248 lines
9.9 KiB
Python
Executable File
248 lines
9.9 KiB
Python
Executable File
#!/usr/bin/python
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#
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# Molecular container for storing all contents of pdb files
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#
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#
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import os, Source.pdb, sys, Source.version, Source.output, Source.conformation_container, Source.group, Source.lib
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class Molecular_container:
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def __init__(self, input_file, options=None):
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# printing out header before parsing input
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Source.output.printHeader()
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# set up some values
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self.options = options
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self.input_file = input_file
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self.dir = os.path.split(input_file)[0]
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self.file = os.path.split(input_file)[1]
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self.name = self.file[0:self.file.rfind('.')]
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input_file_extension = input_file[input_file.rfind('.'):]
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# set the version
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if options:
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parameters = Source.parameters.Parameters(self.options.parameters)
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else:
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parameters = Source.parameters.Parameters('propka.cfg')
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try:
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exec('self.version = Source.version.%s(parameters)'%parameters.version)
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except:
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raise Exception('Error: Version %s does not exist'%parameters.version)
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# read the input file
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if input_file_extension[0:4] == '.pdb':
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# input is a pdb file
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# read in atoms and top up containers to make sure that all atoms are present in all conformations
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[self.conformations, self.conformation_names] = Source.pdb.read_pdb(input_file, self.version.parameters,self)
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if len(self.conformations)==0:
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print('Error: The pdb file does not seems to contain any molecular conformations')
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sys.exit(-1)
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self.top_up_conformations()
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# make a structure precheck
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Source.pdb.protein_precheck(self.conformations, self.conformation_names)
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# set up atom bonding and protonation
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self.version.setup_bonding_and_protonation(self)
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# Extract groups
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self.extract_groups()
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# sort atoms
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for name in self.conformation_names:
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self.conformations[name].sort_atoms()
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# find coupled groups
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self.find_covalently_coupled_groups()
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# write out the input file
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filename = self.file.replace(input_file_extension,'.propka_input')
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Source.pdb.write_input(self, filename)
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elif input_file_extension == '.propka_input':
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#input is a propka_input file
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[self.conformations, self.conformation_names] = Source.pdb.read_input(input_file, self.version.parameters, self)
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# Extract groups - this merely sets up the groups found in the input file
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self.extract_groups()
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# do some additional set up
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self.additional_setup_when_reading_input_file()
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else:
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print('Unrecognized input file:%s'%input_file)
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sys.exit(-1)
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return
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def top_up_conformations(self):
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""" Makes sure that all atoms are present in all conformations """
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for name in self.conformation_names:
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if name!='1A' and (len(self.conformations[name]) < len(self.conformations['1A'])):
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self.conformations[name].top_up(self.conformations['1A'])
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return
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def find_covalently_coupled_groups(self):
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print('-'*103)
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for name in self.conformation_names:
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self.conformations[name].find_covalently_coupled_groups()
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return
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def find_non_covalently_coupled_groups(self):
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print('-'*103)
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for name in self.conformation_names:
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self.conformations[name].find_non_covalently_coupled_groups(verbose=self.options.display_coupled_residues)
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return
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def extract_groups(self):
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""" Identify the groups needed for pKa calculation """
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for name in self.conformation_names:
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self.conformations[name].extract_groups()
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return
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def additional_setup_when_reading_input_file(self):
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for name in self.conformation_names:
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self.conformations[name].additional_setup_when_reading_input_file()
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return
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def calculate_pka(self):
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# calculate for each conformation
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for name in self.conformation_names:
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self.conformations[name].calculate_pka(self.version, self.options)
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# find non-covalently coupled groups
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self.find_non_covalently_coupled_groups()
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# find the average of the conformations
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self.average_of_conformations()
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# print out the conformation-average results
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Source.output.printResult(self, 'AVR', self.version.parameters)
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return
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def average_of_conformations(self):
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# make a new configuration to hold the average values
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avr_conformation = Source.conformation_container.Conformation_container(name='average',
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parameters=self.conformations[self.conformation_names[0]].parameters,
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molecular_container=self)
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for group in self.conformations[self.conformation_names[0]].get_titratable_groups_and_cysteine_bridges():
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# new group to hold average values
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avr_group = group.clone()
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# sum up all groups ...
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for name in self.conformation_names:
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group_to_add = self.conformations[name].find_group(group)
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if group_to_add:
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avr_group += group_to_add
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else:
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print('Warning: Group %s could not be found in conformation %s.'%(group.atom.residue_label, name))
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# ... and store the average value
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avr_group = avr_group / len(self.conformation_names)
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avr_conformation.groups.append(avr_group)
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# store information on coupling in the average container
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if len(list(filter(lambda c: c.non_covalently_coupled_groups, self.conformations.values()))):
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avr_conformation.non_covalently_coupled_groups = True
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# store chain info
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avr_conformation.chains = self.conformations[self.conformation_names[0]].chains
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self.conformations['AVR'] = avr_conformation
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return
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def write_pka(self, filename=None, reference="neutral", direction="folding", options=None):
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#for name in self.conformation_names:
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# Source.output.writePKA(self, self.version.parameters, filename='%s_3.1_%s.pka'%(self.name, name),
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# conformation=name,reference=reference,
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# direction=direction, options=options)
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# write out the average conformation
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filename=os.path.join('%s.pka'%(self.name))
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# if the display_coupled_residues option is true,
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# write the results out to an alternative pka file
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if self.options.display_coupled_residues:
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filename=os.path.join('%s_alt_state.pka'%(self.name))
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if hasattr(self.version.parameters, 'output_file_tag') and len(self.version.parameters.output_file_tag)>0:
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filename=os.path.join('%s_%s.pka'%(self.name,self.version.parameters.output_file_tag))
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Source.output.writePKA(self, self.version.parameters, filename=filename,
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conformation='AVR',reference=reference,
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direction=direction, options=options)
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return
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def getFoldingProfile(self, conformation='AVR',reference="neutral", direction="folding", grid=[0., 14., 0.1], options=None):
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# calculate stability profile
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profile = []
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for ph in Source.lib.make_grid(*grid):
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ddg = self.conformations[conformation].calculate_folding_energy( pH=ph, reference=reference)
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#print(ph,ddg)
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profile.append([ph, ddg])
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# find optimum
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opt =[None, 1e6]
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for point in profile:
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opt = min(opt, point, key=lambda v:v[1])
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# find values within 80 % of optimum
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range_80pct = [None, None]
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values_within_80pct = [p[0] for p in profile if p[1]< 0.8*opt[1]]
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if len(values_within_80pct)>0:
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range_80pct = [min(values_within_80pct), max(values_within_80pct)]
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# find stability range
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stability_range = [None, None]
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stable_values = [p[0] for p in profile if p[1]< 0.0]
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if len(stable_values)>0:
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stability_range = [min(stable_values), max(stable_values)]
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return profile, opt, range_80pct, stability_range
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def getChargeProfile(self, conformation='AVR', grid=[0., 14., .1]):
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charge_profile = []
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for ph in Source.lib.make_grid(*grid):
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q_unfolded, q_folded = self.conformations[conformation].calculate_charge(self.version.parameters, pH=ph)
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charge_profile.append([ph, q_unfolded, q_folded])
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return charge_profile
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def getPI(self, conformation='AVR', grid=[0., 14., 1], iteration=0):
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#print('staring',grid, iteration)
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# search
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charge_profile = self.getChargeProfile(conformation=conformation, grid=grid)
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pi = []
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pi_folded = pi_unfolded = [None, 1e6,1e6]
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for point in charge_profile:
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pi_folded = min(pi_folded, point, key=lambda v:abs(v[2]))
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pi_unfolded = min(pi_unfolded, point, key=lambda v:abs(v[1]))
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# If results are not good enough, do it again with a higher sampling resolution
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pi_folded_value = pi_folded[0]
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pi_unfolded_value = pi_unfolded[0]
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step = grid[2]
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if (pi_folded[2] > 0.01 or pi_unfolded[1] > 0.01) and iteration<4:
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pi_folded_value, x = self.getPI(conformation=conformation, grid=[pi_folded[0]-step, pi_folded[0]+step, step/10.0], iteration=iteration+1)
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x, pi_unfolded_value = self.getPI(conformation=conformation, grid=[pi_unfolded[0]-step, pi_unfolded[0]+step, step/10.0], iteration=iteration+1)
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return pi_folded_value, pi_unfolded_value
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if __name__ == '__main__':
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input_file = sys.argv[1]
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mc = Molecular_container(input_file)
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