def get_titratable_residues(self, pdbfile):
#
# Call WHAT IF to get the titratable residues
#
import WI_tools, string, os
pdbfile = os.path.join(os.getcwd(), pdbfile)
titres = WI_tools.get_titratable_residues(pdbfile)
group = []
for res in titres:
res2 = string.replace(res, '(', '')
res2 = string.replace(res, ')', '')
split = string.split(res2)
#
# Terminal residue?
#
term = None
if split[-1][-4:] == 'TERM':
term = split[-1]
split = split[:-1]
if term:
#
# If a terminal we get rid of the residue name
#
resid = getWI_resid(res)
resname = resid.split(':')[-1]
resname = resid.replace(resname, '')
group.append([resname + term, int(float(split[-1]))])
else:
group.append([getWI_resid(res), int(float(split[-1]))])
return group
def clean_files(pdbfiles):
#
# Clean all PDB files
#
dict={}
print
print 'Cleaning all PDB files (deleting water molecules and reconstructing missing side chains)'
print
import WI_tools, os
for pdb in pdbfiles:
print 'Cleaning: %s' %pdb
newfile=pdb+'.clean.pdb'
dict[pdb]=newfile
if not os.path.isfile(pdb+'.clean.pdb'):
logfile,files=WI_tools.corall_delwat(os.path.join(os.getcwd(),pdb))
if len(files.keys())!=1:
raise "Could not clean ",pdb
#
# Write the new pdbfile
#
fd=open(newfile,'w')
for line in files[files.keys()[0]]:
fd.write(line)
fd.close()
print
print 'All files cleaned'
print '----------------------------------------'
return dict
def get_titratable_residues(self,pdbfile):
#
# Call WHAT IF to get the titratable residues
#
import WI_tools, string, os
pdbfile=os.path.join(os.getcwd(),pdbfile)
titres=WI_tools.get_titratable_residues(pdbfile)
group=[]
for res in titres:
res2=string.replace(res,'(','')
res2=string.replace(res,')','')
split=string.split(res2)
#
# Terminal residue?
#
term=None
if split[-1][-4:]=='TERM':
term=split[-1]
split=split[:-1]
if term:
#
# If a terminal we get rid of the residue name
#
resid=getWI_resid(res)
resname=resid.split(':')[-1]
resname=resid.replace(resname,'')
group.append([resname+term,int(float(split[-1]))])
else:
group.append([getWI_resid(res),int(float(split[-1]))])
return group
def clean_files(pdbfiles):
#
# Clean all PDB files
#
dict = {}
print
print 'Cleaning all PDB files (deleting water molecules and reconstructing missing side chains)'
print
import WI_tools, os
for pdb in pdbfiles:
print 'Cleaning: %s' % pdb
newfile = pdb + '.clean.pdb'
dict[pdb] = newfile
if not os.path.isfile(pdb + '.clean.pdb'):
logfile, files = WI_tools.corall_delwat(
os.path.join(os.getcwd(), pdb))
if len(files.keys()) != 1:
raise "Could not clean ", pdb
#
# Write the new pdbfile
#
fd = open(newfile, 'w')
for line in files[files.keys()[0]]:
fd.write(line)
fd.close()
print
print 'All files cleaned'
print '----------------------------------------'
return dict
os.mkdir(dir)
#
# Does the PDB file exist?
#
if not os.path.isfile(pdb):
failed.append(pdbid)
continue
os.system('cp %s %s' %(pdb,newpdb))
#
# Correct with WI
#
import WI_tools
renumb=1
setcha=None
readallmodels=None
logifile,files=WI_tools.delwat_dellig_corall(newpdb,renumb,readallmodels,setcha)
filenms=files.keys()
if len(filenms)!=1:
print 'More than one filename',filenms
failed.append(pdbid)
continue
import string
#
# Do we have a chain identifier?
#
chainid=-1
changes=0
for line in files[filenms[0]]:
split=string.split(line)
#print split
if split[0]=='ATOM':
def main():
#
# Run pKa calculations in all subdirs given as argument
#
import sys
print
print 'Run pKa calculations in all subdirs. '
print
print 'Looking for pdb files with the name: %s' % sys.argv[1]
print 'Running pKa calculations for: '
import os, sys, string
initial_pdbfile = 'pdbfile.pdb'
dirs = sys.argv[1:]
#
# if the last argument is '-name', then the name of the dir is part of the pdbfile name
#
top = os.getcwd()
for dir in dirs:
if os.path.isdir(dir):
print dir
print
print '----------------------'
for dir in dirs:
if os.path.isdir(dir):
initial_pdbfile = dir + '.pdb'
pkafile = os.path.join(dir, 'clean.pka.pdb')
if not os.path.isfile(pkafile):
import WI_tools
logfile, files = WI_tools.delwat_dellig_corall(
os.path.join(os.getcwd(), dir, initial_pdbfile), None, 1)
print os.path.join(os.getcwd(), dir, initial_pdbfile)
for line in logfile:
print line,
fs = files.keys()
print fs
if len(fs) != 1:
done = None
print dir
import string
pdbfile = string.lower(dir[-4:]) + '.pdb.gz'
pdbdir = '/net/scratch67/jnielsen/pdb'
pdbfile = os.path.join(pdbdir, pdbfile)
if os.path.isfile(pdbfile):
print 'Could not find file'
#a=raw_input('Get file from pdb dir? ')
#a='y'
a = 'n'
if a == 'y':
destination = os.path.join(
os.getcwd(), dir,
os.path.split(pdbfile)[1])
print destination
os.system('cp ' + pdbfile + ' ' + destination)
os.system(gunzip + ' ' + destination)
destination = destination[:-3]
print destination
os.link(
destination,
os.path.join(os.getcwd(), dir,
initial_pdbfile))
done = 1
if not done:
print initial_pdbfile
raise 'Huh? - could not clean PDB file'
else:
fd = open(pkafile, 'w')
fd2 = open(pkafile + '.backup', 'w')
for line in files[fs[0]]:
fd2.write(line)
fd.write(line)
fd.close()
fd2.close()
print 'Cleaned PDB file written'
#
# Prepare the pKa calculation (write the Invocation file)
#
import splitpdb
splitpdb.prep_pKa(dir, os.path.split(pkafile)[1])
os.chdir(dir)
import pKa
#
# Read the parameters
#
infile = 'Invocation'
fd = open(infile)
line = fd.readline()
fd.close()
argv = string.split(line)
#
# Do we need to run the pKa calculations for this dir?
#
pdbfile = argv[1]
if not os.path.isfile(pdbfile + '.PKA.DAT'):
print 'Running pKa calculations for: %s ' % dir
print
print 'Using command:', string.join(argv)
#
# Parse parameters
#
x = pKa.pKamisc()
params = x.parse_parameterlist(argv)
X = pKa.pKarun(os.getcwd(), pdbfile, params)
X.runseq()
print '============================='
print
os.chdir(top)
return
def main():
#
# Do all the analyses we want
#
print
print 'Design_plots.py: Do all analyses of the Design_pKa runs'
print
print 'Usage Design_plots.py [files] <type>'
print
#
# dpKa vs. distance from active site & number of mutations
#
import sys
#
# Get the type
#
type=sys.argv[-1]
if type=='two':
#
# Analysing a single group
#
files=get_files(sys.argv[1:-2],type,sys.argv[-2])
else:
#
# Get the files
#
files=get_files(sys.argv[1:-1],type)
#
# If not files then exit
#
if files==[]:
print 'Error: Did not find any files to match criteria'
return
#
# Prepare the data matrix
#
raw_data={}
max_dist=25
max_muts=20
distance_range=range(max_dist+1)
nummuts_range=range(1,max_muts+1)
for num in nummuts_range:
raw_data[num]={}
for dist in distance_range:
raw_data[num][dist]=[0.0]
#
# Loop over all the files
#
added=0
big_dict={}
tot_target={}
for file in files:
if file[-5:]=='.lock':
continue
print 'Processing %s' %file
try:
import pickle
fd=open(file)
d=pickle.load(fd)
fd.close()
except:
continue
#
# Set the prefix
#
prefix=get_prefix(file)
#
# -----------------------------------
#
# Loop over all the design-data
#
targets=d.keys()
targets.sort()
for target in targets:
#if target!=':0231:ASP':
# continue
#
# pdbfile and wt_full are not interesting
#
if target=='pdbfile' or target=='wt_full':
continue
target_pka=d[target]
designs=target_pka.keys()
designs.sort()
if designs==['pKa out of range']:
continue
#
# Loop over each design (normally +20 and -20 for Design_dist_nummuts)
#
for design in designs:
#if design!='m20':
# continue
try:
nummuts=target_pka[design].keys()
except:
#print 'Skipping:',target_pka[design]
continue
nummuts.sort()
for num in nummuts:
dist_cutoffs=target_pka[design][num].keys()
for cutoff in dist_cutoffs:
#text='%15s %4s #muts: %2d, dist_co: %5.2f, sols:' %(target,design,num,float(cutoff))
#print text
#
# Make sure we have a bin for the this distance cutoff
#
#if not raw_data[num].has_key(cutoff):
# raw_data[num][cutoff]=[]
#
# Loop over all solutions and store the dpKa values
#
sol_dict=target_pka[design][num][cutoff]
solutions=sol_dict.keys()
#
# Loop over all the solutions
#
for sol in solutions:
if sol_dict[sol].has_key(type):
dpka=sol_dict[sol][type][target]
mutations=sol_dict[sol]['mutations']
#
# Count the number of mutations
#
nums=0
for mut in mutations:
if mut:
nums=nums+1
#
# Add the data to the array
#
# We skip all data points outside the range specified
# by max_muts and max_dist
#
skip=None
if not raw_data.has_key(nums):
skip=1
if not skip:
if not raw_data[nums].has_key(cutoff):
skip=1
if not skip:
raw_data[nums][cutoff].append(dpka)
#
# Add to the big dictionary
#
import os
tname=prefix+target
if not big_dict.has_key(tname):
big_dict[tname]=[]
clean_muts=[]
for mut in mutations:
if mut:
clean_muts.append(mut)
big_dict[tname].append([clean_muts,dpka])
#
# Keep track of how many we add
#
added=added+1
#print 'Adding: nummuts: %2d, cutoff: %4.1f, dpka: %4.2f' %(nums,cutoff,dpka)
#except:
# pass
#print '--------------------'
#
# Read the definition of the active site
#
act_site=read_actsit_def()
#
# Get properties from the PDB files/wt pKa calculation
#
import string, os
for file in files:
if file[-5:]=='.lock':
continue
prefix=get_prefix(file)
#
# Analysis
#
print 'Analysing for %s' %prefix
#
# Read the PDB file
#
pdbfile=os.path.join(basedir,prefix[:4],prefix)
import Protool
Z=Protool.structureIO()
Z.readpdb(pdbfile)
#
# Get the relative accs
#
import WI_tools
accs=WI_tools.relative_accessibility(pdbfile)
#
# Open the wt pKa calc
#
import pKaTool.pKaIO as pKaIO
X=pKaIO.pKaIO(pdbfile)
pkavals=X.readpka()
matrix=X.read_matrix()
for residue in pkavals.keys():
target=prefix+residue
if not tot_target.has_key(target):
tot_target[target]={}
tot_target[target]['pKa']=pkavals[residue]['pKa']
elecs=[]
for other_res in matrix[residue].keys():
elecs.append(matrix[residue][other_res][0])
tot_target[target]['elecs']=elecs[:]
#
# Insert number of aas
#
tot_target[target]['prot_aas']=len(Z.residues.keys())
#
# Is this target in the vicinity of the active site?
#
tot_target[target]['act_site']=None
target_res=target.split('pdb')[1]
target_res=':'+target_res.split(':')[1]
try:
target_atoms=Z.residues[target_res]
except:
print target_res
print Z.residues.keys()
stop
if act_site.has_key(prefix):
for act_res in act_site[prefix]:
r_act_res=':'+act_res.split(':')[1]
for atom2 in Z.residues[r_act_res]:
for target_atom in target_atoms:
#print 'Comparing',target_atom,atom2
if Z.distance(target_atom,atom2)<5.0:
tot_target[target]['act_site']='Yes'
#
# Insert rel. acc
#
if residue[-6:]==':CTERM':
residue=residue[:-6]
if residue[-6:]==':NTERM':
residue=residue[:-6]
#print accs[residue]['sum']
tot_target[target]['relacc']=accs[residue]['sum']['rel']
#print residue,accs[residue]
print
print ' All done'
#
# How many solutions in total?
#
print 'I added %5d solutions to the matrix' %added
#
# For each target, what's the maximum dpKa?
#
targets=big_dict.keys()
targets.sort()
max_dpkas=[]
all=[]
actsite_dpkas=[]
all_actsite_dpkas=[]
file_dpkas={}
for target in targets:
tmp_dpkas=[]
for solution,dpka in big_dict[target]:
tmp_dpkas.append(abs(dpka))
if not file_dpkas.has_key(target[:4]):
file_dpkas[target[:4]]={}
file_dpkas[target[:4]]['dpkas']=[]
file_dpkas[target[:4]]['num_target']=0
file_dpkas[target[:4]]['max_dpka']=0.0
#
# Add the new dpKa
#
file_dpkas[target[:4]]['dpkas'].append(abs(dpka))
avg,var,sdev=average(tmp_dpkas)
print 'Average pKa shift for %25s is %5.2f (%5.2f)' %(target,avg,sdev)
tmp_dpkas.sort()
max_dpka=tmp_dpkas[-1]
max_dpkas.append(max_dpka)
all=all+tmp_dpkas
#
# Store the average and max dpka for each target
#
tot_target[target]['avg_dpka']=avg
tot_target[target]['max_dpka']=max_dpka
#
# Set the aa size
#
file_dpkas[target[:4]]['prot_aas']=tot_target[target]['prot_aas']
#
# Increment the number of targets designed for this protein
#
file_dpkas[target[:4]]['num_target']=file_dpkas[target[:4]]['num_target']+1
#
# Is is an active site target?
#
if tot_target[target]['act_site']:
actsite_dpkas.append(max_dpka)
all_actsite_dpkas=all_actsite_dpkas+tmp_dpkas
#
# Write the PDB files
#
for file in files:
tf_max=[]
if file[-5:]=='.lock':
continue
prefix=get_prefix(file)
#
# Writing Yasara script
#
print 'Writing Yasara script for %s' %prefix
#
# Read the PDB file
#
fd=open('yasara.mcr','w')
pdbfile=os.path.join(basedir,prefix[:4],prefix)
fd.write('LoadPDB %s\n' %pdbfile)
fd.write('ColorAll 606060\n')
fd.write('Style Stick\n')
fd.write('HUD Off\n')
fd.write('HideRes Hoh\n')
import Protool
Z=Protool.structureIO()
Z.readpdb(pdbfile)
#
# Zero all B-factors
#
#for residue in Z.residues.keys():
# for atom in Z.residues[residue]:
# Z.atoms[atom]['B-factor']=0.0
#
# Loop over all targets and set the colour
#
colors={1.0:'Blue',
2.0:'Cyan',
3.0:'Green',
4.0:'Yellow',
5.0:'Red'}
for target in tot_target.keys():
#
# Collect stats on the max abs(dpka)
#
pos=target.find(prefix)
if pos!=-1:
if tot_target[target].has_key('max_dpka'):
tf_max.append(abs(tot_target[target]['max_dpka']))
#
# Write the PDB file
#
if pos!=-1:
resnum=target[pos+len(prefix):]
resnum=':'+resnum.split(':')[1]
if Z.residues.has_key(resnum):
if tot_target[target].has_key('max_dpka'):
col_cutoff=colors.keys()
col_cutoff.sort()
co=0.5
for col in col_cutoff:
co=col
if tot_target[target]['max_dpka']<col:
break
colour=colors[co]
fd.write('ColorRes %d,%s\n' %(int(resnum[1:]),colour))
else:
fd.write('ColorRes %d,%s\n' %(int(resnum[1:]),'aaaaaa'))
else:
raise 'Residue not found',target
#Z.writepdb('BF_dpKa')
print 'Number of max_pkas in %s is %d' %(prefix,len(tf_max))
avg,var,sdev=average(tf_max)
print '%s, average max dpKa %5.1f, sdev: %5.1f' %(prefix,avg,sdev)
#fd.write('exit\n')
fd.close()
#
# Print all the stats
#
print
print 'Number of targets designed is : %4d ' %(len(targets))
all_targets=len(tot_target.keys())
print 'Number of targets in total: : %4d ' %all_targets
print '%% designed : %5.2f' %(float(len(targets))/float(all_targets)*100.0)
print
print 'Number of active site targets : %5.2f' %(len(actsite_dpkas))
#
# Get average Delta pKas
#
avg,var,sdev=average(all)
print 'Average dpKa for all targets is : %5.2f (%5.2f)' %(avg,sdev)
avg,var,sdev=average(max_dpkas)
print 'Average MAX dpKa for all targets is :%5.2f (%5.2f)' %(avg,sdev)
# Max dpka for active sites
avg,var,sdev=average(actsite_dpkas)
print 'Average MAX dpKa for active site targets: %5.2f (%5.2f)' %(avg,sdev)
#
avg,var,sdev=average(all_actsite_dpkas)
print 'Average dpKa for actsit target :%5.2f (%5.2f)' %(avg,sdev)
print
print 'Average dpKa per protein'
prots=file_dpkas.keys()
prots.sort()
for prot in prots:
avg,var,sdev=average(file_dpkas[prot]['dpkas'])
num_target=file_dpkas[prot]['num_target']
aa_size=file_dpkas[prot]['prot_aas']
num_sol=len(file_dpkas[prot]['dpkas'])
print 'Average dpKa for %s is : %5.2f (%5.2f) [#targets %4d, #aas %4d, #sols/target %5.2f]' %(prot,avg,sdev,num_target,aa_size,float(num_sol)/float(num_target))
#
# Stats on the types of targets designed
#
designed={}
import pKarun
Y=pKarun.pKanalyse()
for target in big_dict.keys():
rtype=Y.get_residue_type(target)
if not designed.has_key(rtype):
designed[rtype]=0
designed[rtype]=designed[rtype]+1
des=designed.keys()
#
# Look at the targets not designed
#
not_designed={}
all_targets=tot_target.keys()
all_targets.sort()
import pKarun
Y=pKarun.pKanalyse()
for target in all_targets:
if not big_dict.has_key(target):
rtype=Y.get_residue_type(target)
if not not_designed.has_key(rtype):
not_designed[rtype]=0
not_designed[rtype]=not_designed[rtype]+1
#
# Stats
#
print
print 'Stats on types of groups designed'
types=['ASP','GLU','TYR','CYS','CTERM','NTERM','LYS','ARG','HIS']
types.sort()
for rtyp in types:
if designed.has_key(rtyp):
des=designed[rtyp]
else:
des=0
if not_designed.has_key(rtyp):
ndes=not_designed[rtyp]
else:
ndes=0
tot=ndes+des
if tot>0:
avg='%5.2f' %(float(des)/float(tot)*100.0)
else:
avg='NA'
print '%8s des: %3d notD: %3d, tot: %3d %% designed: %s' %(rtyp,des,ndes,tot,avg)
#
# Relation between average dpKa obtained and accessibility, type and electrostatic interactions.
#
print
#
# Plot of avg dpKa vs. sum of abs electrostatic interactions
#
avg_dpka=[]
max_dpka=[]
sum_elec=[]
acc=[]
for target in all_targets:
dpkas=[]
if big_dict.has_key(target):
for mutants,dpka in big_dict[target]:
dpkas.append(abs(dpka))
e_sum=[]
for elec in tot_target[target]['elecs']:
e_sum.append(elec)
#
max_dpka.append(max(dpkas))
#
avg,var,sdev=average(dpkas)
avg_dpka.append(avg)
#
avg,var,sdev=average(e_sum)
sum_elec.append(get_sum(e_sum))
#
# Accessibility
#
acc.append(tot_target[target]['relacc'])
else:
#print 'No design for',target
pass
import dislin_driver
file=dislin_driver.graf_mult2(acc,[avg_dpka,max_dpka],
title='Effect of solvent exposure',
x_legend='Relative accessibility of target',
y_legend='abs(dpKa)',
legends=['Avg. dpKa','Max. dpKa'])
#os.system('eog %s' %file)
#
# Any difference for active site targets?
#
#
# Plot it
#
nummuts={}
nums=raw_data.keys()
nums.sort()
for num in nums:
for co in raw_data[num].keys():
max_val=-1.0
sum=0.0
count=0
for dpka in raw_data[num][co]:
if abs(dpka)>max_val:
max_val=abs(dpka)
if dpka>0.01:
sum=sum+abs(dpka)
count=count+1
#
# Sort as function of number of mutations for other stat
#
if not nummuts.has_key(num):
nummuts[num]=[]
nummuts[num].append(abs(dpka))
if count==0:
raw_data[num][co]=0
else:
raw_data[num][co]=float(sum)/float(count)
#raw_data[num][co]=max_val
import dislin_driver
#dislin_driver.colour_2D(raw_data,'','','# of mutations','distance from target (A)','abs(dpKa)','dpka.tif')
import os
#os.system('gimp dpka.tif')
#
# Get dpKa as a function of # of mutants
#
#nums=nummuts.keys()
#nums.sort()
#x=[]
#y=[]
#for num in nums:
# for dpka in nummuts[num]:
# x.append(num)
# y.append(dpka)
#file=dislin_driver.graf_mult2(x,[y],
# title='dpKa, number of mutations',
# x_legend='Number of mutations',
# y_legend='abs(dpKa)')
#os.system('gimp %s' %file)
#
# Save bigdict
#
fd=open('/home/nielsen/pKa-design/done_distnummuts/bigdict','w')
import pickle
pickle.dump(big_dict,fd)
fd.close()
def main():
#
# Run pKa calculations in all subdirs given as argument
#
import sys
print
print 'Run pKa calculations in all subdirs. '
print
print 'Looking for pdb files with the name: %s' %sys.argv[1]
print 'Running pKa calculations for: '
import os, sys, string
initial_pdbfile='pdbfile.pdb'
dirs=sys.argv[1:]
#
# if the last argument is '-name', then the name of the dir is part of the pdbfile name
#
top=os.getcwd()
for dir in dirs:
if os.path.isdir(dir):
print dir
print
print '----------------------'
for dir in dirs:
if os.path.isdir(dir):
initial_pdbfile=dir+'.pdb'
pkafile=os.path.join(dir,'clean.pka.pdb')
if not os.path.isfile(pkafile):
import WI_tools
logfile,files=WI_tools.delwat_dellig_corall(os.path.join(os.getcwd(),dir,initial_pdbfile),None,1)
print os.path.join(os.getcwd(),dir,initial_pdbfile)
for line in logfile:
print line,
fs=files.keys()
print fs
if len(fs)!=1:
done=None
print dir
import string
pdbfile=string.lower(dir[-4:])+'.pdb.gz'
pdbdir='/net/scratch67/jnielsen/pdb'
pdbfile=os.path.join(pdbdir,pdbfile)
if os.path.isfile(pdbfile):
print 'Could not find file'
#a=raw_input('Get file from pdb dir? ')
#a='y'
a='n'
if a=='y':
destination=os.path.join(os.getcwd(),dir,os.path.split(pdbfile)[1])
print destination
os.system('cp '+pdbfile+' '+destination)
os.system(gunzip+' '+destination)
destination=destination[:-3]
print destination
os.link(destination,os.path.join(os.getcwd(),dir,initial_pdbfile))
done=1
if not done:
print initial_pdbfile
raise 'Huh? - could not clean PDB file'
else:
fd=open(pkafile,'w')
fd2=open(pkafile+'.backup','w')
for line in files[fs[0]]:
fd2.write(line)
fd.write(line)
fd.close()
fd2.close()
print 'Cleaned PDB file written'
#
# Prepare the pKa calculation (write the Invocation file)
#
import splitpdb
splitpdb.prep_pKa(dir,os.path.split(pkafile)[1])
os.chdir(dir)
import pKa
#
# Read the parameters
#
infile='Invocation'
fd=open(infile)
line=fd.readline()
fd.close()
argv=string.split(line)
#
# Do we need to run the pKa calculations for this dir?
#
pdbfile=argv[1]
if not os.path.isfile(pdbfile+'.PKA.DAT'):
print 'Running pKa calculations for: %s ' %dir
print
print 'Using command:',string.join(argv)
#
# Parse parameters
#
x=pKa.pKamisc()
params=x.parse_parameterlist(argv)
X=pKa.pKarun(os.getcwd(),pdbfile,params)
X.runseq()
print '============================='
print
os.chdir(top)
return
os.mkdir(dir)
#
# Does the PDB file exist?
#
if not os.path.isfile(pdb):
failed.append(pdbid)
continue
os.system('cp %s %s' % (pdb, newpdb))
#
# Correct with WI
#
import WI_tools
renumb = 1
setcha = None
readallmodels = None
logifile, files = WI_tools.delwat_dellig_corall(newpdb, renumb,
readallmodels, setcha)
filenms = files.keys()
if len(filenms) != 1:
print 'More than one filename', filenms
failed.append(pdbid)
continue
import string
#
# Do we have a chain identifier?
#
chainid = -1
changes = 0
for line in files[filenms[0]]:
split = string.split(line)
#print split
if split[0] == 'ATOM':