def get_solutions(pdbfile,target_residue,target_dpKa,dpKas):
#
# Main routine.
# Get the solutions with MC_tab and then rescore them with a subset of
# the different methods
#
# Get solutions
#
import Design_pKa
defaults=Design_pKa.get_defaults()
defaults=get_this_defaults(pdbfile,target_residue,target_dpKa)
# Method
defaults['MC'][0]=1
defaults['tabulated'][0]=1
#
# Check if we already did this..
#
missing=None
for x in range(1,11):
if not dpKas.has_key(x):
missing=1
print 'Design solutions not calculated'
break
#
# Do it?
#
if missing:
solutions=Design_pKa.run_opt(defaults)
solution_keys=solutions.keys()
else:
solution_keys=dpKas.keys()
solution_keys.sort()
#
# Calculate pKa values using the other methods
#
# Define all the other methods that we will use
#
# First element is method, second is value of 'tabulated'
#
methods=[['TR',None],[None,None],['MC',None],['TR',1],['MC',1]]
for solution in solution_keys[:10]:
#
# Make sure the dictionary is ready
#
if not dpKas.has_key(solution):
dpKas[solution]={}
dpKas[solution]['mutations']=solutions[solution]['mutations']
realmut=None
for mutation in solutions[solution]['mutations']:
if mutation:
realmut=1
if not realmut:
continue
#
# Now calc dpKas with different methods
#
for method,tabulated in methods:
#
# Print info
#
print
print 'Calculating dpKa for %s with %s. Tabulated: %s' %(str(dpKas[solution]['mutations']),method,tabulated)
#
# Get defaults
#
defaults=get_this_defaults(pdbfile,target_residue,target_dpKa)
if method:
# Choose method
defaults[method][0]=1
#
# Set tabulated
#
defaults['tabulated'][0]=tabulated
#
# Set the general parameters
#
mutations=dpKas[solution]['mutations']
import string
#
# Remove all None elements from mutations
#
filtered_muts=[]
for mut in mutations:
if mut:
filtered_muts.append(mut)
#
# Set the mutations variable in defaults
#
defaults['mutations'][0]=string.join(filtered_muts,',')
# Calculate delta pkas
defaults['calc_dpka'][0]=1
#
# Set a more meaningful method name
#
method_name=method
if not method:
method_name='phi/ln10'
#
# Is this tabulated mode?
#
if tabulated:
method_name=method_name+'_tab'
#
# Get the dpKa(s)
#
if not dpKas[solution].has_key(method_name):
tmp=Design_pKa.run_opt(defaults)
#
# We should have only one key
#
keys=tmp.keys()
if len(keys)!=1:
print keys
raise 'Too many keys when calculating dpkas for one solution'
dpKas[solution][method_name]=tmp[keys[0]]
#
# Done
#
return dpKas
def get_solutions(pdbfile, target_residue, target_dpKa, dpKas):
#
# Main routine.
# Get the solutions with MC_tab and then rescore them with a subset of
# the different methods
#
# Get solutions
#
import Design_pKa
defaults = Design_pKa.get_defaults()
defaults = get_this_defaults(pdbfile, target_residue, target_dpKa)
# Method
defaults['MC'][0] = 1
defaults['tabulated'][0] = 1
#
# Check if we already did this..
#
missing = None
for x in range(1, 11):
if not dpKas.has_key(x):
missing = 1
print 'Design solutions not calculated'
break
#
# Do it?
#
if missing:
solutions = Design_pKa.run_opt(defaults)
solution_keys = solutions.keys()
else:
solution_keys = dpKas.keys()
solution_keys.sort()
#
# Calculate pKa values using the other methods
#
# Define all the other methods that we will use
#
# First element is method, second is value of 'tabulated'
#
methods = [['TR', None], [None, None], ['MC', None], ['TR', 1], ['MC', 1]]
for solution in solution_keys[:10]:
#
# Make sure the dictionary is ready
#
if not dpKas.has_key(solution):
dpKas[solution] = {}
dpKas[solution]['mutations'] = solutions[solution]['mutations']
realmut = None
for mutation in solutions[solution]['mutations']:
if mutation:
realmut = 1
if not realmut:
continue
#
# Now calc dpKas with different methods
#
for method, tabulated in methods:
#
# Print info
#
print
print 'Calculating dpKa for %s with %s. Tabulated: %s' % (str(
dpKas[solution]['mutations']), method, tabulated)
#
# Get defaults
#
defaults = get_this_defaults(pdbfile, target_residue, target_dpKa)
if method:
# Choose method
defaults[method][0] = 1
#
# Set tabulated
#
defaults['tabulated'][0] = tabulated
#
# Set the general parameters
#
mutations = dpKas[solution]['mutations']
import string
#
# Remove all None elements from mutations
#
filtered_muts = []
for mut in mutations:
if mut:
filtered_muts.append(mut)
#
# Set the mutations variable in defaults
#
defaults['mutations'][0] = string.join(filtered_muts, ',')
# Calculate delta pkas
defaults['calc_dpka'][0] = 1
#
# Set a more meaningful method name
#
method_name = method
if not method:
method_name = 'phi/ln10'
#
# Is this tabulated mode?
#
if tabulated:
method_name = method_name + '_tab'
#
# Get the dpKa(s)
#
if not dpKas[solution].has_key(method_name):
tmp = Design_pKa.run_opt(defaults)
#
# We should have only one key
#
keys = tmp.keys()
if len(keys) != 1:
print keys
raise 'Too many keys when calculating dpkas for one solution'
dpKas[solution][method_name] = tmp[keys[0]]
#
# Done
#
return dpKas
def get_solutions_dist_nummuts(pdbfile=None,
target_residue=None,
target_dpKa=2.0,
num_muts=6,
min_target_dist=5.0,
dpKas={},
method='MC',
pKMCsteps=0,
X=None):
#
# Check if we already did this..
#
#
# Do we have solutions for this problem?
#
added_data = None
missing = None
if dpKas == {}:
missing = 1
#
# Do it?
#
if missing:
defaults = local_defaults(pdbfile, target_residue, target_dpKa,
float(min_target_dist), pKMCsteps)
defaults['max_mutations'][0] = int(num_muts)
print 'Target: %s, dpKa: %s, Number of mutations: %3d, min_dist: %5.3f' % (
target_residue, target_dpKa, defaults['max_mutations'][0],
defaults['min_target_dist'][0])
#
# Set the method default
#
allmethods = ['TR', 'MC']
for m in allmethods:
defaults[m][0] = None
if not method == 'phidiff':
defaults[method][0] = 1
#
# Set the parameters
#
if not X:
params = {}
for key in defaults.keys():
params[key] = defaults[key][0]
X = Design_pKa.Design_pKa(params)
#
# Get the solutions and the corresponding dpKas
#
solutions, dpKa_dict = Design_pKa.run_opt(defaults, X)
solution_keys = solutions.keys()
#
#
# If there are no solutions then store that info
#
if len(solution_keys) == 0:
added_data = 1
dpKas[1] = {'no solutions': 1}
#
# Loop over all the solutions and get the dpKas
#
for solution in solution_keys:
#
# Make sure the dictionary is ready
#
if not dpKas.has_key(solution):
added_data = 1
dpKas[solution] = {}
#
# Check that we have a real mutation (i.e. a non- None) mutation
#
dpKas[solution]['mutations'] = solutions[solution]['mutations']
realmut = None
for mutation in solutions[solution]['mutations']:
if mutation:
realmut = 1
if not realmut:
continue
#
# Get the delta pKa value
#
key = str(dpKas[solution]['mutations'])
if dpKa_dict.has_key(key):
dpKas[solution][method] = dpKa_dict[key]
#
# Did we calculate dpKa values?
#
for solution in dpKas.keys():
if not dpKas[solution].has_key('mutations'):
#
# If it's not a real solution then don't check it
#
continue
#
# Now calculate the dpKas with the method we want, it it's needed
#
if method == 'phidiff':
methods = [[None, None]]
else:
methods = [[method, None]]
#
# Set the method name
#
for method, tabulated in methods:
method_name = method
if not method:
method_name = 'phidiff'
#
# Is this tabulated mode?
#
if tabulated:
method_name = method_name + '_tab'
#
# Did we calculate this dpKa?
#
if not dpKas[solution].has_key(
method_name) and not dpKas[solution].has_key(method_name +
'_tab'):
#
# Get defaults
#
import Design_accuracy
defaults = Design_accuracy.get_this_defaults(
pdbfile, target_residue, target_dpKa)
if method:
# Choose method
defaults[method][0] = 1
#
# Set tabulated
#
defaults['tabulated'][0] = tabulated
#
# Set the general parameters
#
mutations = dpKas[solution]['mutations']
import string
#
# Remove all None elements from mutations
#
realmut = None
filtered_muts = []
for mut in mutations:
if mut:
filtered_muts.append(mut)
realmut = 1
if not realmut:
continue
#
# Set the mutations variable in defaults
#
defaults['mutations'][0] = string.join(filtered_muts, ',')
# Calculate delta pkas
defaults['calc_dpka'][0] = 1
#
# Get the dpKa(s)
#
print 'Still calculating here'
tmp = Design_pKa.run_opt(defaults, X)
#
# We should have only one key
#
keys = tmp.keys()
if len(keys) != 1:
print keys
raise 'Too many keys when calculating dpkas for one solution'
added_data = 1
dpKas[solution][method_name] = tmp[keys[0]]
#
# Done
#
return dpKas, added_data, X
def get_solutions_dist_nummuts(pdbfile=None,
target_residue=None,
target_dpKa=2.0,
num_muts=6,
min_target_dist=5.0,
dpKas={},
method='MC',
pKMCsteps=0,
X=None):
#
# Check if we already did this..
#
#
# Do we have solutions for this problem?
#
added_data=None
missing=None
if dpKas=={}:
missing=1
#
# Do it?
#
if missing:
defaults=local_defaults(pdbfile,target_residue,target_dpKa,float(min_target_dist),pKMCsteps)
defaults['max_mutations'][0]=int(num_muts)
print 'Target: %s, dpKa: %s, Number of mutations: %3d, min_dist: %5.3f' %(
target_residue,target_dpKa,defaults['max_mutations'][0],defaults['min_target_dist'][0])
#
# Set the method default
#
allmethods=['TR','MC']
for m in allmethods:
defaults[m][0]=None
if not method=='phidiff':
defaults[method][0]=1
#
# Set the parameters
#
if not X:
params={}
for key in defaults.keys():
params[key]=defaults[key][0]
X=Design_pKa.Design_pKa(params)
#
# Get the solutions and the corresponding dpKas
#
solutions,dpKa_dict=Design_pKa.run_opt(defaults,X)
solution_keys=solutions.keys()
#
#
# If there are no solutions then store that info
#
if len(solution_keys)==0:
added_data=1
dpKas[1]={'no solutions':1}
#
# Loop over all the solutions and get the dpKas
#
for solution in solution_keys:
#
# Make sure the dictionary is ready
#
if not dpKas.has_key(solution):
added_data=1
dpKas[solution]={}
#
# Check that we have a real mutation (i.e. a non- None) mutation
#
dpKas[solution]['mutations']=solutions[solution]['mutations']
realmut=None
for mutation in solutions[solution]['mutations']:
if mutation:
realmut=1
if not realmut:
continue
#
# Get the delta pKa value
#
key=str(dpKas[solution]['mutations'])
if dpKa_dict.has_key(key):
dpKas[solution][method]=dpKa_dict[key]
#
# Did we calculate dpKa values?
#
for solution in dpKas.keys():
if not dpKas[solution].has_key('mutations'):
#
# If it's not a real solution then don't check it
#
continue
#
# Now calculate the dpKas with the method we want, it it's needed
#
if method=='phidiff':
methods=[[None,None]]
else:
methods=[[method,None]]
#
# Set the method name
#
for method,tabulated in methods:
method_name=method
if not method:
method_name='phidiff'
#
# Is this tabulated mode?
#
if tabulated:
method_name=method_name+'_tab'
#
# Did we calculate this dpKa?
#
if not dpKas[solution].has_key(method_name) and not dpKas[solution].has_key(method_name+'_tab'):
#
# Get defaults
#
import Design_accuracy
defaults=Design_accuracy.get_this_defaults(pdbfile,target_residue,target_dpKa)
if method:
# Choose method
defaults[method][0]=1
#
# Set tabulated
#
defaults['tabulated'][0]=tabulated
#
# Set the general parameters
#
mutations=dpKas[solution]['mutations']
import string
#
# Remove all None elements from mutations
#
realmut=None
filtered_muts=[]
for mut in mutations:
if mut:
filtered_muts.append(mut)
realmut=1
if not realmut:
continue
#
# Set the mutations variable in defaults
#
defaults['mutations'][0]=string.join(filtered_muts,',')
# Calculate delta pkas
defaults['calc_dpka'][0]=1
#
# Get the dpKa(s)
#
print 'Still calculating here'
tmp=Design_pKa.run_opt(defaults,X)
#
# We should have only one key
#
keys=tmp.keys()
if len(keys)!=1:
print keys
raise 'Too many keys when calculating dpkas for one solution'
added_data=1
dpKas[solution][method_name]=tmp[keys[0]]
#
# Done
#
return dpKas,added_data,X
def main(pdbfile=None):
#
# Calculate the matrix of pKa shifts [number of mutations:min distance from active site]
#
# Get the PDB file
#
print
print 'Construct dpKa matrix for a single design criterium'
print
print 'Usage: Design_two_targets <pdbfile> <database file> <design statement>'
print
#
# start the run
#
import sys, os
if len(sys.argv) < 4:
raise 'Incorrect usage'
if not pdbfile:
pdbfile = sys.argv[1]
if len(sys.argv) > 2:
dir = sys.argv[2]
else:
raise 'You have to provide a dir for the output'
os.chdir(dir)
#
# Get the method
#
method = 'MC'
#
# Which design are we doing?
#
design_statement = sys.argv[3]
#
# Set the file name
#
filename = os.path.join(
dir, 'designtwo__' +
os.path.split(pdbfile)[1]) + '_' + method + design_statement
#
# Print the filename of the dictionary
#
print 'Setting dictionary filename to', filename
DB = dictIO(filename)
print 'I am running in %s' % os.getcwd()
#
# Get wild type pKa values
#
import pKaTool.pKaIO
X = pKaTool.pKaIO.pKaIO(pdbfile)
if not X.assess_status():
import os
print 'You have to run a pKa calculation first'
raise Exception()
#
# OK, got the pKa calc. Read results
#
wt_pKas = X.readpka()
#
# See if we have an old database file we should work on
#
import os
if os.path.isfile(filename):
#
# Load old file
#
results = DB.load()
else:
#
# No, no old restuls
#
results = {}
#
# Add the PDB file
#
fd = open(pdbfile)
lines = fd.readlines()
fd.close()
results['pdbfile'] = lines
#
# Add the full wt pKa values
#
results['wt_full'] = wt_pKas
DB.save(results)
# ---------------------------------------
#
# Delete old files
#
import Design_pKa
Design_pKa.delete_old_files(pdbfile, 'N', 'N')
#
# Start looping
#
dist_range = range(1, 26)
num_muts_range = range(1, 21)
count = 0
#
# Set the number of MC steps
#
pKMCsteps = 200000
#
# Start of loop
#
#
# Loop over number of mutations and distance cut-off
#
X = None
if not results.has_key(design_statement):
results[design_statement] = {}
for min_target_dist in dist_range:
for num_muts in num_muts_range:
#
# Make sure the dictionary entries are there
#
if not results[design_statement].has_key(num_muts):
results[design_statement][num_muts] = {}
if not results[design_statement][num_muts].has_key(
min_target_dist):
results[design_statement][num_muts][min_target_dist] = {}
#
# Have we done this one yet?
#
x = 'Checking dist: %5.2f #muts: %2d....' % (
float(min_target_dist), num_muts)
print x,
if results[design_statement][num_muts][min_target_dist] == {}:
print 'not done. Designing solutions:'
#
# Get the parameters
#
defaults = set_parameters(pdbfile=pdbfile,
design_statement=design_statement,
min_dist=min_target_dist,
num_muts=num_muts)
params = {}
for key in defaults.keys():
params[key] = defaults[key][0]
#
# Call the design routine
#
if not X:
X = Design_pKa.Design_pKa(params)
solutions, dpKa_dict = Design_pKa.run_opt(defaults, X)
res = dpKa_dict.keys()
res.sort()
#for r in res:
# print r,dpKa_dict[r]
#
# Store the solutions
#
print 'Found these solutions'
print dpKa_dict.keys()
if dpKa_dict.keys() == []:
results[design_statement][num_muts][min_target_dist] = {
method: {
'None': 'No solutions'
}
}
else:
results[design_statement][num_muts][min_target_dist] = {
method: dpKa_dict.copy()
}
results = DB.update(results)
else:
print 'done.'
#
# All done
#
print
print 'All done - normal exit'
print
return
def main(pdbfile=None):
#
# Calculate the matrix of pKa shifts [number of mutations:min distance from active site]
#
# Get the PDB file
#
print
print 'Construct dpKa matrix for a single design criterium'
print
print 'Usage: Design_two_targets <pdbfile> <database file> <design statement>'
print
#
# start the run
#
import sys,os
if len(sys.argv)<4:
raise 'Incorrect usage'
if not pdbfile:
pdbfile=sys.argv[1]
if len(sys.argv)>2:
dir=sys.argv[2]
else:
raise 'You have to provide a dir for the output'
os.chdir(dir)
#
# Get the method
#
method='MC'
#
# Which design are we doing?
#
design_statement=sys.argv[3]
#
# Set the file name
#
filename=os.path.join(dir,'designtwo__'+os.path.split(pdbfile)[1])+'_'+method+design_statement
#
# Print the filename of the dictionary
#
print 'Setting dictionary filename to',filename
DB=dictIO(filename)
print 'I am running in %s' %os.getcwd()
#
# Get wild type pKa values
#
import pKaTool.pKaIO
X=pKaTool.pKaIO.pKaIO(pdbfile)
if not X.assess_status():
import os
print 'You have to run a pKa calculation first'
raise Exception()
#
# OK, got the pKa calc. Read results
#
wt_pKas=X.readpka()
#
# See if we have an old database file we should work on
#
import os
if os.path.isfile(filename):
#
# Load old file
#
results=DB.load()
else:
#
# No, no old restuls
#
results={}
#
# Add the PDB file
#
fd=open(pdbfile)
lines=fd.readlines()
fd.close()
results['pdbfile']=lines
#
# Add the full wt pKa values
#
results['wt_full']=wt_pKas
DB.save(results)
# ---------------------------------------
#
# Delete old files
#
import Design_pKa
Design_pKa.delete_old_files(pdbfile,'N','N')
#
# Start looping
#
dist_range=range(1,26)
num_muts_range=range(1,21)
count=0
#
# Set the number of MC steps
#
pKMCsteps=200000
#
# Start of loop
#
#
# Loop over number of mutations and distance cut-off
#
X=None
if not results.has_key(design_statement):
results[design_statement]={}
for min_target_dist in dist_range:
for num_muts in num_muts_range:
#
# Make sure the dictionary entries are there
#
if not results[design_statement].has_key(num_muts):
results[design_statement][num_muts]={}
if not results[design_statement][num_muts].has_key(min_target_dist):
results[design_statement][num_muts][min_target_dist]={}
#
# Have we done this one yet?
#
x='Checking dist: %5.2f #muts: %2d....' %(float(min_target_dist),num_muts)
print x,
if results[design_statement][num_muts][min_target_dist]=={}:
print 'not done. Designing solutions:'
#
# Get the parameters
#
defaults=set_parameters(pdbfile=pdbfile,
design_statement=design_statement,
min_dist=min_target_dist,
num_muts=num_muts)
params={}
for key in defaults.keys():
params[key]=defaults[key][0]
#
# Call the design routine
#
if not X:
X=Design_pKa.Design_pKa(params)
solutions,dpKa_dict=Design_pKa.run_opt(defaults,X)
res=dpKa_dict.keys()
res.sort()
#for r in res:
# print r,dpKa_dict[r]
#
# Store the solutions
#
print 'Found these solutions'
print dpKa_dict.keys()
if dpKa_dict.keys()==[]:
results[design_statement][num_muts][min_target_dist]={method:{'None':'No solutions'}}
else:
results[design_statement][num_muts][min_target_dist]={method:dpKa_dict.copy()}
results=DB.update(results)
else:
print 'done.'
#
# All done
#
print
print 'All done - normal exit'
print
return
