def printPKA(self):
"""
Prints the result for current configuration (determinants + summary)
"""
output.printPKASection(self)
def printPKA(self):
"""
Prints the result for current configuration (determinants + summary)
"""
output.printPKASection(self)
def calculateAveragePKA(self, version=None, options=None):
"""
Calculates the pKa values of each configuration and averages them
"""
# initializing dictionaries for making averages, key = 'residue.label'
pkas = {}
Nmass = {}
Emass = {}
Nlocl = {}
Elocl = {}
determinants = {}
number_of_configurations = len(self.configurations)
# setting up list for 'propka1 pka residues'
pka_residues = []
for resName in lib.residueList("propka1"):
pka_residues.extend(self.residue_dictionary[resName])
# initializing dictionaries for making averages, key = 'configuration label'
for residue in pka_residues:
key = residue.label
pkas[key] = {}
Nmass[key] = {}
Emass[key] = {}
Nlocl[key] = {}
Elocl[key] = {}
# initializing dictionary for making averages, key = 'determinant.label'
determinants[key] = [{}, {}, {}]
# perform pKa calulation on each configuration and add pKa properties to dictionaries
for key in self.configurations:
self.setConfiguration(key=key, options=options)
self.calculateConfigurationPKA(version=version, options=options)
# printing out the pKa section for this configuration
if options.verbose == True:
output.printPKASection(self)
# transferring property to right dictionary
for residue in pka_residues:
residue_key = residue.label
pkas[residue_key][key] = residue.pKa_pro
Nmass[residue_key][key] = residue.Nmass
Emass[residue_key][key] = residue.Emass
Nlocl[residue_key][key] = residue.Nlocl
Elocl[residue_key][key] = residue.Elocl
for type in range(3):
for determinant in residue.determinants[type]:
if determinant.label in determinants[residue_key][type]:
determinants[residue_key][type][determinant.label] += determinant.value
else:
determinants[residue_key][type][determinant.label] = determinant.value
# print each configuration-pKa in nice matrix
residue_list = self.residue_dictionary["ASP"]
residue_list.extend(self.residue_dictionary["GLU"])
str = "%4s" % ("Res#")
for residue in residue_list:
str += "%6d" % (residue.resNumb)
pka_print(str)
index = 0
for key in self.configurations:
str = "%4s:" % (key)
for residue in residue_list:
reskey = residue.label
str += "%6.2lf" % (pkas[reskey][key])
#str += "%6.2lf" % (Emass[reskey][key])
#str += "%6.2lf" % (Elocl[reskey][key])
pka_print(str)
# get the average pKa properties by dividing the sum with number of configurations, len(configuration keys)
from determinants import Determinant
for residue in pka_residues:
residue_key = residue.label
sum_pka = 0.00; sum_Nmass = 0.00; sum_Emass = 0.00; sum_Nlocl = 0.00; sum_Elocl = 0.00
for key in pkas[residue_key].keys():
sum_pka += pkas[residue_key][key]
sum_Nmass += Nmass[residue_key][key]
sum_Emass += Emass[residue_key][key]
sum_Nlocl += Nlocl[residue_key][key]
sum_Elocl += Elocl[residue_key][key]
residue.pKa_pro = sum_pka/len( pkas[residue_key].keys() )
residue.Nmass = sum_Nmass/len( pkas[residue_key].keys() )
residue.Emass = sum_Emass/len( pkas[residue_key].keys() )
residue.Nlocl = sum_Nlocl/len( pkas[residue_key].keys() )
residue.Elocl = sum_Elocl/len( pkas[residue_key].keys() )
residue.determinants = [[], [], []]
for type in range(3):
for key in determinants[residue_key][type].keys():
value = determinants[residue_key][type][key] / len( pkas[residue_key].keys() )
if abs(value) > 0.005: # <-- removing determinant that appears as 0.00
newDeterminant = Determinant(key, value)
residue.determinants[type].append(newDeterminant)
return
def calculateAveragePKA(self, version=None, options=None):
"""
Calculates the pKa values of each configuration and averages them
"""
# initializing dictionaries for making averages, key = 'residue.label'
pkas = {}
Nmass = {}
Emass = {}
Nlocl = {}
Elocl = {}
determinants = {}
number_of_configurations = len(self.configurations)
# setting up list for 'propka1 pka residues'
pka_residues = []
for resName in lib.residueList("propka1"):
pka_residues.extend(self.residue_dictionary[resName])
# initializing dictionaries for making averages, key = 'configuration label'
for residue in pka_residues:
key = residue.label
pkas[key] = {}
Nmass[key] = {}
Emass[key] = {}
Nlocl[key] = {}
Elocl[key] = {}
# initializing dictionary for making averages, key = 'determinant.label'
determinants[key] = [{}, {}, {}]
# perform pKa calulation on each configuration and add pKa properties to dictionaries
for key in self.configurations:
self.setConfiguration(key=key, options=options)
self.calculateConfigurationPKA(version=version, options=options)
# printing out the pKa section for this configuration
if options.verbose == True:
output.printPKASection(self)
# transferring property to right dictionary
for residue in pka_residues:
residue_key = residue.label
pkas[residue_key][key] = residue.pKa_pro
Nmass[residue_key][key] = residue.Nmass
Emass[residue_key][key] = residue.Emass
Nlocl[residue_key][key] = residue.Nlocl
Elocl[residue_key][key] = residue.Elocl
for type in range(3):
for determinant in residue.determinants[type]:
if determinant.label in determinants[residue_key][
type]:
determinants[residue_key][type][
determinant.label] += determinant.value
else:
determinants[residue_key][type][
determinant.label] = determinant.value
# print each configuration-pKa in nice matrix
residue_list = self.residue_dictionary["ASP"]
residue_list.extend(self.residue_dictionary["GLU"])
str = "%4s" % ("Res#")
for residue in residue_list:
str += "%6d" % (residue.resNumb)
pka_print(str)
index = 0
for key in self.configurations:
str = "%4s:" % (key)
for residue in residue_list:
reskey = residue.label
str += "%6.2lf" % (pkas[reskey][key])
#str += "%6.2lf" % (Emass[reskey][key])
#str += "%6.2lf" % (Elocl[reskey][key])
pka_print(str)
# get the average pKa properties by dividing the sum with number of configurations, len(configuration keys)
from determinants import Determinant
for residue in pka_residues:
residue_key = residue.label
sum_pka = 0.00
sum_Nmass = 0.00
sum_Emass = 0.00
sum_Nlocl = 0.00
sum_Elocl = 0.00
for key in pkas[residue_key].keys():
sum_pka += pkas[residue_key][key]
sum_Nmass += Nmass[residue_key][key]
sum_Emass += Emass[residue_key][key]
sum_Nlocl += Nlocl[residue_key][key]
sum_Elocl += Elocl[residue_key][key]
residue.pKa_pro = sum_pka / len(pkas[residue_key].keys())
residue.Nmass = sum_Nmass / len(pkas[residue_key].keys())
residue.Emass = sum_Emass / len(pkas[residue_key].keys())
residue.Nlocl = sum_Nlocl / len(pkas[residue_key].keys())
residue.Elocl = sum_Elocl / len(pkas[residue_key].keys())
residue.determinants = [[], [], []]
for type in range(3):
for key in determinants[residue_key][type].keys():
value = determinants[residue_key][type][key] / len(
pkas[residue_key].keys())
if abs(
value
) > 0.005: # <-- removing determinant that appears as 0.00
newDeterminant = Determinant(key, value)
residue.determinants[type].append(newDeterminant)
return
