def superimpose_coordinates(all_coordsets, iterpose = True):
all_superimposed_coordsets = []
for coordsets in all_coordsets:
calculator = RMSDCalculator(calculatorType = "QTRFIT_OMP_CALCULATOR",
fittingCoordsets = coordsets)
calculator.setNumberOfOpenMPThreads(4)
if iterpose:
print "\t- Using iterposition on trajectory (shape ", coordsets.shape, ")"
calculator.iterativeSuperposition()
all_superimposed_coordsets.append(coordsets)
else:
print "\t- Superimposing with first trajectory frame (shape ", coordsets.shape, ")"
_, superimposed_coordsets = calculator.oneVsTheOthers(0, get_superposed_coordinates = True)
all_superimposed_coordsets.append(superimposed_coordsets)
return all_superimposed_coordsets
def superimpose_coordinates(all_coordsets, iterpose=True):
all_superimposed_coordsets = []
for coordsets in all_coordsets:
calculator = RMSDCalculator(calculatorType="QTRFIT_OMP_CALCULATOR",
fittingCoordsets=coordsets)
calculator.setNumberOfOpenMPThreads(4)
if iterpose:
print "\t- Using iterposition on trajectory (shape ", coordsets.shape, ")"
calculator.iterativeSuperposition()
all_superimposed_coordsets.append(coordsets)
else:
print "\t- Superimposing with first trajectory frame (shape ", coordsets.shape, ")"
_, superimposed_coordsets = calculator.oneVsTheOthers(
0, get_superposed_coordinates=True)
all_superimposed_coordsets.append(superimposed_coordsets)
return all_superimposed_coordsets
def process_after_perturb_max_and_mean_disp(data):
number_of_sets = len(data["coords_before"])
num_coords = len(data["coords_before"][0])
coordsets_before = numpy.array(data["coords_before"])
coordsets_before = numpy.reshape(coordsets_before, (number_of_sets, num_coords/3, 3))
coordsets_after = numpy.array(data["coords_after"])
coordsets_after = numpy.reshape(coordsets_after, (number_of_sets, num_coords/3, 3))
superimposed_translations = []
for i in range(number_of_sets):
coords = numpy.array([coordsets_before[i], coordsets_after[i]])
calculator = RMSDCalculator(calculatorType = "QTRFIT_OMP_CALCULATOR",
fittingCoordsets = coords)
_, rearranged_coords = calculator.oneVsTheOthers(0, get_superposed_coordinates = True)
superimposed_translations.append(rearranged_coords[1]-rearranged_coords[0])
translations = numpy.array(superimposed_translations)
norms = numpy.array([norm(t) for t in translations])
return numpy.max(norms, axis = 1), numpy.mean(norms, axis = 1)
selections = {}
for selection in datum["selection"]:
print selection
selections[selection] = pdb.select(datum["selection"][selection])
prody.writePDB(os.path.join("selections", selection), selections[selection])
#############################
# Motif VS Helix Distance
#############################
calculator = RMSDCalculator(
calculatorType="QCP_OMP_CALCULATOR",
fittingCoordsets=selections["motif_all"].getCoordsets(),
calculationCoordsets=selections["motif_backbone"].getCoordsets(),
)
motif_rmsd = calculator.oneVsTheOthers(conformation_number=0, get_superposed_coordinates=False)
residue_distances = []
for conf in selections["arg131_leu272"].getCoordsets():
arg131 = conf[0]
leu272 = conf[1]
residue_distances.append(distance(arg131, leu272))
exp_motif_rmsd = [0] + list(motif_rmsd)
matplotlib.pyplot.scatter(residue_distances, exp_motif_rmsd)
matplotlib.pyplot.savefig(os.path.join("plots", "motif_vs_helix_dist.svg"))
matplotlib.pyplot.close()
###########################################
# Backbone of ser 207 rmsd Vs ser to ligand distance
###########################################
