323 lines
13 KiB
Python
323 lines
13 KiB
Python
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from __future__ import division
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from __future__ import print_function
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import math, propka.output, propka.group, propka.lib, itertools
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from propka.lib import info, warning
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class non_covalently_couple_groups:
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def __init__(self):
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self.parameters = None
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# self.do_intrinsic = False
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# self.do_pair_wise = False
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self.do_prot_stat = True
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return
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#
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# Methods for finding coupled groups
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#
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def is_coupled_protonation_state_probability(self, group1, group2, energy_method, return_on_fail=True):
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# check if the interaction energy is high enough
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interaction_energy = max(self.get_interaction(group1,group2), self.get_interaction(group2,group1))
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if interaction_energy<=self.parameters.min_interaction_energy and return_on_fail:
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return {'coupling_factor':-1.0}
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# calculate intrinsic pKa's, if not already done
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for group in [group1, group2]:
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if not hasattr(group, 'intrinsic_pKa'):
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group.calculate_intrinsic_pka()
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use_pH = self.parameters.pH
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if self.parameters.pH == 'variable':
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use_pH = min(group1.pka_value, group2.pka_value)
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default_energy = energy_method(pH=use_pH, reference=self.parameters.reference)
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default_pka1 = group1.pka_value
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default_pka2 = group2.pka_value
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# check that pka values are within relevant limits
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if max(default_pka1, default_pka2) < self.parameters.min_pka or \
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min(default_pka1, default_pka2) > self.parameters.max_pka:
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if return_on_fail:
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return {'coupling_factor':-1.0}
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# Swap interactions and re-calculate pKa values
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self.swap_interactions([group1], [group2])
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group1.calculate_total_pka()
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group2.calculate_total_pka()
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# store swapped energy and pka's
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swapped_energy = energy_method(pH=use_pH, reference=self.parameters.reference)
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swapped_pka1 = group1.pka_value
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swapped_pka2 = group2.pka_value
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pka_shift1 = swapped_pka1 - default_pka1
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pka_shift2 = swapped_pka2 - default_pka2
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# Swap back to original protonation state
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self.swap_interactions([group1], [group2])
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group1.calculate_total_pka()
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group2.calculate_total_pka()
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# check difference in free energy
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if abs(default_energy - swapped_energy) > self.parameters.max_free_energy_diff and return_on_fail:
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return {'coupling_factor':-1.0}
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# check pka shift
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if max(abs(pka_shift1), abs(pka_shift2)) < self.parameters.min_swap_pka_shift and return_on_fail:
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return {'coupling_factor':-1.0}
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# check intrinsic pka diff
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if abs(group1.intrinsic_pKa - group2.intrinsic_pKa) > self.parameters.max_intrinsic_pKa_diff and return_on_fail:
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return {'coupling_factor':-1.0}
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# if everything is OK, calculate the coupling factor and return all info
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factor = self.get_free_energy_diff_factor(default_energy, swapped_energy)*\
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self.get_pka_diff_factor(group1.intrinsic_pKa, group2.intrinsic_pKa)*\
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self.get_interaction_factor(interaction_energy)
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return {'coupling_factor':factor,
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'default_energy':default_energy,
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'swapped_energy':swapped_energy,
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'interaction_energy':interaction_energy,
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'swapped_pka1':swapped_pka1,
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'swapped_pka2':swapped_pka2,
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'pka_shift1':pka_shift1,
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'pka_shift2':pka_shift2,
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'pH':use_pH}
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#
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# Methods for calculating the coupling factor
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#
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def get_pka_diff_factor(self, pka1, pka2):
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intrinsic_pka_diff = abs(pka1-pka2)
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res = 0.0
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if intrinsic_pka_diff <= self.parameters.max_intrinsic_pKa_diff:
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res = 1-(intrinsic_pka_diff/self.parameters.max_intrinsic_pKa_diff)**2
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return res
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def get_free_energy_diff_factor(self, energy1, energy2):
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free_energy_diff = abs(energy1-energy2)
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res = 0.0
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if free_energy_diff <= self.parameters.max_free_energy_diff:
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res = 1-(free_energy_diff/self.parameters.max_free_energy_diff)**2
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return res
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def get_interaction_factor(self, interaction_energy):
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res = 0.0
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interaction_energy = abs(interaction_energy)
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if interaction_energy >= self.parameters.min_interaction_energy:
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res = (interaction_energy-self.parameters.min_interaction_energy)/(1.0+interaction_energy-self.parameters.min_interaction_energy)
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return res
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def identify_non_covalently_coupled_groups(self, conformation, verbose=True):
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""" Finds coupled residues in protein """
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self.parameters = conformation.parameters
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if verbose:
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info('')
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info(' Warning: When using the -d option, pKa values based on \'swapped\' interactions')
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info(' will be writting to the output .pka file')
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info('')
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info('-' * 103)
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info(' Detecting non-covalently coupled residues')
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info('-' * 103)
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info(' Maximum pKa difference: %4.2f pKa units' % self.parameters.max_intrinsic_pKa_diff)
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info(' Minimum interaction energy: %4.2f pKa units' % self.parameters.min_interaction_energy)
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info(' Maximum free energy diff.: %4.2f pKa units' % self.parameters.max_free_energy_diff)
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info(' Minimum swap pKa shift: %4.2f pKa units' % self.parameters.min_swap_pka_shift)
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info(' pH: %6s ' % str(self.parameters.pH))
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info(' Reference: %s' % self.parameters.reference)
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info(' Min pKa: %4.2f' % self.parameters.min_pka)
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info(' Max pKa: %4.2f' % self.parameters.max_pka)
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info('')
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# find coupled residues
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titratable_groups = conformation.get_titratable_groups()
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if not conformation.non_covalently_coupled_groups:
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for g1 in titratable_groups:
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for g2 in titratable_groups:
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if g1==g2:
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break
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if not g1 in g2.non_covalently_coupled_groups and self.do_prot_stat:
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data = self.is_coupled_protonation_state_probability(g1, g2,conformation.calculate_folding_energy)
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if data['coupling_factor'] >0.0:
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g1.couple_non_covalently(g2)
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if verbose:
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self.print_out_swaps(conformation)
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return
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def print_out_swaps(self, conformation, verbose=True):
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map = propka.output.make_interaction_map('Non-covalent coupling map for %s'%conformation,
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conformation.get_non_covalently_coupled_groups(),
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lambda g1,g2: g1 in g2.non_covalently_coupled_groups)
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info(map)
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for system in conformation.get_coupled_systems(conformation.get_non_covalently_coupled_groups(),propka.group.Group.get_non_covalently_coupled_groups):
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self.print_system(conformation, list(system))
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return
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def print_system(self, conformation, system):
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info('System containing %d groups:' % len(system))
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# make list of interactions withi this system
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interactions = list(itertools.combinations(system,2))
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# print out coupling info for each interaction
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coup_info = ''
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for interaction in interactions:
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data = self.is_coupled_protonation_state_probability(interaction[0], interaction[1],conformation.calculate_folding_energy, return_on_fail=False)
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coup_info += self.make_data_to_string(data,interaction[0],interaction[1])+'\n\n'
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info(coup_info)
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# make list of possible combinations of swap to try out
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combinations = propka.lib.generate_combinations(interactions)
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# Make possible swap combinations
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swap_info = ''
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swap_info += self.print_determinants_section(system,'Original')
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for combination in combinations:
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# Tell the user what is swap in this combination
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swap_info += 'Swapping the following interactions:\n'
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for interaction in combination:
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swap_info += ' %s %s\n'%(interaction[0].label, interaction[1].label)
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# swap...
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for interaction in combination:
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self.swap_interactions([interaction[0]],[interaction[1]])
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swap_info += self.print_determinants_section(system,'Swapped')
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# ...and swap back
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#for interaction in combination:
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# self.swap_interactions([interaction[0]], [interaction[1]])
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info(swap_info)
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return
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#
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# Interaction and swapping methods
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#
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def get_interaction(self, group1, group2, include_side_chain_hbs = True):
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determinants = group1.determinants['coulomb']
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if include_side_chain_hbs:
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determinants = group1.determinants['sidechain'] + group1.determinants['coulomb']
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interaction_energy = 0.0
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for det in determinants:
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if group2 == det.group:
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interaction_energy += det.value
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return interaction_energy
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def print_determinants_section(self, system, tag):
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all_labels = [g.label for g in system]
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s = ' '+'-'*113+'\n'
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for group in system:
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s += self.tagged_format(' %-8s|' % tag, group.getDeterminantString(), all_labels)
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return s+'\n'
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def swap_interactions(self, groups1, groups2, include_side_chain_hbs = True):
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for i in range(len(groups1)):
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group1 = groups1[i]
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group2 = groups2[i]
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# swap the interactions!
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self.transfer_determinant(group1.determinants['coulomb'], group2.determinants['coulomb'], group1.label, group2.label)
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if include_side_chain_hbs:
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self.transfer_determinant(group1.determinants['sidechain'], group2.determinants['sidechain'], group1.label, group2.label)
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# re-calculate pKa values
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group1.calculate_total_pka()
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group2.calculate_total_pka()
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return
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def transfer_determinant(self, determinants1, determinants2, label1, label2):
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# find out what to transfer...
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from1to2 = []
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from2to1 = []
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for det in determinants1:
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if det.label == label2:
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from1to2.append(det)
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for det in determinants2:
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if det.label == label1:
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from2to1.append(det)
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# ...and transfer it!
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for det in from1to2:
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det.label = label1
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determinants2.append(det)
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determinants1.remove(det)
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for det in from2to1:
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det.label = label2
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determinants1.append(det)
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determinants2.remove(det)
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return
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#
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# Output methods
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#
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def tagged_format(self, tag, s, labels):
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s = "%s %s"%(tag,s)
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s = s.replace('\n','\n%s '%tag)
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for label in labels:
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s = s.replace(label, '\033[31m%s\033[30m'%label)
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return s+'\n'
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def make_data_to_string(self, data, group1, group2):
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s = """ %s and %s coupled (prot.state): %5.2f
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Energy levels: %6.2f, %6.2f (difference: %6.2f) at pH %6.2f
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Interaction energy: %6.2f
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Intrinsic pka's: %6.2f, %6.2f (difference: %6.2f)
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Swapped pKa's: %6.2f, %6.2f (difference: %6.2f, %6.2f)"""%(group1.label,
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group2.label,
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data['coupling_factor'],
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data['default_energy'], data['swapped_energy'],
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data['default_energy'] - data['swapped_energy'],
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data['pH'],
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data['interaction_energy'],
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group1.intrinsic_pKa,
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group2.intrinsic_pKa,
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group1.intrinsic_pKa-group2.intrinsic_pKa,
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data['swapped_pka1'],
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data['swapped_pka2'],
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data['pka_shift1'],
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data['pka_shift2'])
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return s
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nccg = non_covalently_couple_groups()
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