def score(session, atomic_model, map_model, rez):
''' Perform the CCC score. Takes a session, a single model and map.'''
print("Calculating CCC Score")
# make class instances for density simulation (blurring), scoring and plot scores
blurrer = StructureBlurrer()
scorer = ScoringFunctions()
atomlist = []
for atom in atomic_model.atoms:
atomlist.append(chimera_to_tempy_atom(atom, len(atomlist)))
bio_atom_structure = BioPy_Structure(atomlist)
bio_map_structure = chimera_to_tempy_map(map_model)
map_probe = blurrer.gaussian_blur(bio_atom_structure,
rez,
densMap=bio_map_structure)
score = scorer.CCC(bio_map_structure, map_probe)
print(score)
return score
os.mkdir(path_out)
os.chdir(path_out)
structure_instance=PDBParser.read_PDB_file('1J6Z','1J6Z.pdb',hetatm=False,water=False)
print structure_instance
blurrer = StructureBlurrer()
EnsembleGeneration=EnsembleGeneration()
scorer = ScoringFunctions()
map_target=MapParser.readMRC('emd_5168_monomer.mrc') #read target map
map_probe = blurrer.gaussian_blur(structure_instance, 6.6,densMap=map_target)#create a simulated map from the structure instance
#Create a Random ensemble of 10 structures randomly within 5 A translation and 60 deg rotation.
list_rotate_models=EnsembleGeneration.randomise_structs(structure_instance, 10, 5, 60, v_grain=30, rad=False,write=True)
#CCC score from starting fit
line='%s %s\n'%('1J6Z',scorer.CCC(map_probe,map_target))
count=0
#loop to score each of the alternative fits in the ensemble
for mod in list_rotate_models:
count+=1
mod_name=mod[0]
mod_structure_instance=mod[1]
map_probe = blurrer.gaussian_blur(mod_structure_instance, 6.6,densMap=map_target,sigma_coeff=0.187)
line+='%s %s\n'%(mod_name,scorer.CCC(map_probe,map_target))
print line
water=False)
print structure_instance2
blurrer = StructureBlurrer()
scorer = ScoringFunctions()
Plot = Plot()
emmap = MapParser.readMRC('emd_5168_monomer.mrc') #read target map
print emmap
sim_map = blurrer.gaussian_blur(structure_instance,
6.6,
densMap=emmap,
sigma_coeff=sim_sigma_coeff,
normalise=True)
print 'structure_instance', scorer.CCC(sim_map, emmap)
print sim_map
sim_map2 = blurrer.gaussian_blur(structure_instance2,
6.6,
densMap=emmap,
sigma_coeff=sim_sigma_coeff,
normalise=True)
print 'structure_instance_same', scorer.CCC(sim_map2, emmap)
SCCC_list_structure_instance = []
listRB = RBParser.read_FlexEM_RIBFIND_files(rb_file, structure_instance2)
for RB in listRB:
score_SCCC = scorer.SCCC(emmap, 6.6, sim_sigma_coeff, structure_instance2,
RB)
SCCC_list_structure_instance.append(score_SCCC)
map_probe = blurrer.gaussian_blur(structure_instance, 6.6,densMap=map_target)#create a simulated map from the structure instance
map_probe.write_to_MRC_file("map_probe_actin.mrc") #write simulated map to a MRC file format
##SCORING FUNCTION
print "Calculate Envelope Score (ENV):"
molecualr_weight=structure_instance.get_prot_mass_from_atoms()
#Mmolecualr_weight=structure_instance.get_prot_mass_from_res()
first_bound=map_target.get_primary_boundary(molecualr_weight, map_target.min(), map_target.max())
#print scorer.envelope_score_APJ(map_target, first_bound, structure_instance,norm=True)
print scorer.envelope_score(map_target, first_bound, structure_instance,norm=True)
print "Calculate Mutual information Score (MI)"
print scorer.MI(map_target,map_probe)
print "Calculate Laplacian cross-correlation Score (LAP)"
print scorer.laplace_CCC(map_target,map_probe)
print "Calculate cross-correlation Score (CCC)"
print scorer.CCC(map_target,map_probe)
print "Calculate Normal Vector (NV):"
#Number of points to use in the normal vector score
points= round((map_target.map_size())*0.01)
first_bound=map_target.get_primary_boundary(structure_instance.get_prot_mass_from_atoms(), map_target.min(), map_target.max())
second_bound=map_target.get_second_boundary(first_bound, points, first_bound, map_target.max(),err_percent=1)
print scorer.normal_vector_score(map_target,map_probe, first_bound, second_bound)
print "Calculate Normal Vector (NV) with Sobel Filter:"
print scorer.normal_vector_score(map_target,map_probe, first_bound, second_bound,Filter='Sobel')
