def get_region_name_dict(m, unique_regions, keep_list=None):
region_name_dict = {}
chainid_list = []
from mmtbx.secondary_structure.find_ss_from_ca import get_chain_id
chainid = get_chain_id(m.get_hierarchy()).strip()
if not keep_list:
keep_list = len(unique_regions) * [True]
assert len(keep_list) == len(unique_regions)
i = 0
for region_number, keep in zip(unique_regions, keep_list):
if not keep: continue
i += 1
if len(chainid) == 1 and i < 10:
region_name = "%s%s" % (chainid, i)
else:
region_name = str(i)
region_name_dict[region_number] = region_name
chainid_list.append(region_name)
return region_name_dict, chainid_list
def run(
params=None, # params for running from command line
map_data=None, # map_data, as_double()
pdb_inp=None,
pdb_hierarchy=None,
crystal_symmetry=None,
resolution=None,
scattering_table='n_gaussian',
smoothing_window=5,
crossover_atom='CA',
minimum_matching_atoms=3,
minimum_length=2,
dist_max=1.0,
minimum_improvement=0.01,
max_regions_to_test=10,
max_ends_per_region=5,
maximum_fraction=0.5,
max_keep=10,
map_coeffs_file=None,map_coeffs_labels=None,
pdb_in_file=None,
pdb_out=None,
verbose=None,
out=sys.stdout):
if out is None: out=sys.stdout # explode and refine calls it this way
# get info from params if present
if params:
verbose=params.control.verbose
map_coeffs_file=params.input_files.map_coeffs_file
map_coeffs_labels=params.input_files.map_coeffs_labels
pdb_in_file=params.input_files.pdb_in_file
resolution=params.crystal_info.resolution
scattering_table=params.crystal_info.scattering_table
smoothing_window=params.crossover.smoothing_window
crossover_atom=params.crossover.crossover_atom
minimum_matching_atoms=params.crossover.minimum_matching_atoms
minimum_length=params.crossover.minimum_length
dist_max=params.crossover.dist_max
minimum_improvement=params.crossover.minimum_improvement
max_regions_to_test=params.crossover.max_regions_to_test
max_ends_per_region=params.crossover.max_ends_per_region
maximum_fraction=params.crossover.maximum_fraction
max_keep=params.crossover.max_keep
pdb_out=params.output_files.pdb_out
# Consistency checks
if(pdb_hierarchy is not None):
assert pdb_in_file is None
assert pdb_inp is None
assert crystal_symmetry is not None
# XXX more checks here!
# Get map_data if not present
if not map_data:
if not map_coeffs_file or not os.path.isfile(map_coeffs_file):
raise Sorry("Cannot find the map_coeffs_file '%s'" %(
str(map_coeffs_file)))
from mmtbx.building.minimize_chain import get_map_coeffs
map_coeffs=get_map_coeffs(map_coeffs_file,
map_coeffs_labels=map_coeffs_labels)
fft_map = map_coeffs.fft_map(resolution_factor = 0.25)
fft_map.apply_sigma_scaling()
map_data = fft_map.real_map_unpadded()
map_data=map_data.as_double()
if map_coeffs and not crystal_symmetry:
crystal_symmetry=map_coeffs.crystal_symmetry()
if map_coeffs and not resolution:
resolution=map_coeffs.d_min()
# Get the starting model
if(pdb_hierarchy is None):
if pdb_inp is None:
if not pdb_in_file or not os.path.isfile(pdb_in_file):
raise Sorry("Cannot read input PDB file '%s'" %(
str(pdb_in_file)))
else:
print("Taking models from %s" %(pdb_in_file), file=out)
pdb_string=open(pdb_in_file).read()
pdb_inp=iotbx.pdb.input(source_info=None, lines = pdb_string)
if pdb_inp is None:
raise Sorry("Need a model or models")
if not crystal_symmetry:
crystal_symmetry=pdb_inp.crystal_symmetry()
assert crystal_symmetry is not None
hierarchy = pdb_inp.construct_hierarchy()
else:
hierarchy = pdb_hierarchy # XXX FIXME
n_models=0
for model in hierarchy.models():
n_models+=1
if n_models==1: # nothing to do
return hierarchy
#xrs = pdb_inp.xray_structure_simple(crystal_symmetry=crystal_symmetry)
xrs = hierarchy.extract_xray_structure(crystal_symmetry=crystal_symmetry)
xrs.scattering_type_registry(table=scattering_table)
if not resolution:
from cctbx import maptbx
resolution=maptbx.resolution_from_map_and_model.run(
map_data=map_data, xray_structure=xrs).d_min
if(resolution is None):
raise Sorry("Resolution is required")
print("\nResolution limit: %7.2f" %(resolution), file=out)
print("\nSummary of input models", file=out)
xrs.show_summary(f=out, prefix=" ")
print("\nReady with %d models and map" %(n_models), file=out)
# Get CC by residue for each model and map
chain_id_and_resseq_list=[] # Instead set up chain_id and resseq (range)
from mmtbx.secondary_structure.find_ss_from_ca import \
split_model,get_first_resno, get_last_resno,get_chain_id
model_list=split_model(hierarchy=hierarchy,only_first_model=True)
for m in model_list:
h=m.hierarchy
first_resno=get_first_resno(h)
last_resno=get_last_resno(h)
chain_id=get_chain_id(h)
residue_range=[first_resno,last_resno]
chain_id_and_resseq=[chain_id,residue_range]
if not chain_id_and_resseq in chain_id_and_resseq_list:
chain_id_and_resseq_list.append(chain_id_and_resseq)
# Run through chains separately
# NOTE: All models of each chain must match exactly
# Save composite model, chain by chain
composite_model_stream=StringIO()
for chain_id_and_resseq in chain_id_and_resseq_list:
f=StringIO()
chain_id,[start_resno,end_resno]=chain_id_and_resseq
atom_selection=get_atom_selection(chain_id=chain_id,
start_resno=start_resno,end_resno=end_resno)
asc=hierarchy.atom_selection_cache()
sel=asc.selection(string = atom_selection)
sel_hierarchy=hierarchy.select(sel)
pdb_inp=sel_hierarchy.as_pdb_input(crystal_symmetry=crystal_symmetry)
ph=pdb_inp.construct_hierarchy()
print("\nWorking on chain_id='%s' resseq %d:%d\n" %(
chain_id_and_resseq[0],chain_id_and_resseq[1][0],chain_id_and_resseq[1][1]), file=out)
# get CC values for all residues
cc_dict=get_cc_dict(hierarchy=ph,map_data=map_data,d_min=resolution,
crystal_symmetry=crystal_symmetry,
table=scattering_table,out=out)
# smooth CC values with window of smoothing_window
smoothed_cc_dict=smooth_cc_values(cc_dict=cc_dict,
smoothing_window=smoothing_window,
verbose=verbose,out=out)
# figure out all the places where crossover can occur.
# FIXME: order of keys changes in py2/3 vthis could be bad
n_residues=cc_dict[list(cc_dict.keys())[0]].size()
crossover_dict=get_crossover_dict(
n_residues=n_residues,
hierarchy=ph,
crossover_atom=crossover_atom,
dist_max=dist_max,
minimum_matching_atoms=minimum_matching_atoms,
verbose=verbose,out=out)
# Now we are ready to identify the best composite model...
# A composite has reside 0 from model x, residue 1 from model y etc.
# Each change from model a to model b between residues i and i+1 must have
# a crossover between a and b at either residue i or i+1
keys=list(cc_dict.keys())
keys.sort()
sorted_working_model_list=[]
for key in keys:
working_model=model_object(source_id=key,
cc_dict=cc_dict,
smoothed_cc_dict=smoothed_cc_dict,
crossover_dict=crossover_dict,
minimum_length=minimum_length,
minimum_improvement=minimum_improvement,
max_regions_to_test=max_regions_to_test,
max_ends_per_region=max_ends_per_region,
maximum_fraction=maximum_fraction)
if verbose:
working_model.show_summary(out=out)
sorted_working_model_list.append(
[working_model.get_score(),working_model])
sorted_working_model_list.sort()
sorted_working_model_list.reverse()
sorted_working_model_list=\
sorted_working_model_list[:max_keep]
working_model_list=[]
for s,m in sorted_working_model_list:
working_model_list.append(m)
# Go through all the working models and cross them with other models to
# optimize...Then take all the best and cross...
best_score,best_model=sorted_working_model_list[0]
found=True
cycle=0
while found:
cycle+=1
print("\nCYCLE %d current best is %7.3f\n" %(
cycle,best_model.get_score()), file=out)
found=False
sorted_working_model_list=[]
new_best=best_model
id=0
for working_model in working_model_list:
id+=1
others=[]
for m in working_model_list:
if not working_model==m: others.append(m)
new_working_model=working_model.optimize_with_others(others=others)
if not new_working_model:
print()
continue
aa=[new_working_model.get_score(),new_working_model]
if not aa in sorted_working_model_list:
sorted_working_model_list.append(aa)
if not sorted_working_model_list:
break # nothing to do
sorted_working_model_list.sort()
sorted_working_model_list.reverse()
sorted_working_model_list=sorted_working_model_list[:max_keep]
new_working_score,new_working_model=sorted_working_model_list[0]
if new_working_score>best_model.get_score():
best_model=new_working_model
found=True
if verbose:
print("NEW BEST SCORE: %7.2f" %(best_model.get_score()), file=out)
best_model.show_summary(out=out)
print("\nDONE... best is %7.3f\n" %(
best_model.get_score()), file=out)
# Create composite of this chain
# Note residue values. We are going to pick each residue from one of
# the models
for model in ph.models():
for chain in model.chains():
if chain.id != chain_id: continue
residue_list=[]
for rg in chain.residue_groups():
residue_list.append(rg.resseq)
residue_list.sort()
assert len(best_model.source_list)==len(residue_list)
for i in range(len(residue_list)):
atom_selection=get_atom_selection(model_id=best_model.source_list[i],
resseq_sel=residue_list[i])
asc=ph.atom_selection_cache()
sel=asc.selection(string = atom_selection)
sel_hierarchy=ph.select(sel)
print(remove_ter(sel_hierarchy.as_pdb_string()), file=composite_model_stream)
# All done, make a new pdb_hierarchy
pdb_string=composite_model_stream.getvalue()
pdb_inp=iotbx.pdb.input(source_info=None, lines = pdb_string)
pdb_hierarchy=pdb_inp.construct_hierarchy()
if pdb_out:
f=open(pdb_out,'w')
print(pdb_hierarchy.as_pdb_string(crystal_symmetry=crystal_symmetry), file=f)
print("Final model is in: %s\n" %(f.name))
f.close()
return pdb_hierarchy
def process_predicted_model(model, params, pae_matrix=None, log=sys.stdout):
"""
process_predicted_model:
Purpose: Convert values in B-value field to pseudo-B-values, remove
low_confidence residues, optionally split into compact regions.
Rationale: predicted models may have regions of low and high confidence.
This routine uses values in the B-value field to identify confidence,
removes low-confidence regions, and then examines the remaining model to
find regions that are compact (residues have high contact with neighbors)
and that are separate from other regions (low contact with neigbors).
Inputs (supplied as model and a params object):
model: iotbx.model.model object containing model information.
Normally contains a single chain. If multiple chains, process
each separately.
b_value_field_is: 'lddt' or 'rmsd' or 'b_value'. For AlphaFold models
the b-value field is a value of LDDT (confidence)
on scale of 0-1 or 0-100
For RoseTTAFold, the B-value field is rmsd (A)
If b_value... it is left as is.
input_lddt_is_fractional: if True, input lddt is scale of 0 to 1,
otherwise 0 - 100
If None, set to True if all lddt are from 0 to 1
remove_low_confidence_residues: remove residues with low confidence
(lddt or rmsd as set below)
minimum_lddt: minimum lddt to keep residues (on same scale as b_value_field,
if not set, calculated from maximum_rmsd).
maximum_rmsd: alternative specification of minimum confidence based on rmsd.
If not set, calculated from minimum_lddt.
default_maximum_rmsd: used as default if nothing specified for
maximum_rmsd or minimum_lddt .Default is 1.5 A,
split_model_by_compact_regions: split resulting model into compact regions
pae_matrix: matrix of predicted aligned errors (e.g., from AlphaFold2), NxN
matrix of RMSD values, N = number of residues in model.
Alternative to splitting by compact regions. Split to minimize predicted
aligned errors in each grouping.
pae_power (default=1): each edge in the graph will be weighted
proportional to (1/pae**pae_power)
pae_cutoff (optional, default=5): graph edges will only be created for
residue pairs with pae<pae_cutoff
domain_size: typical size of domains (resolution used for filtering is
the domain size)
minimum_domain_length: minimum length (residues) of a domain to keep
maximum_domains: if more than this many domains, merge close ones to reduce
number
chain_id: if model contains more than one chain, split this chain only.
NOTE: only one chain can be processed at a time.
if subtract_minimum_b is set, subtract minimum(B values) from all B values
after applying any B value cutoffs
Output:
processed_model_info: group_args object containing:
processed_model: single model with regions identified in chainid field
How to get the parameters object set up:
You can set up a parameters object like this (see example at end of this
file as well:
master_phil = iotbx.phil.parse(master_phil_str)
params = master_phil.extract()
The default values are set in the master_phil_str string above.
You can then set values of params:
params.process_predicted_model.split_model_by_compact_regions = True
"""
# Make sure we have what we expect:
import mmtbx.model
assert isinstance(model, mmtbx.model.manager)
# Decide what to do
p = params.process_predicted_model
# Determine if input lddt is fractional and get b values
b_value_field = model.get_hierarchy().atoms().extract_b()
if p.b_value_field_is == 'lddt':
if p.input_lddt_is_fractional is None:
sel = (b_value_field < 0) | (b_value_field > 1)
p.input_lddt_is_fractional = (sel.count(True) == 0)
b_values = get_b_values_from_lddt(
b_value_field, input_lddt_is_fractional=p.input_lddt_is_fractional)
if p.input_lddt_is_fractional:
print("B-value field interpreted as LDDT %s" % ("(0 - 1)"),
file=log)
else:
print("B-value field interpreted as LDDT %s" % ("(0 - 100)"),
file=log)
elif p.b_value_field_is == 'rmsd':
b_values = get_b_values_rmsd(b_value_field)
print("B-value field interpreted as rmsd %s" % ("(0 - 1)"), file=log)
elif p.b_value_field_is == 'b_value':
b_values = b_value_field
print("B-value field interpreted as b_values", file=log)
else:
raise AssertionError(
"Please set b_value_field_is to either lddt or rmsd")
if (not p.input_lddt_is_fractional):
if p.minimum_lddt is not None: # convert to fractional
p.minimum_lddt = p.minimum_lddt * 0.01
print("Minimum LDDT converted to %.2f" % (p.minimum_lddt),
file=log)
# From here on we work only with fractional lddt
# Get confidence cutoff if needed
if p.remove_low_confidence_residues:
maximum_b_value = get_cutoff_b_value(
p.maximum_rmsd,
p.minimum_lddt,
default_maximum_rmsd=p.default_maximum_rmsd,
log=log)
else:
maximum_b_value = None
# Offset b-values and cutoff if requested
if p.subtract_minimum_b:
minimum_b = b_values.min_max_mean().min
b_values -= minimum_b
assert b_values.min_max_mean().min == 0
if maximum_b_value is not None:
maximum_b_value -= minimum_b # offset this too
print("Subtracting minimum B of " +
"%.2f from values and from cutoff (now %s)" %
(minimum_b, " %.2f" %
maximum_b_value if maximum_b_value is not None else "None"),
file=log)
# Make a new model with new B-values
ph = model.get_hierarchy().deep_copy()
ph.atoms().set_b(b_values)
# Remove low_confidence regions if desired
if p.remove_low_confidence_residues:
n_before = ph.overall_counts().n_residues
selection_string = " (bfactor < %s)" % maximum_b_value
asc1 = ph.atom_selection_cache()
sel = asc1.selection(selection_string)
new_ph = ph.select(sel)
n_after = new_ph.overall_counts().n_residues
print("Total of %s of %s residues kept after B-factor filtering" %
(n_after, n_before),
file=log)
if n_after == 0:
raise Sorry(
"No residues remaining after filtering...please check if " +
"B-value field is really '%s'" % (p.b_value_field_is))
removed_ph = ph.select(~sel)
from mmtbx.secondary_structure.find_ss_from_ca import model_info, \
split_model
from iotbx.bioinformatics import get_sequence_from_hierarchy
remainder_sequence_str = ""
for m in split_model(model_info(removed_ph)):
seq = get_sequence_from_hierarchy(m.hierarchy)
if len(seq) >= p.minimum_remainder_sequence_length:
remainder_sequence_str += "\n> fragment sequence "
remainder_sequence_str += "\n%s\n" % (
get_sequence_from_hierarchy(m.hierarchy))
else:
remainder_sequence_str = None
# Get a new model
new_model = model.as_map_model_manager().model_from_hierarchy(
ph, return_as_model=True)
# Get high-confidence regions as domains if desired:
if p.split_model_by_compact_regions:
# Make sure we have just 1 chain or a chain ID supplied
chain_id = get_chain_id(model, None, log=log)
if pae_matrix is not None: # use pae matrix method
info = split_model_with_pae(
model,
new_model,
pae_matrix,
maximum_domains=p.maximum_domains,
pae_power=p.pae_power,
pae_cutoff=p.pae_cutoff,
pae_graph_resolution=p.pae_graph_resolution,
minimum_domain_length=p.minimum_domain_length,
log=log)
else: # usual
info = split_model_into_compact_units(
new_model,
d_min=p.domain_size,
maximum_domains=p.maximum_domains,
minimum_domain_length=p.minimum_domain_length,
log=log)
if info is None:
print("No compact regions identified", file=log)
chainid_list = []
model_list = []
else:
new_model = info.model
chainid_list = info.chainid_list
print("Total of %s regions identified" % (len(chainid_list)),
file=log)
model_list = split_model_by_chainid(new_model, chainid_list)
else:
model_list = []
chainid_list = []
return group_args(
group_args_type='processed predicted model',
model=new_model,
model_list=model_list,
chainid_list=chainid_list,
remainder_sequence_str=remainder_sequence_str,
)
def run(
params=None, # params for running from command line
map_data=None, # map_data, as_double()
pdb_inp=None,
pdb_hierarchy=None,
crystal_symmetry=None,
resolution=None,
scattering_table='n_gaussian',
smoothing_window=5,
crossover_atom='CA',
minimum_matching_atoms=3,
minimum_length=2,
dist_max=1.0,
minimum_improvement=0.01,
max_regions_to_test=10,
max_ends_per_region=5,
maximum_fraction=0.5,
max_keep=10,
map_coeffs_file=None,map_coeffs_labels=None,
pdb_in_file=None,
pdb_out=None,
verbose=None,
out=sys.stdout):
if out is None: out=sys.stdout # explode and refine calls it this way
# get info from params if present
if params:
verbose=params.control.verbose
map_coeffs_file=params.input_files.map_coeffs_file
map_coeffs_labels=params.input_files.map_coeffs_labels
pdb_in_file=params.input_files.pdb_in_file
resolution=params.crystal_info.resolution
scattering_table=params.crystal_info.scattering_table
smoothing_window=params.crossover.smoothing_window
crossover_atom=params.crossover.crossover_atom
minimum_matching_atoms=params.crossover.minimum_matching_atoms
minimum_length=params.crossover.minimum_length
dist_max=params.crossover.dist_max
minimum_improvement=params.crossover.minimum_improvement
max_regions_to_test=params.crossover.max_regions_to_test
max_ends_per_region=params.crossover.max_ends_per_region
maximum_fraction=params.crossover.maximum_fraction
max_keep=params.crossover.max_keep
pdb_out=params.output_files.pdb_out
# Consistency checks
if(pdb_hierarchy is not None):
assert pdb_in_file is None
assert pdb_inp is None
assert crystal_symmetry is not None
# XXX more checks here!
# Get map_data if not present
if not map_data:
if not map_coeffs_file or not os.path.isfile(map_coeffs_file):
raise Sorry("Cannot find the map_coeffs_file '%s'" %(
str(map_coeffs_file)))
from mmtbx.building.minimize_chain import get_map_coeffs
map_coeffs=get_map_coeffs(map_coeffs_file,
map_coeffs_labels=map_coeffs_labels)
fft_map = map_coeffs.fft_map(resolution_factor = 0.25)
fft_map.apply_sigma_scaling()
map_data = fft_map.real_map_unpadded()
map_data=map_data.as_double()
if map_coeffs and not crystal_symmetry:
crystal_symmetry=map_coeffs.crystal_symmetry()
if map_coeffs and not resolution:
resolution=map_coeffs.d_min()
# Get the starting model
if(pdb_hierarchy is None):
if pdb_inp is None:
if not pdb_in_file or not os.path.isfile(pdb_in_file):
raise Sorry("Cannot read input PDB file '%s'" %(
str(pdb_in_file)))
else:
print >>out,"Taking models from %s" %(pdb_in_file)
pdb_string=open(pdb_in_file).read()
pdb_inp=iotbx.pdb.input(source_info=None, lines = pdb_string)
if pdb_inp is None:
raise Sorry("Need a model or models")
if not crystal_symmetry:
crystal_symmetry=pdb_inp.crystal_symmetry()
assert crystal_symmetry is not None
hierarchy = pdb_inp.construct_hierarchy()
else:
hierarchy = pdb_hierarchy # XXX FIXME
n_models=0
for model in hierarchy.models():
n_models+=1
if n_models==1: # nothing to do
return hierarchy
#xrs = pdb_inp.xray_structure_simple(crystal_symmetry=crystal_symmetry)
xrs = hierarchy.extract_xray_structure(crystal_symmetry=crystal_symmetry)
xrs.scattering_type_registry(table=scattering_table)
if not resolution:
from cctbx import maptbx
resolution=maptbx.resolution_from_map_and_model(
map_data=map_data, xray_structure=xrs)
print >>out,"\nResolution limit: %7.2f" %(resolution)
print >>out,"\nSummary of input models"
xrs.show_summary(f=out, prefix=" ")
print >>out, "\nReady with %d models and map" %(n_models)
# Get CC by residue for each model and map
chain_id_and_resseq_list=[] # Instead set up chain_id and resseq (range)
from mmtbx.secondary_structure.find_ss_from_ca import \
split_model,get_first_resno, get_last_resno,get_chain_id
model_list=split_model(hierarchy=hierarchy,only_first_model=True)
for m in model_list:
h=m.hierarchy
first_resno=get_first_resno(h)
last_resno=get_last_resno(h)
chain_id=get_chain_id(h)
residue_range=[first_resno,last_resno]
chain_id_and_resseq=[chain_id,residue_range]
if not chain_id_and_resseq in chain_id_and_resseq_list:
chain_id_and_resseq_list.append(chain_id_and_resseq)
# Run through chains separately
# NOTE: All models of each chain must match exactly
# Save composite model, chain by chain
from cStringIO import StringIO
composite_model_stream=StringIO()
for chain_id_and_resseq in chain_id_and_resseq_list:
from cStringIO import StringIO
f=StringIO()
chain_id,[start_resno,end_resno]=chain_id_and_resseq
atom_selection=get_atom_selection(chain_id=chain_id,
start_resno=start_resno,end_resno=end_resno)
asc=hierarchy.atom_selection_cache()
sel=asc.selection(string = atom_selection)
sel_hierarchy=hierarchy.select(sel)
pdb_inp=sel_hierarchy.as_pdb_input(crystal_symmetry=crystal_symmetry)
ph=pdb_inp.construct_hierarchy()
print >>out,"\nWorking on chain_id='%s' resseq %d:%d\n" %(
chain_id_and_resseq[0],chain_id_and_resseq[1][0],chain_id_and_resseq[1][1])
# get CC values for all residues
cc_dict=get_cc_dict(hierarchy=ph,map_data=map_data,d_min=resolution,
crystal_symmetry=crystal_symmetry,
table=scattering_table,out=out)
# smooth CC values with window of smoothing_window
smoothed_cc_dict=smooth_cc_values(cc_dict=cc_dict,
smoothing_window=smoothing_window,
verbose=verbose,out=out)
# figure out all the places where crossover can occur.
n_residues=cc_dict[cc_dict.keys()[0]].size()
crossover_dict=get_crossover_dict(
n_residues=n_residues,
hierarchy=ph,
crossover_atom=crossover_atom,
dist_max=dist_max,
minimum_matching_atoms=minimum_matching_atoms,
verbose=verbose,out=out)
# Now we are ready to identify the best composite model...
# A composite has reside 0 from model x, residue 1 from model y etc.
# Each change from model a to model b between residues i and i+1 must have
# a crossover between a and b at either residue i or i+1
keys=cc_dict.keys()
keys.sort()
sorted_working_model_list=[]
for key in keys:
working_model=model_object(source_id=key,
cc_dict=cc_dict,
smoothed_cc_dict=smoothed_cc_dict,
crossover_dict=crossover_dict,
minimum_length=minimum_length,
minimum_improvement=minimum_improvement,
max_regions_to_test=max_regions_to_test,
max_ends_per_region=max_ends_per_region,
maximum_fraction=maximum_fraction)
if verbose:
working_model.show_summary(out=out)
sorted_working_model_list.append(
[working_model.get_score(),working_model])
sorted_working_model_list.sort()
sorted_working_model_list.reverse()
sorted_working_model_list=\
sorted_working_model_list[:max_keep]
working_model_list=[]
for s,m in sorted_working_model_list:
working_model_list.append(m)
# Go through all the working models and cross them with other models to
# optimize...Then take all the best and cross...
best_score,best_model=sorted_working_model_list[0]
found=True
cycle=0
while found:
cycle+=1
print >>out, "\nCYCLE %d current best is %7.3f\n" %(
cycle,best_model.get_score())
found=False
sorted_working_model_list=[]
new_best=best_model
id=0
for working_model in working_model_list:
id+=1
others=[]
for m in working_model_list:
if not working_model==m: others.append(m)
new_working_model=working_model.optimize_with_others(others=others)
if not new_working_model:
print
continue
aa=[new_working_model.get_score(),new_working_model]
if not aa in sorted_working_model_list:
sorted_working_model_list.append(aa)
if not sorted_working_model_list:
break # nothing to do
sorted_working_model_list.sort()
sorted_working_model_list.reverse()
sorted_working_model_list=sorted_working_model_list[:max_keep]
new_working_score,new_working_model=sorted_working_model_list[0]
if new_working_score>best_model.get_score():
best_model=new_working_model
found=True
if verbose:
print >>out,"NEW BEST SCORE: %7.2f" %(best_model.get_score())
best_model.show_summary(out=out)
print >>out, "\nDONE... best is %7.3f\n" %(
best_model.get_score())
# Create composite of this chain
# Note residue values. We are going to pick each residue from one of
# the models
for model in ph.models():
for chain in model.chains():
if chain.id != chain_id: continue
residue_list=[]
for rg in chain.residue_groups():
residue_list.append(rg.resseq)
residue_list.sort()
assert len(best_model.source_list)==len(residue_list)
for i in xrange(len(residue_list)):
atom_selection=get_atom_selection(model_id=best_model.source_list[i],
resseq_sel=residue_list[i])
asc=ph.atom_selection_cache()
sel=asc.selection(string = atom_selection)
sel_hierarchy=ph.select(sel)
print >>composite_model_stream,remove_ter(sel_hierarchy.as_pdb_string())
# All done, make a new pdb_hierarchy
pdb_string=composite_model_stream.getvalue()
pdb_inp=iotbx.pdb.input(source_info=None, lines = pdb_string)
pdb_hierarchy=pdb_inp.construct_hierarchy()
if pdb_out:
f=open(pdb_out,'w')
print >>f,pdb_hierarchy.as_pdb_string(crystal_symmetry=crystal_symmetry)
print "Final model is in: %s\n" %(f.name)
f.close()
return pdb_hierarchy