table_compared = pd.concat([
ecs_inter_compared, ecs_intra_i_compared, ecs_intra_j_compared
])
# rename the precision column to "segmentwise_precision"
# because we calculated precision for each segment independently
ec_table_compared = ec_table_compared.rename(
columns={"precision": "segmentwise_precision"})
# TODO: change "cn" to "score" eventually
ec_table_compared = ec_table_compared.sort_values("cn",
ascending=False)
# add the total precision
# TODO: implement different cutoffs for intra vs inter contacts
ec_table_compared = add_precision(
ec_table_compared, dist_cutoff=kwargs["distance_cutoff"])
# save to file
# all ecs
ec_table_compared.to_csv(outcfg[out_file])
# save the inter ECs to a file
ecs_inter_compared.to_csv(outcfg["ec_compared_inter_file"])
# create the inter-ecs line drawing script
if outcfg["ec_compared_inter_file"] is not None and kwargs[
"plot_highest_count"] is not None:
inter_ecs = ec_table.query("segment_i != segment_j")
outcfg[
"ec_lines_compared_pml_file"] = prefix + "_draw_ec_lines_compared.pml"
pairs.ec_lines_pymol_script(
inter_ecs.iloc[:kwargs["plot_highest_count"], :],
outcfg["ec_lines_compared_pml_file"],
distance_cutoff=kwargs["distance_cutoff"],
chain={
first_segment_name: "A",
second_segment_name: "B"
})
# Remap the complex crystal structures, if available
if len(first_sifts_map.hits) > 0 and len(second_sifts_map.hits) > 0:
outcfg["complex_remapped_pdb_files"] = {
filename: mapping_index
for mapping_index, filename in remap_complex_chains(
first_sifts_map,
second_sifts_map,
seqmap_i,
seqmap_j,
output_prefix=aux_prefix,
raise_missing=kwargs["raise_missing"]).items()
}
# Step 4: Make contact map plots
# if no structures available, defaults to EC-only plot
outcfg["contact_map_files"] = _make_complex_contact_maps(
ec_table, d_intra_i, d_multimer_i, d_intra_j, d_multimer_j, d_inter,
first_segment_name, second_segment_name, **kwargs)
return outcfg
def standard(**kwargs):
"""
Protocol:
Compare ECs for single proteins (or domains)
to 3D structure information
Parameters
----------
Mandatory kwargs arguments:
See list below in code where calling check_required
Returns
-------
outcfg : dict
Output configuration of the pipeline, including
the following fields:
* ec_file_compared_all
* ec_file_compared_all_longrange
* pdb_structure_hits
* distmap_monomer
* distmap_multimer
* contact_map_files
* remapped_pdb_files
"""
check_required(kwargs, [
"prefix",
"ec_file",
"min_sequence_distance",
"pdb_mmtf_dir",
"atom_filter",
"compare_multimer",
"distance_cutoff",
"target_sequence_file",
"scale_sizes",
])
prefix = kwargs["prefix"]
outcfg = {
"ec_compared_all_file": prefix + "_CouplingScoresCompared_all.csv",
"ec_compared_longrange_file":
prefix + "_CouplingScoresCompared_longrange.csv",
"pdb_structure_hits_file": prefix + "_structure_hits.csv",
"pdb_structure_hits_unfiltered_file":
prefix + "_structure_hits_unfiltered.csv",
# cannot have the distmap files end with "_file" because there are
# two files (.npy and .csv), which would cause problems with automatic
# checking if those files exist
"distmap_monomer": prefix + "_distance_map_monomer",
"distmap_multimer": prefix + "_distance_map_multimer",
}
# make sure EC file exists
verify_resources("EC file does not exist", kwargs["ec_file"])
# make sure output directory exists
create_prefix_folders(prefix)
# store auxiliary files here (too much for average user)
aux_prefix = insert_dir(prefix, "aux", rootname_subdir=False)
create_prefix_folders(aux_prefix)
# Step 1: Identify 3D structures for comparison
sifts_map, sifts_map_full = _identify_structures(
**{
**kwargs,
"prefix": aux_prefix,
})
# save selected PDB hits
sifts_map.hits.to_csv(outcfg["pdb_structure_hits_file"], index=False)
# also save full list of hits
sifts_map_full.hits.to_csv(outcfg["pdb_structure_hits_unfiltered_file"],
index=False)
# Step 2: Compute distance maps
# load all structures at once
structures = load_structures(sifts_map.hits.pdb_id,
kwargs["pdb_mmtf_dir"],
raise_missing=False)
# compute distance maps and save
# (but only if we found some structure)
if len(sifts_map.hits) > 0:
d_intra = intra_dists(sifts_map,
structures,
atom_filter=kwargs["atom_filter"],
output_prefix=aux_prefix + "_distmap_intra")
d_intra.to_file(outcfg["distmap_monomer"])
# save contacts to separate file
outcfg["monomer_contacts_file"] = prefix + "_contacts_monomer.csv"
d_intra.contacts(kwargs["distance_cutoff"]).to_csv(
outcfg["monomer_contacts_file"], index=False)
# compute multimer distances, if requested;
# note that d_multimer can be None if there
# are no structures with multiple chains
if kwargs["compare_multimer"]:
d_multimer = multimer_dists(sifts_map,
structures,
atom_filter=kwargs["atom_filter"],
output_prefix=aux_prefix +
"_distmap_multimer")
else:
d_multimer = None
# if we have a multimer contact mapin the end, save it
if d_multimer is not None:
d_multimer.to_file(outcfg["distmap_multimer"])
outcfg[
"multimer_contacts_file"] = prefix + "_contacts_multimer.csv"
# save contacts to separate file
d_multimer.contacts(kwargs["distance_cutoff"]).to_csv(
outcfg["multimer_contacts_file"], index=False)
else:
outcfg["distmap_multimer"] = None
# at this point, also create remapped structures (e.g. for
# later comparison of folding results)
verify_resources("Target sequence file does not exist",
kwargs["target_sequence_file"])
# create target sequence map for remapping structure
with open(kwargs["target_sequence_file"]) as f:
header, seq = next(read_fasta(f))
seq_id, seq_start, seq_end = parse_header(header)
seqmap = dict(zip(range(seq_start, seq_end + 1), seq))
# remap structures, swap mapping index and filename in
# dictionary so we have a list of files in the dict keys
outcfg["remapped_pdb_files"] = {
filename: mapping_index
for mapping_index, filename in remap_chains(
sifts_map, aux_prefix, seqmap).items()
}
else:
# if no structures, can not compute distance maps
d_intra = None
d_multimer = None
outcfg["distmap_monomer"] = None
outcfg["distmap_multimer"] = None
outcfg["remapped_pdb_files"] = None
# Step 3: Compare ECs to distance maps
ec_table = pd.read_csv(kwargs["ec_file"])
# identify number of sites in EC model
num_sites = len(
set.union(set(ec_table.i.unique()), set(ec_table.j.unique())))
for out_file, min_seq_dist in [
("ec_compared_longrange_file", kwargs["min_sequence_distance"]),
("ec_compared_all_file", 0),
]:
# compare ECs only if we minimally have intra distance map
if d_intra is not None:
coupling_scores_compared(ec_table,
d_intra,
d_multimer,
dist_cutoff=kwargs["distance_cutoff"],
output_file=outcfg[out_file],
min_sequence_dist=min_seq_dist)
else:
outcfg[out_file] = None
# also create line-drawing script if we made the csv
if outcfg["ec_compared_longrange_file"] is not None:
ecs_longrange = pd.read_csv(outcfg["ec_compared_longrange_file"])
outcfg[
"ec_lines_compared_pml_file"] = prefix + "_draw_ec_lines_compared.pml"
pairs.ec_lines_pymol_script(ecs_longrange.iloc[:num_sites, :],
outcfg["ec_lines_compared_pml_file"],
distance_cutoff=kwargs["distance_cutoff"])
# Step 4: Make contact map plots
# if no structures available, defaults to EC-only plot
outcfg["contact_map_files"] = _make_contact_maps(ec_table, d_intra,
d_multimer, **kwargs)
return outcfg
def execute(**config):
"""
Execute a pipeline configuration
Parameters
----------
**config
Input configuration for pipeline
(see pipeline config files for
example of how this should look like)
Returns
-------
global_state : dict
Global output state of pipeline
"""
check_required(config, ["pipeline", "stages", "global"])
# check if valid pipeline was selected
if config["pipeline"] not in PIPELINES:
raise InvalidParameterError("Not a valid pipeline selection. "
"Valid choices are:\n{}".format(", ".join(
PIPELINES.keys())))
stages = config["stages"]
if stages is None:
raise InvalidParameterError("No stages defined, need at least one.")
# get definition of selected pipeline
pipeline = PIPELINES[config["pipeline"]]
prefix = config["global"]["prefix"]
# make sure output directory exists
create_prefix_folders(prefix)
# this is the global state of results as
# we move through different stages of
# the pipeline
global_state = config["global"]
# keep track of how many stages are still
# to be run, so we can leave out stages at
# the end of workflow below
num_stages_to_run = len(stages)
# get job tracker
tracker = get_result_tracker(config)
# set job status to running and also initalize global state
tracker.update(status=EStatus.RUN, results=global_state)
# iterate through individual stages
for (stage, runner, key_prefix) in pipeline:
# check if anything else is left to
# run, otherwise skip
if num_stages_to_run == 0:
break
# check if config for stage is there
check_required(config, [stage])
# output files for stage into an individual folder
stage_prefix = insert_dir(prefix, stage)
create_prefix_folders(stage_prefix)
# config files for input and output of stage
stage_incfg = "{}_{}.incfg".format(stage_prefix, stage)
stage_outcfg = "{}_{}.outcfg".format(stage_prefix, stage)
# update current stage of job
tracker.update(stage=stage)
# check if stage should be executed
if stage in stages:
# global state inserted at end, overrides any
# stage-specific settings (except for custom prefix)
incfg = {
**config["tools"],
**config["databases"],
**config[stage],
**global_state, "prefix": stage_prefix
}
# save input of stage in config file
write_config_file(stage_incfg, incfg)
# run stage
outcfg = runner(**incfg)
# prefix output keys if this parameter is
# given in stage configuration, to avoid
# name clashes if same protocol run multiple times
if key_prefix is not None:
outcfg = {key_prefix + k: v for k, v in outcfg.items()}
# save output of stage in config file
write_config_file(stage_outcfg, outcfg)
# one less stage to put through after we ran this...
num_stages_to_run -= 1
else:
# skip state by injecting state from previous run
verify_resources(
"Trying to skip, but output configuration "
"for stage '{}' does not exist. Has it already "
"been run?".format(stage, stage), stage_outcfg)
# read output configuration
outcfg = read_config_file(stage_outcfg)
# verify all the output files are there
outfiles = [
filepath for f, filepath in outcfg.items()
if f.endswith("_file") and filepath is not None
]
verify_resources(
"Output files from stage '{}' "
"missing".format(stage), *outfiles)
# update global state with outputs of stage
global_state = {**global_state, **outcfg}
# update state in tracker accordingly
tracker.update(results=outcfg)
# create results archive
archive_file = create_archive(config, global_state, prefix)
# only store results archive if a result file was created
if archive_file is not None:
global_state["archive_file"] = archive_file
# prepare update for tracker, but only store in last
# go when job is set to done
tracker_archive_update = {"archive_file": archive_file}
else:
tracker_archive_update = None
# set job status to done and transfer archive if selected for syncing
tracker.update(status=EStatus.DONE, results=tracker_archive_update)
# delete selected output files if requested;
# tracker does not need to update here since it won't
# sync entries of delete list in the first place
global_state = delete_outputs(config, global_state)
# write final global state of pipeline
write_config_file(prefix + FINAL_CONFIG_SUFFIX, global_state)
return global_state
def standard(**kwargs):
"""
Protocol:
Predict 3D structure from evolutionary couplings
Parameters
----------
Mandatory kwargs arguments:
See list below in code where calling check_required
Returns
-------
outcfg : dict
Output configuration of the pipeline, including
the following fields:
* sec_struct_file
* folding_ec_file
* folded_structure_files
"""
check_required(
kwargs,
[
"prefix", "engine", "ec_file", "target_sequence_file",
"segments", "folding_config_file", "cut_to_alignment_region",
"sec_struct_method", "reuse_sec_struct",
"sec_struct_file", "filter_sec_struct_clashes",
"min_sequence_distance", "fold_probability_cutoffs",
"fold_lowest_count", "fold_highest_count", "fold_increase",
"num_models", "psipred", "cpu", "remapped_pdb_files",
"cleanup",
]
)
prefix = kwargs["prefix"]
# make sure output directory exists
create_prefix_folders(prefix)
outcfg = {
"folding_ec_file": prefix + "_CouplingScores_with_clashes.csv",
"sec_struct_file": prefix + "_secondary_structure.csv",
}
# get secondary structure prediction
# check if we should (and can) reuse output file from previous run
if kwargs["reuse_sec_struct"] and valid_file(outcfg["sec_struct_file"]):
residues = pd.read_csv(outcfg["sec_struct_file"])
else:
residues = secondary_structure(**kwargs)
# make pymol secondary structure assignment script
outcfg["secondary_structure_pml_file"] = prefix + "_ss_draw.pml"
pymol_secondary_structure(
residues, outcfg["secondary_structure_pml_file"]
)
# load ECs and filter for long-range pairs
verify_resources(
"EC file does not exist", kwargs["ec_file"]
)
ecs_all = pd.read_csv(kwargs["ec_file"])
ecs = ecs_all.query("abs(i - j) > {}".format(
kwargs["min_sequence_distance"])
)
# find secondary structure clashes
ecs = secstruct_clashes(ecs, residues)
ecs.to_csv(outcfg["folding_ec_file"], index=False)
# if requested, filter clashes out before folding
if kwargs["filter_sec_struct_clashes"]:
ecs_fold = ecs.loc[~ecs.ss_clash]
else:
ecs_fold = ecs
# cut modelled region to aligned region, if selected
if kwargs["cut_to_alignment_region"]:
segments = kwargs["segments"]
# infer region from segment positions if we have it
if segments is not None:
positions = Segment.from_list(segments[0]).positions
else:
# otherwise get from EC values (could be misleading if
# EC list is truncated, so only second option)
positions = set(ecs.i.unique()).union(ecs.j.unique())
# limit modelled positions to covered region
first_pos, last_pos = min(positions), max(positions)
residues.loc[:, "in_model"] = False
residues.loc[
(residues.i >= first_pos) & (residues.i <= last_pos),
"in_model"
] = True
else:
# otherwise include all positions in model
residues.loc[:, "in_model"] = True
# save secondary structure prediction
residues.to_csv(outcfg["sec_struct_file"], index=False)
# only use the residues that will be in model for folding
residues_fold = residues.loc[residues.in_model]
# after all the setup, now fold the structures...
# to speed things up, parallelize this to the number of
# available CPUs
num_procs = kwargs["cpu"]
if num_procs is None:
num_procs = 1
# first define all the sub-runs...
folding_runs = []
# ... based on mixture model probability
cutoffs = kwargs["fold_probability_cutoffs"]
if cutoffs is not None and "probability" in ecs_fold.columns:
if not isinstance(cutoffs, list):
cutoffs = [cutoffs]
for c in cutoffs:
sig_ecs = ecs_fold.query("probability >= @c")
if len(sig_ecs) > 0:
folding_runs.append(
(sig_ecs,
"_significant_ECs_{}".format(c))
)
# ... and on simple EC counts/bins
flc = kwargs["fold_lowest_count"]
fhc = kwargs["fold_highest_count"]
fi = kwargs["fold_increase"]
if flc is not None and fhc is not None and fi is not None:
num_sites = len(
set.union(set(ecs.i.unique()), set(ecs.j.unique()))
)
# transform fraction of number of sites into discrete number of ECs
def _discrete_count(x):
if isinstance(x, float):
x = ceil(x * num_sites)
return int(x)
# range of plots to make
lowest = _discrete_count(flc)
highest = _discrete_count(fhc)
step = _discrete_count(fi)
# append to list of jobs to run
folding_runs += [
(
ecs_fold.iloc[:c],
"_{}".format(c)
)
for c in range(lowest, highest + 1, step)
]
# set up method to drive the folding of each job
method = kwargs["engine"]
# store structures in an auxiliary subdirectory, after folding
# final models will be moved to main folding dir. Depending
# on cleanup setting, the aux directory will be removed
aux_prefix = insert_dir(prefix, "aux", rootname_subdir=False)
aux_dir = path.dirname(aux_prefix)
folding_runs = [
(job_ecs, aux_prefix + job_suffix)
for (job_ecs, job_suffix) in folding_runs
]
if method == "cns_dgsa":
folder = partial(
cns_dgsa_fold,
residues_fold,
config_file=kwargs["folding_config_file"],
num_structures=kwargs["num_models"],
log_level=None,
binary=kwargs["cns"]
)
else:
raise InvalidParameterError(
"Invalid folding engine: {} ".format(method) +
"Valid selections are: cns_dgsa"
)
# then apply folding function to each sub-run
pool = mp.Pool(processes=num_procs)
results = pool.starmap(folder, folding_runs)
# make double sure that the pool is cleaned up,
# or SIGTERM upon exit will interfere with
# interrupt signal interception
pool.close()
pool.join()
# merge result dictionaries into one dict
folded_files = {
k: v for subres in results for k, v in subres.items()
}
# move structures from aux into main folding dir
fold_dir = path.dirname(prefix)
prediction_files = []
for name, file_path in folded_files.items():
# move file (use copy to allow overwriting)
shutil.copy(file_path, fold_dir)
# update file path to main folding dir,
# and put in a flat list of result files
prediction_files.append(
file_path.replace(aux_prefix, prefix)
)
outcfg["folded_structure_files"] = prediction_files
# remove aux dir if cleanup is requested
if kwargs["cleanup"]:
shutil.rmtree(aux_dir)
# apply ranking to predicted models
ranking = dihedral_ranking(prediction_files, residues)
# apply clustering (all available methods), but only
# if we have something to cluster
if len(prediction_files) > 1:
clustering = maxcluster_clustering_table(
prediction_files, binary=kwargs["maxcluster"]
)
# join ranking with clustering
ranking = ranking.merge(clustering, on="filename", how="left")
# sort by score (best models first)
ranking = ranking.sort_values(by="ranking_score", ascending=False)
# store as file
outcfg["folding_ranking_file"] = prefix + "_ranking.csv"
ranking.to_csv(outcfg["folding_ranking_file"], index=False)
# apply comparison to existing structures
if kwargs["remapped_pdb_files"] is not None and len(kwargs["remapped_pdb_files"]) > 0:
experimental_files = kwargs["remapped_pdb_files"]
comp_all, comp_singles = compare_models_maxcluster(
list(experimental_files.keys()), prediction_files,
norm_by_intersection=True, distance_cutoff=None,
binary=kwargs["maxcluster"]
)
# merge with ranking and save
comparison = ranking.merge(
comp_all, on="filename", how="left"
).sort_values(by="tm", ascending=False)
outcfg["folding_comparison_file"] = prefix + "_comparison.csv"
comparison.to_csv(outcfg["folding_comparison_file"], index=False)
# also store comparison to structures in individual files
ind_comp_files = {}
for filename, comp_single in comp_singles.items():
comparison_s = ranking.merge(
comp_single, on="filename", how="left"
).sort_values(by="tm", ascending=False)
basename = path.splitext(path.split(filename)[1])[0]
ind_file = path.join(fold_dir, basename + ".csv")
# map back to original key from remapped_pdb_files as a key for this list
ind_comp_files[ind_file] = experimental_files[filename]
comparison_s.to_csv(ind_file, index=False)
outcfg["folding_individual_comparison_files"] = ind_comp_files
return outcfg
