def tabulate(d_mmCIF_main,):
l_pdbs = list(set(d_mmCIF_main.keys()))
l_pdbs.sort()
l = ['ID mut T pH res spacegroup startingmodel\n']
for i in range(len(l_pdbs)):
pdb = l_pdbs[i]
d_mmCIF = d_mmCIF_main[pdb]
if d_mmCIF['_entity_poly_seq.mon_id'] == ref_seq:
s_mutation = 'wt'
else:
for i in range(len(d_mmCIF['_entity_poly_seq.mon_id'])):
res_id_mmCIF = d_mmCIF['_entity_poly_seq.mon_id'][i]
res_id_ref = ref_seq[i]
if res_id_mmCIF != res_id_ref:
res_no = i+1
s_mutation = '%1s%3i%1s' %(d_321[res_id_ref],res_no,d_321[res_id_mmCIF],)
spacegroup = core.parse_mmCIF_item(d_mmCIF,'_symmetry.space_group_name_H-M',)
T = core.parse_mmCIF_item(d_mmCIF,'_diffrn.ambient_temp',)
pH = core.parse_mmCIF_item(d_mmCIF,'_exptl_crystal_grow.pH',)
starting_model = core.parse_mmCIF_item(d_mmCIF,'_refine.pdbx_starting_model',)
resolution = core.parse_mmCIF_item(d_mmCIF,'_refine.ls_d_res_high',)
l += ['%4s %-5s %5s %5s %3.1f %-10s %4s\n' %(pdb,s_mutation,T,pH,float(resolution),spacegroup,starting_model,)]
fd = open('HEWL_table.txt','w')
fd.writelines(l)
fd.close()
return
def plot(d_mmCIF_main,d_rmsds,):
l_pdbs = d_rmsds.keys()
l_pdbs.sort()
l_temperature = []
l_ph = []
l_resolution = []
d_spacegroup = {}
d_starting_model = {}
l_correl_T = [[],[],]
l_correl_pH = [[],[],]
l_correl_resol_max = [[],[],]
d_histo_pH = {}
d_histo_T = {}
d_histo_resol = {}
for i1 in range(len(l_pdbs)-1):
pdb1 = l_pdbs[i1]
spacegroup1 = core.parse_mmCIF_item(d_mmCIF_main[pdb1[:4]],'_symmetry.space_group_name_H-M',)
T1 = core.parse_mmCIF_item(d_mmCIF_main[pdb1[:4]],'_diffrn.ambient_temp',)
pH1 = core.parse_mmCIF_item(d_mmCIF_main[pdb1[:4]],'_exptl_crystal_grow.pH',)
starting_model1 = core.parse_mmCIF_item(d_mmCIF_main[pdb1[:4]],'_refine.pdbx_starting_model',)
resolution1 = core.parse_mmCIF_item(d_mmCIF_main[pdb1[:4]],'_refine.ls_d_res_high',)
for i2 in range(i1+1,len(l_pdbs)):
pdb2 = l_pdbs[i2]
spacegroup2 = core.parse_mmCIF_item(d_mmCIF_main[pdb2[:4]],'_symmetry.space_group_name_H-M',)
T2 = core.parse_mmCIF_item(d_mmCIF_main[pdb2[:4]],'_diffrn.ambient_temp',)
pH2 = core.parse_mmCIF_item(d_mmCIF_main[pdb2[:4]],'_exptl_crystal_grow.pH',)
starting_model2 = core.parse_mmCIF_item(d_mmCIF_main[pdb2[:4]],'_refine.pdbx_starting_model',)
resolution2 = core.parse_mmCIF_item(d_mmCIF_main[pdb2[:4]],'_refine.ls_d_res_high',)
rmsd = d_rmsds[pdb1][pdb2]
if rmsd > 1:
print pdb1, pdb2, rmsd
if T1 and T2:
T_diff = abs(float(T2)-float(T1))
l_temperature += ['%s %s\n' %(T_diff,rmsd),]
l_correl_T[0] += [T_diff]
l_correl_T[1] += [rmsd]
print T_diff, 10*round(T_diff/10.,0)
if not 10*round(T_diff/10.,0) in d_histo_T.keys():
d_histo_T[10*round(T_diff/10.,0)] = 0
d_histo_T[10*round(T_diff/10.,0)] += 1
if pH1 and pH2:
pH_diff = abs(float(pH2)-float(pH1))
l_ph += ['%s %s\n' %(pH_diff,rmsd),]
l_correl_pH[0] += [pH_diff]
l_correl_pH[1] += [rmsd]
if not pH_diff in d_histo_pH.keys():
d_histo_pH[pH_diff] = 0
d_histo_pH[pH_diff] += 1
resolution_max = max(resolution1,resolution2,)
l_resolution += ['%s %s\n' %(resolution_max,rmsd),]
if resolution_max != 'N/A':
l_correl_resol_max[0] += [float(resolution_max)]
l_correl_resol_max[1] += [rmsd]
if not round(float(resolution_max),0) in d_histo_resol.keys():
d_histo_resol[round(float(resolution_max),0)] = 0
d_histo_resol[round(float(resolution_max),0)] += 1
d_spacegroup = append_to_dictionary(d_spacegroup,spacegroup1,spacegroup2,rmsd,)
d_starting_model = append_to_dictionary(d_starting_model,starting_model1,starting_model2,rmsd,)
r1 = statistics.correlation(l_correl_T[0],l_correl_T[1],)
r2 = statistics.correlation(l_correl_pH[0],l_correl_pH[1],)
r3 = statistics.correlation(l_correl_resol_max[0],l_correl_resol_max[1],)
##
## plot histograms
##
for prefix,d in [
['deltapH',d_histo_pH,],
['deltaT',d_histo_T,],
['maxresolution',d_histo_resol,],
]:
l = []
l_diffs = d.keys()
l_diffs.sort()
for diff in l_diffs:
l += ['%s %s\n' %(diff,d[diff],)]
fd = open('histo_%s.txt' %(prefix),'w')
fd.writelines(l)
fd.close()
l = [
'set terminal postscript eps enhanced color "Helvetica"\n',
'set output "gnuplot.ps"\n',
'set size 3,3\n',
'set style data histogram\n',
'set xtics rotate\n',
'set xlabel "%s\n' %(prefix),
'set ylabel "count\n',
'plot "histo_%s.txt" u 2:xtic(1) t ""\n' %(prefix)
]
fd = open('tmp.txt','w')
fd.writelines(l)
fd.close()
os.system('gnuplot tmp.txt')
os.system('convert gnuplot.ps histo_%s.png' %(prefix))
##
## plot rmsd as a function of each property (2d)
##
for prefix,data,xlabel in [
['pH',l_ph,'pH diff',],
['Temperature',l_temperature,'T diff',],
['resolution',l_resolution,'maximum resolution',],
]:
prefix += method
fd = open('%s.gnuplotdata' %(prefix),'w')
fd.writelines(data)
fd.close()
gnuplot.scatter_plot_2d(
prefix,xlabel=xlabel,ylabel='RMSD %s' %(method,),
## averages=True,
regression=True,
)
##
## plot rmsd as a function of each property (contour)
##
for d,prefix in [
[d_spacegroup,'spacegroup',],
[d_starting_model,'startingmodel',],
]:
d_tics = {}
l_tics = d.keys()
l_tics.sort()
for i in range(len(l_tics)):
d_tics[l_tics[i]] = i+.5
z1 = 9
z2 = 0
l_data = []
for x in range(len(l_tics)):
k1 = l_tics[x]
for y in range(len(l_tics)):
k2 = l_tics[y]
if not k2 in d[k1].keys():
average = 9
else:
l_rmsds = d[k1][k2]
average = sum(l_rmsds)/len(l_rmsds)
if average < z1:
z1 = average
if average > z2:
z2 = average
l_data += ['%s %s %s\n' %(x,y,average,)]
l_data += ['%s %s %s\n' %(x,y+1,1,)]
l_data += ['\n']
for y in range(len(l_tics)):
l_data += ['%s %s %s\n' %(x+1,y,1,)]
l_data += ['%s %s %s\n' %(x+1,y+1,1,)]
l_data += ['\n']
gnuplot.contour_plot(
prefix,l_data,
title='%s %s' %(prefix,method,),zlabel='RMSD %s' %(method),
d_xtics = d_tics, d_ytics = d_tics,
palette = '0 1 0 0, 0.9999 0 0 1, 0.9999 1 1 1, 1 1 1 1',
z1 = z1, z2 = z2+0.1,
bool_remove = False,
)
os.system('convert %s.ps %s_spacegroup%s_mutations%s_atoms%s.png' %(prefix,prefix,spacegroup.replace(' ',''),n_mutations_max,method,))
## os.remove('%s.ps' %(prefix,))
print d_spacegroup
print d_starting_model
print r1
print r2
print r3
return
