def mk_native_files(self, native_pdb, native_mtz):
"""Create the files required by SHELXE from the native structure
Parameters
----------
native_pdb : str
Path to the native PDB file
native_mtz : str
Path to the native MTZ file
"""
mtz_util.to_hkl(native_mtz, hkl_file=os.path.join(self.work_dir, self.stem + ".hkl"))
shutil.copyfile(native_pdb, os.path.join(self.work_dir, self.stem + ".ent"))
def mk_native_files(self, native_pdb, native_mtz):
"""Create the files required by SHELXE from the native structure
Parameters
----------
native_pdb : str
Path to the native PDB file
native_mtz : str
Path to the native MTZ file
"""
mtz_util.to_hkl(native_mtz,
hkl_file=os.path.join(self.work_dir,
self.stem + ".hkl"))
shutil.copyfile(native_pdb,
os.path.join(self.work_dir, self.stem + ".ent"))
def analyse(amoptd, newroot=None):
if newroot:
assert os.path.isdir(newroot)
global _oldroot, _newroot
_newroot = newroot
_oldroot = amoptd['work_dir']
if not os.path.isdir(fixpath(amoptd['benchmark_dir'])):
os.mkdir(fixpath(amoptd['benchmark_dir']))
os.chdir(fixpath(amoptd['benchmark_dir']))
# AnalysePdb may have already been called from the main script
if amoptd['native_pdb'] and 'native_pdb_std' not in amoptd:
analysePdb(amoptd)
if amoptd['native_pdb_std']:
# Generate an SHELXE HKL and ENT file so that we can calculate phase errors
mtz_util.to_hkl(amoptd['mtz'],
hkl_file=os.path.join(amoptd['benchmark_dir'],
SHELXE_STEM + ".hkl"))
shutil.copyfile(
amoptd['native_pdb_std'],
os.path.join(amoptd['benchmark_dir'], SHELXE_STEM + ".ent"))
if amoptd['native_pdb'] and \
not (amoptd['homologs'] or amoptd['ideal_helices']
or amoptd['import_ensembles'] or amoptd['single_model_mode']):
analyseModels(amoptd)
# Get the ensembling data
if 'ensembles_data' not in amoptd or not len(amoptd['ensembles_data']):
logger.critical("Benchmark cannot find any ensemble data!")
return
# Get dict of ensemble name -> ensemble result
ensemble_results = {e['name']: e for e in amoptd['ensembles_data']}
# Get mrbump_results for cluster
if 'mrbump_results' not in amoptd or not len(amoptd['mrbump_results']):
logger.critical("Benchmark cannot find any mrbump results!")
return
data = []
mrinfo = shelxe.MRinfo(amoptd['shelxe_exe'], amoptd['native_pdb_info'].pdb,
amoptd['mtz'])
for result in amoptd['mrbump_results']:
# use mrbump dict as basis for result object
d = copy.copy(result)
# Add in the data from the ensemble
d.update(ensemble_results[d['ensemble_name']])
assert d['ensemble_name'] == d['name'], d
# Hack for old results
if 'truncation_num_residues' in d:
d['num_residues'] = d['truncation_num_residues']
del d['truncation_num_residues']
# Hack for ideal helices where num_residues are missing
if amoptd['ideal_helices'] and ('num_residues' not in d
or d['num_residues'] is None):
d['num_residues'] = int(d['ensemble_name'].lstrip('polyala'))
# Get the ensemble data and add to the MRBUMP data
d['ensemble_percent_model'] = int(
(float(d['num_residues']) / float(amoptd['fasta_length'])) * 100)
if amoptd['native_pdb']:
# Add in stuff we've cleaned from the pdb
native_keys = [
'native_pdb_code', 'native_pdb_title', 'native_pdb_resolution',
'native_pdb_solvent_content', 'native_pdb_space_group',
'native_pdb_num_chains', 'native_pdb_num_atoms',
'native_pdb_num_residues'
]
d.update({key: amoptd[key] for key in native_keys})
# Analyse the solution
analyseSolution(amoptd, d, mrinfo)
data.append(d)
# Put everything in a pandas DataFrame
dframe = pd.DataFrame(data)
# General stuff
dframe['ample_version'] = amoptd['ample_version']
dframe['fasta_length'] = amoptd['fasta_length']
# Analyse subcluster centroid models
if 'subcluster_centroid_model' in dframe.columns and amoptd['native_pdb']:
centroid_index = dframe.index
centroid_models = [
fixpath(f) for f in dframe.subcluster_centroid_model
]
native_pdb_std = fixpath(amoptd['native_pdb_std'])
fasta = fixpath(amoptd['fasta'])
# Calculation of TMscores for subcluster centroid models
if amoptd['have_tmscore']:
tm = tm_util.TMscore(amoptd['tmscore_exe'],
wdir=fixpath(amoptd['benchmark_dir']),
**amoptd)
tm_results = tm.compare_structures(centroid_models,
[native_pdb_std], [fasta])
centroid_tmscores = [r['tmscore'] for r in tm_results]
centroid_rmsds = [r['rmsd'] for r in tm_results]
else:
# Use maxcluster
centroid_tmscores = []
for centroid_model in centroid_models:
n = os.path.splitext(os.path.basename(centroid_model))[0]
cm = None
for pdb in amoptd['models']:
if n.startswith(
os.path.splitext(os.path.basename(pdb))[0]):
cm = pdb
break
if cm:
centroid_tmscores.append(_MAXCLUSTERER.tm(cm))
else:
msg = "Cannot find model for subcluster_centroid_model {0}".format(
dframe[0, 'subcluster_centroid_model'])
raise RuntimeError(msg)
# There is an issue here as this is the RMSD over the TM-aligned residues
# NOT the global RMSD, which it is for the tm_util results
centroid_rmsds = [None for _ in centroid_index]
dframe['subcluster_centroid_model_TM'] = pd.Series(
centroid_tmscores, index=centroid_index)
dframe['subcluster_centroid_model_RMSD'] = pd.Series(
centroid_rmsds, index=centroid_index)
# Save the data
file_name = os.path.join(fixpath(amoptd['benchmark_dir']), 'results.csv')
dframe.to_csv(file_name, columns=_CSV_KEYLIST, index=False, na_rep="N/A")
amoptd['benchmark_results'] = dframe.to_dict('records')
return
def analyse(amoptd, newroot=None):
if newroot:
assert os.path.isdir(newroot)
global _oldroot,_newroot
_newroot = newroot
_oldroot = amoptd['work_dir']
if not os.path.isdir(fixpath(amoptd['benchmark_dir'])):
os.mkdir(fixpath(amoptd['benchmark_dir']))
os.chdir(fixpath(amoptd['benchmark_dir']))
# AnalysePdb may have already been called from the main script
if amoptd['native_pdb'] and 'native_pdb_std' not in amoptd:
analysePdb(amoptd)
if amoptd['native_pdb_std']:
# Generate an SHELXE HKL and ENT file so that we can calculate phase errors
mtz_util.to_hkl(amoptd['mtz'], hkl_file=os.path.join(amoptd['benchmark_dir'], SHELXE_STEM + ".hkl"))
shutil.copyfile(amoptd['native_pdb_std'], os.path.join(amoptd['benchmark_dir'], SHELXE_STEM + ".ent"))
if amoptd['native_pdb'] and \
not (amoptd['homologs'] or amoptd['ideal_helices']
or amoptd['import_ensembles'] or amoptd['single_model_mode']):
analyseModels(amoptd)
# Get the ensembling data
if 'ensembles_data' not in amoptd or not len(amoptd['ensembles_data']):
logger.critical("Benchmark cannot find any ensemble data!")
return
# Get dict of ensemble name -> ensemble result
ensemble_results = {
e['name']: e for e in amoptd['ensembles_data']
}
# Get mrbump_results for cluster
if 'mrbump_results' not in amoptd or not len(amoptd['mrbump_results']):
logger.critical("Benchmark cannot find any mrbump results!")
return
data = []
mrinfo = shelxe.MRinfo(amoptd['shelxe_exe'], amoptd['native_pdb_info'].pdb, amoptd['mtz'])
for result in amoptd['mrbump_results']:
# use mrbump dict as basis for result object
d = copy.copy(result)
# Add in the data from the ensemble
d.update(ensemble_results[d['ensemble_name']])
assert d['ensemble_name'] == d['name'], d
# Hack for old results
if 'truncation_num_residues' in d:
d['num_residues'] = d['truncation_num_residues']
del d['truncation_num_residues']
# Hack for ideal helices where num_residues are missing
if amoptd['ideal_helices'] and ('num_residues' not in d or d['num_residues'] is None):
d['num_residues'] = int(d['ensemble_name'].lstrip('polyala'))
# Get the ensemble data and add to the MRBUMP data
d['ensemble_percent_model'] = int((float(d['num_residues']) / float(amoptd['fasta_length'])) * 100)
if amoptd['native_pdb']:
# Add in stuff we've cleaned from the pdb
native_keys = [
'native_pdb_code', 'native_pdb_title', 'native_pdb_resolution', 'native_pdb_solvent_content',
'native_pdb_space_group', 'native_pdb_num_chains', 'native_pdb_num_atoms', 'native_pdb_num_residues'
]
d.update({key: amoptd[key] for key in native_keys})
# Analyse the solution
analyseSolution(amoptd, d, mrinfo)
data.append(d)
# Put everything in a pandas DataFrame
dframe = pd.DataFrame(data)
# General stuff
dframe['ample_version'] = amoptd['ample_version']
dframe['fasta_length'] = amoptd['fasta_length']
# Analyse subcluster centroid models
if 'subcluster_centroid_model' in dframe.columns and amoptd['native_pdb']:
centroid_index = dframe.index
centroid_models = [fixpath(f) for f in dframe.subcluster_centroid_model]
native_pdb_std = fixpath(amoptd['native_pdb_std'])
fasta = fixpath(amoptd['fasta'])
# Calculation of TMscores for subcluster centroid models
if amoptd['have_tmscore']:
tm = tm_util.TMscore(amoptd['tmscore_exe'], wdir=fixpath(amoptd['benchmark_dir']), **amoptd)
tm_results = tm.compare_structures(centroid_models, [native_pdb_std], [fasta])
centroid_tmscores = [r['tmscore'] for r in tm_results]
centroid_rmsds = [r['rmsd'] for r in tm_results]
else:
raise RuntimeError("No program to calculate tmscores!")
dframe['subcluster_centroid_model_TM'] = pd.Series(centroid_tmscores, index=centroid_index)
dframe['subcluster_centroid_model_RMSD'] = pd.Series(centroid_rmsds, index=centroid_index)
# Save the data
file_name = os.path.join(fixpath(amoptd['benchmark_dir']), 'results.csv')
dframe.to_csv(file_name, columns=_CSV_KEYLIST, index=False, na_rep="N/A")
amoptd['benchmark_results'] = dframe.to_dict('records')
return