def calc_rmsd_matrix_intra(self, align=False, sel='all'):
ag = self.ag.copy()
rmsd = []
for i in range(ag.numCoordsets()):
ag.setACSIndex(i)
if align:
prody.alignCoordsets(ag.select(sel))
rmsd.append([prody.calcRMSD(ag.select(sel))])
rmsd = np.concatenate(rmsd)
return rmsd
def prody_align(opt):
"""Align models in a PDB file or a PDB file onto others."""
import prody
LOGGER = prody.LOGGER
args = opt.pdb
if len(args) == 1:
pdb = args[0]
LOGGER.info('Aligning multiple models in: ' + pdb)
selstr, prefix, model = opt.select, opt.prefix, opt.model
pdb = prody.parsePDB(pdb)
pdbselect = pdb.select(selstr)
if pdbselect is None:
opt.subparser.error('Selection {0:s} do not match any atoms.'
.format(repr(selstr)))
LOGGER.info('{0:d} atoms will be used for alignment.'
.format(len(pdbselect)))
pdbselect.setACSIndex(model-1)
prody.printRMSD(pdbselect, msg='Before alignment ')
prody.alignCoordsets(pdbselect)
prody.printRMSD(pdbselect, msg='After alignment ')
if prefix == '':
prefix = pdb.getTitle() + '_aligned'
outfn = prefix + '.pdb'
LOGGER.info('Writing file: ' + outfn)
prody.writePDB(outfn, pdb)
else:
reffn = args.pop(0)
seqid=opt.seqid
overlap=opt.overlap
LOGGER.info('Aligning structures onto: ' + reffn)
ref = prody.parsePDB(reffn)
for arg in args:
if arg == reffn:
continue
if '_aligned.pdb' in arg:
continue
pdb = prody.parsePDB(arg)
result = prody.matchAlign(pdb, ref, seqid=seqid, overlap=overlap,
tarsel=opt.select, allcsets=True,
cslabel='Model', csincr=1)
if result:
outfn = pdb.getTitle() + '_aligned.pdb'
LOGGER.info('Writing file: ' + outfn)
prody.writePDB(outfn, pdb)
else:
LOGGER.warning('Failed to align ' + arg)
def prody_align(opt):
"""Align models in a PDB file or a PDB file onto others."""
import prody
LOGGER = prody.LOGGER
args = opt.pdb
if len(args) == 1:
pdb = args[0]
LOGGER.info('Aligning multiple models in: ' + pdb)
selstr, prefix, model = opt.select, opt.prefix, opt.model
pdb = prody.parsePDB(pdb)
pdbselect = pdb.select(selstr)
if pdbselect is None:
LOGGER.warning('Selection "{0:s}" do not match any atoms.'
.format(selstr))
sys.exit(-1)
LOGGER.info('{0:d} atoms will be used for alignment.'
.format(len(pdbselect)))
pdb.setACSIndex(model-1)
prody.alignCoordsets(pdb, selstr=selstr)
rmsd = prody.calcRMSD(pdb)
LOGGER.info('Max RMSD: {0:0.2f} Mean RMSD: {1:0.2f}'
.format(rmsd.max(), rmsd.mean()))
if prefix == '':
prefix = pdb.getTitle() + '_aligned'
outfn = prefix + '.pdb'
LOGGER.info('Writing file: ' + outfn)
prody.writePDB(outfn, pdb)
else:
reffn = args.pop(0)
LOGGER.info('Aligning structures onto: ' + reffn)
ref = prody.parsePDB(reffn)
for arg in args:
if arg == reffn:
continue
if '_aligned.pdb' in arg:
continue
pdb = prody.parsePDB(arg)
if prody.matchAlign(pdb, ref):
outfn = pdb.getTitle() + '_aligned.pdb'
LOGGER.info('Writing file: ' + outfn)
prody.writePDB(outfn, pdb)
else:
LOGGER.warning('Failed to align ' + arg)
def calc_rmsd_with(self, mol, align=False, sel='all'):
ag1 = self.ag.copy()
ag2 = mol.ag.copy()
sel1 = ag1.select(sel).copy()
sel2 = ag2.select(sel).copy()
if sel1 is None or sel2 is None:
raise RuntimeError('Selection is empty')
if sel1.numAtoms() != sel2.numAtoms():
raise RuntimeError('Selections are different')
merged = np.concatenate([sel1.getCoordsets(), sel2.getCoordsets()])
n1, n2 = sel1.numCoordsets(), sel2.numCoordsets()
sel1.setCoords(merged)
rmsd = []
for i in range(n1):
sel1.setACSIndex(i)
if align:
prody.alignCoordsets(sel1)
rmsd.append([prody.calcRMSD(sel1)[n1:]])
rmsd = np.concatenate(rmsd)
return rmsd
def calc_rmsd_to_frame(self, frame, align=False, sel='all'):
ag = self.ag.copy()
ag.setACSIndex(frame)
if align:
prody.alignCoordsets(ag.select(sel))
return prody.calcRMSD(ag.select(sel))
lines = fileLoader.load_dat(file)
atom_indices.append((lines,indices_name))
## Distance range parameters for RMSD clustering:
start_dist, end_dist, interval = args.distance_range[0:]
## Loads precalculated fingerprint matrix.
fingerprints_matrix = fileLoader.load_nc(args.fingerprint_matrix)
from netCDF4 import Dataset
## user-defined distance cutoffs.
from prody import alignCoordsets
for atom_inds in atom_indices:
selection = pdb_file[atom_inds[0]]
align_coords = alignCoordsets(selection)
aligned_coords = align_coords.getCoordsets()
distance_matrix = calcDistMatrix(aligned_coords)
# dmat_file = Dataset('%s-distanceMatrix.nc'%atom_inds[1],mode = 'w',format='NETCDF4')
# dmat_dim = dmat_file.createDimension( 'distance_matrix')
# dmat_var = dmat_file.createVariable('distance_matrix','f',('distance_matrix',))
# dmat_var[:] = distance_matrix
# continue
# break
if args.split_array > 1:
if not os.path.exists('split_assignments'): os.mkdir('split_assignments')
split_matrices = arraySplitter(distance_matrix,args.split_array)
for i in range(len(split_matrices)):
cluster = clusterData(split_matrices[i])
dist_range = distRange(start_dist,end_dist,interval)
def prody_align(*pdbs, **kwargs):
"""Align models in a PDB file or multiple structures in separate PDB files.
By default, protein chains will be matched based on selected atoms and
alignment will be performed based on matching residues. If non-protein
atoms are selected and selected atoms match in multiple structures,
they will be used for alignment.
:arg pdbs: PDB identifier(s) or filename(s)
:arg select: atom selection string, default is :term:`calpha`,
see :ref:`selections`
:arg model: for NMR files, reference model index, default is ``1``
:arg seqid: percent sequence identity, default is ``90``
:arg overlap: percent sequence overlap, default is ``90``
:arg prefix: prefix for output file, default is PDB filename
:arg suffix: output filename suffix, default is :file:`_aligned`"""
from numpy import all
from prody import LOGGER, writePDB, parsePDB
from prody import alignCoordsets, printRMSD, matchAlign, superpose
selstr = kwargs.get('select', 'calpha')
suffix = kwargs.get('suffix', '_aligned')
if len(pdbs) == 1:
pdb = pdbs[0]
LOGGER.info('Aligning multiple models in: ' + pdb)
prefix = kwargs.get('prefix')
model = kwargs.get('model')
pdb = parsePDB(pdb)
pdbselect = pdb.select(selstr)
if pdbselect is None:
subparser = kwargs.get('subparser')
if subparser:
subparser.error('Selection {0} do not match any atoms.'.format(
repr(selstr)))
else:
raise ValueError('select does not match any atoms')
LOGGER.info('{0} atoms will be used for alignment.'.format(
len(pdbselect)))
pdbselect.setACSIndex(model - 1)
printRMSD(pdbselect, msg='Before alignment ')
alignCoordsets(pdbselect)
printRMSD(pdbselect, msg='After alignment ')
outfn = (prefix or pdb.getTitle()) + suffix + '.pdb'
LOGGER.info('Writing file: ' + outfn)
writePDB(outfn, pdb)
else:
pdbs = list(pdbs)
reffn = pdbs.pop(0)
seqid = kwargs.get('seqid')
overlap = kwargs.get('overlap')
LOGGER.info('Aligning structures onto: ' + reffn)
ref = parsePDB(reffn)
ref_sel = ref.select(selstr)
if ref_sel:
LOGGER.info('Selection {0} matched {1} atoms.'.format(
repr(selstr), len(ref_sel)))
else:
raise ValueError('selection {0} did not match any atoms'.format(
repr(selstr)))
match = True
if ref_sel.numAtoms('ca') < 2:
match = False
for arg in pdbs:
if arg == reffn:
continue
#if '_aligned.pdb' in arg:
# continue
LOGGER.info('Evaluating structure: ' + arg)
pdb = parsePDB(arg)
if match:
result = matchAlign(pdb,
ref,
seqid=seqid,
overlap=overlap,
tarsel=selstr,
allcsets=True,
cslabel='Model',
csincr=1)
if result:
outfn = pdb.getTitle() + suffix + '.pdb'
LOGGER.info('Writing file: ' + outfn)
writePDB(outfn, pdb)
continue
pdb_sel = pdb.select(selstr)
LOGGER.info('Selection {0} matched {1} atoms.'.format(
repr(selstr), len(pdb_sel)))
if (len(pdb_sel) == len(ref_sel)
and all(pdb_sel.getNames() == ref_sel.getNames())):
printRMSD(ref_sel, pdb_sel, msg='Before alignment ')
superpose(pdb_sel, ref_sel)
printRMSD(ref_sel, pdb_sel, msg='After alignment ')
outfn = pdb.getTitle() + suffix + '.pdb'
LOGGER.info('Writing file: ' + outfn)
writePDB(outfn, pdb)
else:
LOGGER.warn('Failed to align structure ' + arg + '.')
def s2_values(
model_data, calculate_on_models, s2_records, s2_type, fit, fit_range
):
"""Returns a dictionary with the average S2 values:
s2_calced[residue] = value"""
if fit:
reference = model_data.atomgroup[:]
model_data.atomgroup.setACSIndex(0)
prody.alignCoordsets(model_data.atomgroup.calpha)
if fit_range:
for model_num in calculate_on_models:
model_data.atomgroup.setACSIndex(model_num)
mobile = model_data.atomgroup[:]
matches = prody.matchChains(reference, mobile)
match = matches[0]
ref_chain = match[0]
mob_chain = match[1]
weights = np.zeros((len(ref_chain), 1), dtype=np.int)
fit_start, fit_end = fit_range.split("-")
for i in range(int(fit_start) - 1, int(fit_end) - 1):
weights[i] = 1
t = prody.calcTransformation(mob_chain, ref_chain, weights)
t.apply(mobile)
# get NH vectors from models (model_data[] -> vectors{resnum : vector})
vector_data = []
s2_pairs = {"N": "H", "CA": "HA"}
h_coords = None
n_coords = None
for model_num in calculate_on_models:
model_data.atomgroup.setACSIndex(model_num)
current_resindex = 1
has_first, has_second = False, False
vectors = {}
for atom in model_data.atomgroup:
atom_res = atom.getResnum()
if atom_res != current_resindex:
current_resindex = atom_res
has_first, has_second = False, False
if atom_res == current_resindex:
if atom.getName() == s2_type:
has_second = True
n_coords = Vec3D(atom.getCoords())
elif atom.getName() == s2_pairs[s2_type]:
has_first = True
h_coords = Vec3D(atom.getCoords())
if has_first and has_second:
has_first, has_second = False, False
vectors[atom_res] = Vec3D(
n_coords - h_coords
).normalize()
vector_data.append(vectors)
s2_calced = {}
# iterating over STR records
for resnum in [int(s2rec.resnum) for s2rec in s2_records]:
x2, y2, z2, xy, xz, yz = 0, 0, 0, 0, 0, 0
# iterating over PDB models
for m in vector_data:
# coordinates in model at a given resnum
x, y, z = m[resnum].v[0], m[resnum].v[1], m[resnum].v[2]
x2 += x ** 2
y2 += y ** 2
z2 += z ** 2
xy += x * y
xz += x * z
yz += y * z
x2 /= len(vector_data)
y2 /= len(vector_data)
z2 /= len(vector_data)
xy /= len(vector_data)
xz /= len(vector_data)
yz /= len(vector_data)
s2 = (
3
/ 2.0
* (
x2 ** 2
+ y2 ** 2
+ z2 ** 2
+ 2 * xy ** 2
+ 2 * xz ** 2
+ 2 * yz ** 2
)
- 0.5
)
s2_calced[resnum] = s2
return s2_calced
def s2_sidechain(csv_buffer, s2_sidechain, my_path, model_data, fit=None):
"""Back-calculate order parameters from given S2 dict and PDB models"""
sc_lot = {
"VAL": {"CG1": "CB", "CG2": "CB"},
"ILE": {"CG2": "CB", "CD": "CG1", "CD1": "CG1"},
"THR": {"CG2": "CB"},
"LEU": {"CD1": "CG", "CD2": "CG"},
"ALA": {"CB": "CA"},
"MET": {"CE": "SD"},
}
if fit:
model_data.atomgroup.setACSIndex(0)
prody.alignCoordsets(model_data.atomgroup.calpha)
for record in s2_sidechain:
vectors = []
resnum = record.resnum
my_type = record.type
my_res = model_data.atomgroup[("A", resnum)].getResname()
# find pair for measured aa
pair = sc_lot[my_res][my_type]
for model_num in range(model_data.model_count):
model_data.atomgroup.setACSIndex(model_num)
try:
sel = "resnum {} name {}".format(resnum, my_type)
coords = model_data.atomgroup.select(sel).getCoords()[0]
sel = "resnum {} name {}".format(resnum, pair)
pair_coords = model_data.atomgroup.select(sel).getCoords()[0]
except AttributeError:
return {
"error": "Sidechain order parameter atom name not found\
in PDB. Please check your atom naming."
}
vectors.append(Vec3D(coords - pair_coords).normalize())
x2, y2, z2, xy, xz, yz = 0, 0, 0, 0, 0, 0
for vector in vectors:
x, y, z = vector.v[0], vector.v[1], vector.v[2]
x2 += x ** 2
y2 += y ** 2
z2 += z ** 2
xy += x * y
xz += x * z
yz += y * z
x2 /= len(vectors)
y2 /= len(vectors)
z2 /= len(vectors)
xy /= len(vectors)
xz /= len(vectors)
yz /= len(vectors)
s2 = 3 / 2.0 * (x2 ** 2 + y2 ** 2 + z2 ** 2 +
2 * xy ** 2 + 2 * xz ** 2 + 2 * yz ** 2) - 0.5
record.calced = s2
sidechain_exp1, sidechain_exp2 = [], []
sidechain_calc1, sidechain_calc2 = {}, {}
prev_resnum = -100000
for record in s2_sidechain:
if record.resnum != prev_resnum:
sidechain_exp1.append(record)
sidechain_calc1[record.resnum] = record.calced
prev_resnum = record.resnum
else:
sidechain_exp2.append(record)
sidechain_calc2[record.resnum] = record.calced
csv_buffer.add_data(
{
"name": "S2_meth",
"calced": sidechain_calc1,
"experimental": sidechain_exp1,
}
)
if sidechain_exp2:
csv_buffer.add_data(
{
"name": "S2_meth (cont)",
"calced": sidechain_calc2,
"experimental": sidechain_exp2,
}
)
# correlation calculation
m = [0.0, 0.0, 0.0]
d = [0.0, 0.0]
for record in s2_sidechain:
exp = record.value
calc = record.calced
m[0] += calc
m[1] += exp
m[2] += calc * exp
m[0] /= len(s2_sidechain)
m[1] /= len(s2_sidechain)
m[2] /= len(s2_sidechain)
for record in s2_sidechain:
exp = record.value
calc = record.calced
d[0] += (calc - m[0]) ** 2
d[1] += (exp - m[1]) ** 2
d[0] /= len(s2_sidechain)
d[0] = math.sqrt(d[0])
d[1] /= len(s2_sidechain)
d[1] = math.sqrt(d[1])
correl = (m[2] - (m[0] * m[1])) / (d[0] * d[1])
print("Corr: ", correl)
# Q-value calculation
d2, e2 = 0, 0
for record in s2_sidechain:
exp = record.value
calc = record.calced
d2 += (calc - exp) ** 2
e2 += exp ** 2
Q = 100 * math.sqrt(d2) / math.sqrt(e2)
q_value = round(Q, 6)
print("Q-value: ", q_value)
# RMSD calculation
d2 = 0
for record in s2_sidechain:
exp = record.value
calc = record.calced
d2 += (calc - exp) ** 2
rmsd = math.sqrt(d2 / len(s2_sidechain))
print("RMSD: ", round(rmsd, 6))
exp_values = []
calced_values = []
for record in s2_sidechain:
exp_values.append(record.value)
calced_values.append(record.calced)
min_calc = min(calced_values)
max_calc = max(calced_values)
min_exp = min(exp_values)
max_exp = max(exp_values)
miny = min(min_calc, min_exp) # get minimum value
maxy = max(max_calc, max_exp) # get maximum value
diag = []
margin = int(abs(miny - maxy) * 0.05)
if abs(miny - maxy) < 10:
margin = 0.3
elif abs(miny - maxy) < 2:
margin = 0.01
elif abs(miny - maxy) < 1:
margin = 0
maxy += margin
miny -= margin
for i in np.arange(miny, maxy * 1.42, 0.1): # draw graph diagonal
diag.append(i)
plt.figure(figsize=(6, 5), dpi=80)
plt.plot(diag, diag, linewidth=2.0, color="red", alpha=0.7)
plt.plot(exp_values, calced_values, "bo")
plt.axis([miny, maxy, miny, maxy])
plt.xlabel("experimental")
plt.ylabel("calculated")
plt.tight_layout(pad=1.08)
plt.savefig(my_path + "/" + "S2_sc_corr.svg", format="svg")
plt.close()
xs = []
prev_resnum2 = -1
for record in s2_sidechain:
if record.resnum != prev_resnum2:
xs.append(record.resnum)
prev_resnum2 = record.resnum
else:
xs.append(record.resnum + 0.3)
print("XS AXIS", xs)
print("len SC", len(s2_sidechain))
print("len xs", len(xs))
plt.figure(figsize=(10, 5), dpi=80)
plt.plot(
xs,
exp_values,
linewidth=2.0,
color="red",
marker="o",
label="exp",
alpha=0.7,
)
plt.plot(
xs,
calced_values,
linewidth=2.0,
color="blue",
marker="o",
label="calc",
alpha=0.7,
)
plt.legend(loc="lower left")
plt.xlabel("residue number")
plt.ylabel("value")
ax = plt.axes()
ax.yaxis.grid()
plt.tight_layout(pad=1.08)
plt.savefig(my_path + "/" + "S2_sc_graph.svg", format="svg")
plt.close()
my_id = my_path.split("/")[-2] + "/"
print("CORR GRAPH", my_id + "S2_sc_corr.svg")
print("GRAPH", my_id + "S2_sc_graph.svg")
s2_sc_data = {
"S2_model_n": len(s2_sidechain),
"correlation": "{0:.3f}".format(correl),
"q_value": "{0:.3f}".format(q_value),
"rmsd": "{0:.3f}".format(rmsd),
"corr_graph_name": my_id + "S2_sc_corr.svg",
"graph_name": my_id + "S2_sc_graph.svg",
}
return s2_sc_data
def prody_align(*pdbs, **kwargs):
"""Align models in a PDB file or multiple structures in separate PDB files.
By default, protein chains will be matched based on selected atoms and
alignment will be performed based on matching residues. If non-protein
atoms are selected and selected atoms match in multiple structures,
they will be used for alignment.
:arg pdbs: PDB identifier(s) or filename(s)
:arg select: atom selection string, default is :term:`calpha`,
see :ref:`selections`
:arg model: for NMR files, reference model index, default is ``1``
:arg seqid: percent sequence identity, default is ``90``
:arg overlap: percent sequence overlap, default is ``90``
:arg prefix: prefix for output file, default is PDB filename
:arg suffix: output filename suffix, default is :file:`_aligned`"""
from numpy import all
from prody import LOGGER, writePDB, parsePDB
from prody import alignCoordsets, printRMSD, matchAlign, superpose
selstr = kwargs.get('select', 'calpha')
suffix = kwargs.get('suffix', '_aligned')
if len(pdbs) == 1:
pdb = pdbs[0]
LOGGER.info('Aligning multiple models in: ' + pdb)
prefix = kwargs.get('prefix')
model = kwargs.get('model')
pdb = parsePDB(pdb)
pdbselect = pdb.select(selstr)
if pdbselect is None:
subparser = kwargs.get('subparser')
if subparser:
subparser.error('Selection {0} do not match any atoms.'
.format(repr(selstr)))
else:
raise ValueError('select does not match any atoms')
LOGGER.info('{0} atoms will be used for alignment.'
.format(len(pdbselect)))
pdbselect.setACSIndex(model-1)
printRMSD(pdbselect, msg='Before alignment ')
alignCoordsets(pdbselect)
printRMSD(pdbselect, msg='After alignment ')
outfn = (prefix or pdb.getTitle()) + suffix + '.pdb'
LOGGER.info('Writing file: ' + outfn)
writePDB(outfn, pdb)
else:
pdbs = list(pdbs)
reffn = pdbs.pop(0)
seqid = kwargs.get('seqid')
overlap = kwargs.get('overlap')
LOGGER.info('Aligning structures onto: ' + reffn)
ref = parsePDB(reffn)
ref_sel = ref.select(selstr)
if ref_sel:
LOGGER.info('Selection {0} matched {1} atoms.'
.format(repr(selstr), len(ref_sel)))
else:
raise ValueError('selection {0} did not match any atoms'
.format(repr(selstr)))
match = True
if ref_sel.numAtoms('ca') < 2:
match = False
for arg in pdbs:
if arg == reffn:
continue
#if '_aligned.pdb' in arg:
# continue
LOGGER.info('Evaluating structure: ' + arg)
pdb = parsePDB(arg)
if match:
result = matchAlign(pdb, ref, seqid=seqid, overlap=overlap,
tarsel=selstr, allcsets=True,
cslabel='Model', csincr=1)
if result:
outfn = pdb.getTitle() + suffix + '.pdb'
LOGGER.info('Writing file: ' + outfn)
writePDB(outfn, pdb)
continue
pdb_sel = pdb.select(selstr)
LOGGER.info('Selection {0} matched {1} atoms.'
.format(repr(selstr), len(pdb_sel)))
if (len(pdb_sel) == len(ref_sel) and
all(pdb_sel.getNames() == ref_sel.getNames())):
printRMSD(ref_sel, pdb_sel, msg='Before alignment ')
superpose(pdb_sel, ref_sel)
printRMSD(ref_sel, pdb_sel, msg='After alignment ')
outfn = pdb.getTitle() + suffix + '.pdb'
LOGGER.info('Writing file: ' + outfn)
writePDB(outfn, pdb)
else:
LOGGER.warn('Failed to align structure ' + arg + '.')
def run_selection(my_path, original_values, user_selection_JSON):
DumpedData.RDC_isloaded = False
DumpedData.S2_isloaded = False
DumpedData.PDB_isloaded = False
DumpedData.Jcoup_isloaded = False
DumpedData.ChemShift_isloaded = False
working_dir = my_path
user_sel = getUserSel(user_selection_JSON)
pdb_output_name = my_path + "/raw.pdb"
if os.path.isfile(pdb_output_name):
os.remove(pdb_output_name)
if os.path.isfile(my_path + "/selected.pdb"):
os.remove(my_path + "/selected.pdb")
global max_size
global min_size
global overdrive
if "min_size" not in globals():
min_size = None
if "max_size" not in globals():
max_size = None
print("max_size -> ", max_size)
if "overdrive" not in globals():
overdrive = None
in_selection, iter_data, iter_all = selection_on(
working_dir, measure, user_sel,
min_size=min_size, max_size=max_size, overdrive=overdrive
)
print("ITER ALL", iter_all)
for key, val in iter_data.items():
print("CALCED ", key, '{0:.3f}'.format(val))
DumpedData.loadPDBData(my_path)
PDB_data = DumpedData.PDB_model_data
sel_ensemble = PDB_data.atomgroup.copy()
for model_num in reversed(range(sel_ensemble.numCoordsets())):
if model_num not in in_selection:
sel_ensemble.delCoordset(model_num)
num_coordsets = sel_ensemble.numCoordsets()
print("NUM_COORDSETS: ", num_coordsets)
prody.alignCoordsets(sel_ensemble.calpha)
prody.writePDB(pdb_output_name, sel_ensemble)
in_selection = [str(x+1) for x in sorted(in_selection)]
dummy_pdb = open(pdb_output_name, 'r')
output_pdb = open(my_path + "/selected.pdb", "w")
for line in dummy_pdb:
output_pdb.write(line)
if 'REMARK' in line:
model_line = "REMARK ORIGINAL MODELS: "
for model_num in in_selection:
if len(model_line) < 76:
model_line += model_num + " "
else:
output_pdb.write(model_line + "\n")
model_line = "REMARK ORIGINAL MODELS: "
output_pdb.write(model_line + "\n")
calc_id = my_path.split('/')[-1]
print('calcID', calc_id)
db_entry = CSX_upload.objects.get(id_code=calc_id)
print(db_entry.PDB_file)
print("db_entry", db_entry)
pca_image_names = graph.pca.create_pca_comparison(
my_path, db_entry.PDB_file, in_selection
)
return num_coordsets, iter_data, pca_image_names