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De-lint conformation_container.py.

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Some public methods/members were changed.  These were checked against
Google for obvious use in other packages.
This commit is contained in:
Nathan Baker
2020-05-23 17:22:18 -07:00
parent 5afc5d511a
commit 83c54ec153
6 changed files with 387 additions and 249 deletions
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2
propka/calculations.py
View File

@@ -680,7 +680,7 @@ def backbone_reorganization(parameters, conformation):
conformation: specific molecule conformation
"""
titratable_groups = conformation.get_backbone_reorganisation_groups()
bbc_groups = conformation.get_backbone_CO_groups()
bbc_groups = conformation.get_backbone_co_groups()
for titratable_group in titratable_groups:
weight = titratable_group.buried

600
propka/conformation_container.py
View File

@@ -1,451 +1,589 @@
#
# Container for molecular conformations
#
"""Container for molecular conformations"""
import functools
import propka.ligand
from propka.output import make_interaction_map
from propka.determinant import Determinant
from propka.coupled_groups import nccg
from propka.determinants import setBackBoneDeterminants, setIonDeterminants
from propka.determinants import setDeterminants
from propka.group import Group, is_group
from propka.lib import info
from __future__ import division
from __future__ import print_function
import propka.group, propka.determinants, propka.determinant, propka.ligand, propka.output, propka.coupled_groups, functools
from propka.lib import info, warning
# A large number that gets multipled with the integer obtained from applying
# ord() to the atom chain ID. Used in calculating atom keys for sorting.
UNICODE_MULTIPLIER = 1e7
# A number that gets mutiplied with an atom's residue number. Used in
# calculating keys for atom sorting.
RESIDUE_MULTIPLIER = 1000
class ConformationContainer:
"""Container for molecular conformations"""
class Conformation_container:
def __init__(self, name='', parameters=None, molecular_container=None):
"""Initialize conformation container.
Args:
name: name for conformation
parameters: parmameters for conformation
molecular_container: container for molecule
"""
self.molecular_container = molecular_container
self.name=name
self.parameters=parameters
self.name = name
self.parameters = parameters
self.atoms = []
self.groups = []
self.chains = []
self.current_iter_item = 0
# TODO - what is marvin_pkas_calculated?
self.marvin_pkas_calculated = False
self.non_covalently_coupled_groups = False
return
#
# Group related methods
#
def extract_groups(self):
""" Generates at list of molecular groups needed for calculating pKa values """
"""Generate molecular groups needed for calculating pKa values."""
for atom in self.get_non_hydrogen_atoms():
# has this atom been checked for groups?
if atom.groups_extracted == 0:
group = propka.group.is_group(self.parameters, atom)
group = is_group(self.parameters, atom)
else:
group = atom.group
# if the atom has been checked in a another conformation, check if it has a
# group that should be used in this conformation as well
# if the atom has been checked in a another conformation, check
# if it has a group that should be used in this conformation as well
if group:
self.setup_and_add_group(group)
return
def additional_setup_when_reading_input_file(self):
# if a group is coupled and we are reading a .propka_input file,
"""Generate interaction map and charge centers."""
# if a group is coupled and we are reading a .propka_input file, then
# some more configuration might be needed
# print coupling map
map = propka.output.make_interaction_map('Covalent coupling map for %s'%self,
self.get_covalently_coupled_groups(),
lambda g1,g2: g1 in g2.covalently_coupled_groups)
info(map)
map_ = make_interaction_map('Covalent coupling map for %s' % self,
self.get_covalently_coupled_groups(),
lambda g1, g2: g1 in g2.covalently_coupled_groups)
info(map_)
# check if we should set a common charge centre as well
if self.parameters.common_charge_centre:
self.set_common_charge_centres()
return
def set_common_charge_centres(self):
for system in self.get_coupled_systems(self.get_covalently_coupled_groups(), propka.group.Group.get_covalently_coupled_groups):
"""Assign charge centers to groups."""
for system in self.get_coupled_systems(self.get_covalently_coupled_groups(),
Group.get_covalently_coupled_groups):
# make a list of the charge centre coordinates
all_coordinates = list(map(lambda g: [g.x, g.y, g.z], system))
# find the common charge center
ccc = functools.reduce(lambda g1,g2: [g1[0]+g2[0], g1[1]+g2[1], g1[2]+g2[2]], all_coordinates)
ccc = functools.reduce(lambda g1, g2: [g1[0]+g2[0], g1[1]+g2[1],
g1[2]+g2[2]],
all_coordinates)
ccc = list(map(lambda c: c/len(system), ccc))
# set the ccc for all coupled groups in this system
for g in system:
[g.x, g.y, g.z] = ccc
g.common_charge_centre = True
return
for group in system:
[group.x, group.y, group.z] = ccc
group.common_charge_centre = True
def find_covalently_coupled_groups(self):
""" Finds covalently coupled groups and sets common charge centres if needed """
"""Find covalently coupled groups and set common charge centres."""
for group in self.get_titratable_groups():
# Find covalently bonded groups
bonded_groups = self.find_bonded_titratable_groups(group.atom, 1, group.atom)
# couple groups
for cg in bonded_groups:
if cg in group.covalently_coupled_groups:
bonded_groups = self.find_bonded_titratable_groups(group.atom, 1,
group.atom)
# coupled groups
for bond_group in bonded_groups:
if bond_group in group.covalently_coupled_groups:
continue
if cg.atom.sybyl_type == group.atom.sybyl_type:
group.couple_covalently(cg)
if bond_group.atom.sybyl_type == group.atom.sybyl_type:
group.couple_covalently(bond_group)
# check if we should set a common charge centre as well
if self.parameters.common_charge_centre:
self.set_common_charge_centres()
# print coupling map
map = propka.output.make_interaction_map('Covalent coupling map for %s'%self,
#self.get_titratable_groups(),
self.get_covalently_coupled_groups(),
lambda g1,g2: g1 in g2.covalently_coupled_groups)
info(map)
return
map_ = make_interaction_map('Covalent coupling map for %s' % self,
self.get_covalently_coupled_groups(),
lambda g1, g2: g1 in g2.covalently_coupled_groups)
info(map_)
def find_non_covalently_coupled_groups(self, verbose=False):
"""Find non-covalently coupled groups and set common charge centres.
Args:
verbose: verbose output
"""
# check if non-covalent coupling has already been set up in an input file
if len(list(filter(lambda g: len(g.non_covalently_coupled_groups)>0, self.get_titratable_groups())))>0:
if len(list(filter(lambda g: len(g.non_covalently_coupled_groups) > 0,
self.get_titratable_groups()))) > 0:
self.non_covalently_coupled_groups = True
propka.coupled_groups.nccg.identify_non_covalently_coupled_groups(self,verbose=verbose)
nccg.identify_non_covalently_coupled_groups(self, verbose=verbose)
# re-do the check
if len(list(filter(lambda g: len(g.non_covalently_coupled_groups)>0, self.get_titratable_groups())))>0:
if len(list(filter(lambda g: len(g.non_covalently_coupled_groups) > 0,
self.get_titratable_groups()))) > 0:
self.non_covalently_coupled_groups = True
return
def find_bonded_titratable_groups(self, atom, num_bonds, original_atom):
"""Find bonded titrable groups.
def find_bonded_titratable_groups(self, atom, no_bonds, original_atom):
Args:
atom: atom to check for bonds
num_bonds: number of bonds for coupling
original_atom: another atom to check for bonds
Returns:
a set of bonded atom groups
"""
res = set()
for ba in atom.bonded_atoms:
for bond_atom in atom.bonded_atoms:
# skip the original atom
if ba == original_atom:
if bond_atom == original_atom:
continue
# check if this atom has a titratable group
if ba.group and ba.group.titratable and no_bonds <= self.parameters.coupling_max_number_of_bonds:
res.add(ba.group)
if bond_atom.group and bond_atom.group.titratable \
and num_bonds <= self.parameters.coupling_max_number_of_bonds:
res.add(bond_atom.group)
# check for titratable groups bonded to this atom
if no_bonds < self.parameters.coupling_max_number_of_bonds:
res |= self.find_bonded_titratable_groups(ba,no_bonds+1, original_atom)
if num_bonds < self.parameters.coupling_max_number_of_bonds:
res |= self.find_bonded_titratable_groups(bond_atom,
num_bonds+1,
original_atom)
return res
def setup_and_add_group(self, group):
""" Checks if we want to include this group in the calculations """
"""Check if we want to include this group in the calculations.
Args:
group: group to check
"""
# Is it recognized as a group at all?
if not group:
return
# Other checks (include ligands, which chains etc.)
# if all ok, accept the group
self.init_group(group)
self.groups.append(group)
def init_group(self, group):
"""
Initialize the given Group object.
"""Initialize the given Group object.
Args:
group: group object to initialize
"""
# set up the group
group.parameters=self.parameters
group.parameters = self.parameters
group.setup()
# If --titrate_only option is set, make non-specified residues un-titratable:
# If --titrate_only option is set, make non-specified residues
# un-titratable:
titrate_only = self.molecular_container.options.titrate_only
if titrate_only is not None:
at = group.atom
if not (at.chain_id, at.res_num, at.icode) in titrate_only:
atom = group.atom
if not (atom.chain_id, atom.res_num, atom.icode) in titrate_only:
group.titratable = False
if group.residue_type == 'CYS':
group.exclude_cys_from_results = True
#
# pka calculation methods
#
def calculate_pka(self, version, options):
"""Calculate pKas for conformation container.
Args:
version: version object
options: option object
"""
info('\nCalculating pKas for', self)
# calculate desolvation
for group in self.get_titratable_groups()+self.get_ions():
for group in self.get_titratable_groups() + self.get_ions():
version.calculate_desolvation(group)
# calculate backbone interactions
propka.determinants.setBackBoneDeterminants(self.get_titratable_groups(), self.get_backbone_groups(), version)
setBackBoneDeterminants(self.get_titratable_groups(),
self.get_backbone_groups(), version)
# setting ion determinants
propka.determinants.setIonDeterminants(self, version)
setIonDeterminants(self, version)
# calculating the back-bone reorganization/desolvation term
version.calculatebackbone_reorganization(self)
# setting remaining non-iterative and iterative side-chain & Coulomb interaction determinants
propka.determinants.setDeterminants(self.get_sidechain_groups(), version=version, options=options)
# setting remaining non-iterative and iterative side-chain & Coulomb
# interaction determinants
setDeterminants(self.get_sidechain_groups(), version=version,
options=options)
# calculating the total pKa values
for group in self.groups: group.calculate_total_pka()
for group in self.groups:
group.calculate_total_pka()
# take coupling effects into account
penalised_labels = self.coupling_effects()
if self.parameters.remove_penalised_group and len(penalised_labels)>0:
if self.parameters.remove_penalised_group and len(penalised_labels) > 0:
info('Removing penalised groups!!!')
for g in self.get_titratable_groups():
g.remove_determinants(penalised_labels)
for group in self.get_titratable_groups():
group.remove_determinants(penalised_labels)
# re-calculating the total pKa values
for group in self.groups: group.calculate_total_pka()
return
for group in self.groups:
group.calculate_total_pka()
def coupling_effects(self):
#
# Bases: The group with the highest pKa (the most stable one in the
# charged form) will be the first one to adopt a proton as pH
# is lowered and this group is allowed to titrate.
# The remaining groups are penalised
#
# Acids: The group with the highest pKa (the least stable one in the
# charged form) will be the last group to loose the proton as
# pH is raised and will be penalised.
# The remaining groups are allowed to titrate.
#
"""Penalize groups based on coupling effects.
Bases: The group with the highest pKa (the most stable one in the
charged form) will be the first one to adopt a proton as pH is lowered
and this group is allowed to titrate. The remaining groups are
penalised.
Acids: The group with the highest pKa (the least stable one in the
charged form) will be the last group to loose the proton as pH is
raised and will be penalised. The remaining groups are allowed to
titrate.
"""
penalised_labels = []
for all_groups in self.get_coupled_systems(self.get_covalently_coupled_groups(),
propka.group.Group.get_covalently_coupled_groups):
Group.get_covalently_coupled_groups):
# check if we should share determinants
if self.parameters.shared_determinants:
self.share_determinants(all_groups)
# find the group that has the highest pKa value
first_group = max(all_groups, key=lambda g:g.pka_value)
first_group = max(all_groups, key=lambda g: g.pka_value)
# In case of acids
if first_group.charge < 0:
first_group.coupled_titrating_group = min(all_groups, key=lambda g:g.pka_value)
first_group.coupled_titrating_group = min(all_groups, key=lambda g: g.pka_value)
penalised_labels.append(first_group.label) # group with the highest pKa is penalised
# In case of bases
else:
for a in all_groups:
if a == first_group:
for group in all_groups:
if group == first_group:
continue # group with the highest pKa is allowed to titrate...
a.coupled_titrating_group = first_group
penalised_labels.append(a.label) #... and the rest is penalised
group.coupled_titrating_group = first_group
penalised_labels.append(group.label) #... and the rest is penalised
return penalised_labels
@staticmethod
def share_determinants(groups):
"""Share sidechain, backbone, and Coloumb determinants between groups.
def share_determinants(self, groups):
Args:
groups: groups to share between
"""
# make a list of the determinants to share
types = ['sidechain','backbone','coulomb']
for type in types:
types = ['sidechain', 'backbone', 'coulomb']
for type_ in types:
# find maximum value for each determinant
max_dets = {}
for g in groups:
for d in g.determinants[type]:
for group in groups:
for det in group.determinants[type_]:
# update max dets
if d.group not in max_dets.keys():
max_dets[d.group] = d.value
if det.group not in max_dets.keys():
max_dets[det.group] = det.value
else:
max_dets[d.group] = max(d.value, max_dets[d.group], key= lambda v: abs(v))
max_dets[det.group] = max(det.value,
max_dets[det.group],
key=lambda v: abs(v))
# overwrite/add maximum value for each determinant
for det_group in max_dets.keys():
new_determinant = propka.determinant.Determinant(det_group, max_dets[det_group])
for g in groups:
g.set_determinant(new_determinant,type)
return
for det_group in max_dets:
new_determinant = Determinant(det_group, max_dets[det_group])
for group in groups:
group.set_determinant(new_determinant, type_)
def get_coupled_systems(self, groups, get_coupled_groups):
""" This generator will yield one covalently coupled system at the time """
"""A generator that yields covalently coupled systems.
Args:
groups: groups for generating coupled systems
get_coupled_groups: TODO - I don't know what this is
Yields:
covalently coupled systems
"""
groups = set(groups)
while len(groups)>0:
while len(groups) > 0:
# extract a system of coupled groups ...
system = set()
self.get_a_coupled_system_of_groups(groups.pop(), system, get_coupled_groups)
# ... and remove them from the list
groups -= system
yield system
return
def get_a_coupled_system_of_groups(self, new_group, coupled_groups,
get_coupled_groups):
"""Set up coupled systems of groups.
def get_a_coupled_system_of_groups(self, new_group, coupled_groups, get_coupled_groups):
Args:
new_group: added to coupled_groups
coupled_groups: existing coupled groups
get_coupled_groups: TODO - I don't know what this
"""
coupled_groups.add(new_group)
[self.get_a_coupled_system_of_groups(c, coupled_groups, get_coupled_groups) for c in get_coupled_groups(new_group) if c not in coupled_groups]
return
for coupled_group in get_coupled_groups(new_group):
if coupled_group not in coupled_groups:
self.get_a_coupled_system_of_groups(coupled_group,
coupled_groups,
get_coupled_groups)
def calculate_folding_energy(self, ph=None, reference=None):
"""Calculate folding energy over all groups in conformation container.
#
# Energy/summary-related methods
#
def calculate_folding_energy(self, pH=None, reference=None):
Args:
ph: pH for calculation
reference: reference state
Returns:
folding energy
TODO - need units
"""
ddg = 0.0
for group in self.groups:
#info('Folding energy for %s at pH %f: %f'%(group,pH,group.calculate_folding_energy(self.parameters, pH=pH, reference=reference)))
ddg += group.calculate_folding_energy(self.parameters, pH=pH, reference=reference)
ddg += group.calculate_folding_energy(self.parameters, ph=ph,
reference=reference)
return ddg
def calculate_charge(self, parmaeters, pH=None):
def calculate_charge(self, parameters, ph=None):
"""Calculate charge for folded and unfolded states.
Args:
parameters: parameters for calculation
ph: pH for calculation
Returns:
1. charge for unfolded state
2. charge for folded state
"""
unfolded = folded = 0.0
for group in self.get_titratable_groups():
unfolded += group.calculate_charge(parmaeters, pH=pH, state='unfolded')
folded += group.calculate_charge(parmaeters, pH=pH, state='folded')
return unfolded,folded
#
# conformation/bookkeeping/atom methods
#
unfolded += group.calculate_charge(parameters, ph=ph,
state='unfolded')
folded += group.calculate_charge(parameters, ph=ph,
state='folded')
return unfolded, folded
def get_backbone_groups(self):
""" returns all backbone groups needed for the pKa calculations """
"""Get backbone groups needed for the pKa calculations.
Returns:
list of groups
"""
return [group for group in self.groups if 'BB' in group.type]
def get_sidechain_groups(self):
""" returns all sidechain groups needed for the pKa calculations """
return [group for group in self.groups if ('BB' not in group.type\
and not group.atom.cysteine_bridge)]
"""Get sidechain groups needed for the pKa calculations.
Returns:
list of groups
"""
return [group for group in self.groups \
if ('BB' not in group.type and not group.atom.cysteine_bridge)]
def get_covalently_coupled_groups(self):
return [g for g in self.groups if len(g.covalently_coupled_groups)>0]
"""Get covalently coupled groups needed for pKa calculations.
Returns:
list of groups
"""
return [g for g in self.groups \
if len(g.covalently_coupled_groups) > 0]
def get_non_covalently_coupled_groups(self):
return [g for g in self.groups if len(g.non_covalently_coupled_groups)>0]
"""Get non-covalently coupled groups needed for pKa calculations.
def get_backbone_NH_groups(self):
""" returns all NH backbone groups needed for the pKa calculations """
Returns:
list of groups
"""
return [g for g in self.groups \
if len(g.non_covalently_coupled_groups) > 0]
def get_backbone_nh_groups(self):
"""Get NH backbone groups needed for pKa calculations.
Returns:
list of groups
"""
return [group for group in self.groups if group.type == 'BBN']
def get_backbone_CO_groups(self):
""" returns all CO backbone groups needed for the pKa calculations """
def get_backbone_co_groups(self):
"""Get CO backbone groups needed for pKa calculations.
Returns:
list of groups
"""
return [group for group in self.groups if group.type == 'BBC']
def get_groups_in_residue(self, residue):
"""Get residue groups needed for pKa calculations.
Args:
residue: specific residue with groups
Returns:
list of groups
"""
return [group for group in self.groups if group.residue_type == residue]
def get_titratable_groups(self):
"""Get all titratable groups needed for pKa calculations.
Returns:
list of groups
"""
return [group for group in self.groups if group.titratable]
def get_groups_for_calculations(self):
"""
Returns a list of groups that should be included in results report.
"""Get a list of groups that should be included in results report.
If --titrate_only option is specified, only residues that are titratable
and are in that list are included; otherwise all titratable residues
and CYS residues are included.
Returns:
list of groups
"""
return [group for group in self.groups if group.use_in_calculations()]
def get_acids(self):
return [group for group in self.groups if (group.residue_type in self.parameters.acid_list
and not group.atom.cysteine_bridge)]
"""Get acid groups needed for pKa calculations.
Returns:
list of groups
"""
return [group for group in self.groups \
if (group.residue_type in self.parameters.acid_list \
and not group.atom.cysteine_bridge)]
def get_backbone_reorganisation_groups(self):
return [group for group in self.groups if (group.residue_type in self.parameters.backbone_reorganisation_list
and not group.atom.cysteine_bridge)]
"""Get groups involved with backbone reorganization.
Returns:
list of groups
"""
return [group for group in self.groups \
if (group.residue_type in self.parameters.backbone_reorganisation_list \
and not group.atom.cysteine_bridge)]
def get_ions(self):
return [group for group in self.groups if group.residue_type in self.parameters.ions.keys()]
"""Get ion groups.
def get_group_names(self, list):
return [group for group in self.groups if group.type in list]
Returns:
list of groups
"""
return [group for group in self.groups \
if group.residue_type in self.parameters.ions.keys()]
def get_group_names(self, group_list):
"""Get names of groups in list.
Args:
group_list: list to check
Returns:
list of groups
"""
return [group for group in self.groups if group.type in group_list]
def get_ligand_atoms(self):
return [atom for atom in self.atoms if atom.type=='hetatm']
"""Get atoms associated with ligands.
Returns:
list of atoms
"""
return [atom for atom in self.atoms if atom.type == 'hetatm']
def get_heavy_ligand_atoms(self):
return [atom for atom in self.atoms if atom.type=='hetatm' and atom.element != 'H']
"""Get heavy atoms associated with ligands.
def get_chain(self,chain):
Returns:
list of atoms
"""
return [atom for atom in self.atoms \
if atom.type == 'hetatm' and atom.element != 'H']
def get_chain(self, chain):
"""Get atoms associated with a specific chain.
Args:
chain: chain to select
Returns:
list of atoms
"""
return [atom for atom in self.atoms if atom.chain_id != chain]
def add_atom(self, atom):
#info(self,'adding',atom)
"""Add atom to container.
Args:
atom: atom to add
"""
self.atoms.append(atom)
if not atom.conformation_container:
atom.conformation_container = self
if not atom.molecular_container:
atom.molecular_container = self.molecular_container
# store chain id for bookkeeping
if not atom.chain_id in self.chains:
self.chains.append(atom.chain_id)
return
def copy_atom(self, atom):
new_atom = atom.make_copy()
"""Add a copy of the atom to container.
Args:
atom: atom to copy and add
"""
new_atom = atom.make_copy()
self.atoms.append(new_atom)
new_atom.conformation_container = self
return
def get_non_hydrogen_atoms(self):
return [atom for atom in self.atoms if atom.element!='H']
"""Get atoms that are not hydrogens.
Returns:
list of atoms
"""
return [atom for atom in self.atoms if atom.element != 'H']
def top_up(self, other):
""" Tops up self with all atoms found in other but not in self """
my_residue_labels = { a.residue_label for a in self.atoms }
"""Adds any atoms found in `other` but not in this container.
Tops up self with all atoms found in other but not in self.
Args:
other: conformation container with atoms to add
"""
my_residue_labels = {a.residue_label for a in self.atoms}
for atom in other.atoms:
if not atom.residue_label in my_residue_labels:
self.copy_atom(atom)
return
def find_group(self, group):
for g in self.groups:
if g.atom.residue_label == group.atom.residue_label:
if g.type == group.type:
return g
"""Find a group in the container.
Args:
group: group to find
Returns:
False (if group not found) or group
"""
for group_ in self.groups:
if group_.atom.residue_label == group.atom.residue_label:
if group_.type == group.type:
return group_
return False
def set_ligand_atom_names(self):
"""Set names for atoms in ligands."""
for atom in self.get_ligand_atoms():
propka.ligand.assign_sybyl_type(atom)
return
def __str__(self):
return'Conformation container %s with %d atoms and %d groups'%(self.name,len(self),len(self.groups))
"""String that lists statistics of atoms and groups."""
str_ = 'Conformation container %s with %d atoms and %d groups' % (self.name,
len(self),
len(self.groups))
return str_
def __len__(self):
"""Number of atoms in container."""
return len(self.atoms)
def sort_atoms(self):
"""Sort atoms by `self.sort_atoms_key()` and renumber."""
# sort the atoms ...
self.atoms.sort(key=self.sort_atoms_key)
# ... and re-number them
for i in range(len(self.atoms)):
self.atoms[i].numb = i+1
return
@staticmethod
def sort_atoms_key(atom):
"""Generate key for atom sorting.
def sort_atoms_key(self, atom):
key = ord(atom.chain_id)*1e7
key += atom.res_num*1000
Args:
atom: atom for key generation.
Returns:
key for atom
"""
key = ord(atom.chain_id) * UNICODE_MULTIPLIER
key += atom.res_num * RESIDUE_MULTIPLIER
if len(atom.name) > len(atom.element):
key += ord(atom.name[len(atom.element)])
#info(atom,ord(atom.name[len(atom.element)]), '|%s||%s|'%(atom.name,atom.element))
return key

4
propka/coupled_groups.py
View File

@@ -38,7 +38,7 @@ class non_covalently_couple_groups:
if self.parameters.pH == 'variable':
use_pH = min(group1.pka_value, group2.pka_value)
default_energy = energy_method(pH=use_pH, reference=self.parameters.reference)
default_energy = energy_method(ph=use_pH, reference=self.parameters.reference)
default_pka1 = group1.pka_value
default_pka2 = group2.pka_value
@@ -54,7 +54,7 @@ class non_covalently_couple_groups:
group2.calculate_total_pka()
# store swapped energy and pka's
swapped_energy = energy_method(pH=use_pH, reference=self.parameters.reference)
swapped_energy = energy_method(ph=use_pH, reference=self.parameters.reference)
swapped_pka1 = group1.pka_value
swapped_pka2 = group2.pka_value

18
propka/group.py
View File

@@ -557,11 +557,11 @@ class Group:
# Energy-related methods
#
def calculate_folding_energy(self, parameters, pH=None, reference=None):
def calculate_folding_energy(self, parameters, ph=None, reference=None):
"""
returning the electrostatic energy of this residue at pH 'pH'
"""
if pH == None:
if ph == None:
pH = parameters.pH
if reference == None:
reference = parameters.reference
@@ -582,12 +582,12 @@ class Group:
# calculating the ddG(low-pH --> pH) contribution
# folded
x = pH - self.pka_value
x = ph - self.pka_value
y = 10**x
Q_pro = math.log10(1+y)
# unfolded
x = pH - self.model_pka
x = ph - self.model_pka
y = 10**x
Q_mod = math.log10(1+y)
@@ -596,12 +596,12 @@ class Group:
return ddG
def calculate_charge(self, parmaeters, pH=7.0, state='folded'):
def calculate_charge(self, parmaeters, ph=7.0, state='folded'):
if state == "unfolded":
x = self.charge * (self.model_pka - pH)
x = self.charge * (self.model_pka - ph)
else:
x = self.charge * (self.pka_value - pH)
x = self.charge * (self.pka_value - ph)
y = 10**x
charge = self.charge*(y/(1.0+y))
@@ -1319,8 +1319,8 @@ def is_ligand_group_by_marvin_pkas(parameters, atom):
if not atom.conformation_container.marvin_pkas_calculated:
lpka = propka.ligand_pka_values.ligand_pka_values(parameters)
lpka.get_marvin_pkas_for_molecular_container(atom.molecular_container,
min_pH=parameters.min_ligand_model_pka,
max_pH=parameters.max_ligand_model_pka)
min_ph=parameters.min_ligand_model_pka,
max_ph=parameters.max_ligand_model_pka)
if atom.marvin_pka:

6
propka/molecular_container.py
View File

@@ -136,7 +136,7 @@ class Molecular_container:
def average_of_conformations(self):
# make a new configuration to hold the average values
avr_conformation = propka.conformation_container.Conformation_container(name='average',
avr_conformation = propka.conformation_container.ConformationContainer(name='average',
parameters=self.conformations[self.conformation_names[0]].parameters,
molecular_container=self)
@@ -192,7 +192,7 @@ class Molecular_container:
# calculate stability profile
profile = []
for ph in propka.lib.make_grid(*grid):
ddg = self.conformations[conformation].calculate_folding_energy( pH=ph, reference=reference)
ddg = self.conformations[conformation].calculate_folding_energy( ph=ph, reference=reference)
#info(ph,ddg)
profile.append([ph, ddg])
@@ -220,7 +220,7 @@ class Molecular_container:
def getChargeProfile(self, conformation='AVR', grid=[0., 14., .1]):
charge_profile = []
for ph in propka.lib.make_grid(*grid):
q_unfolded, q_folded = self.conformations[conformation].calculate_charge(self.version.parameters, pH=ph)
q_unfolded, q_folded = self.conformations[conformation].calculate_charge(self.version.parameters, ph=ph)
charge_profile.append([ph, q_unfolded, q_folded])
return charge_profile

6
propka/pdb.py
View File

@@ -8,7 +8,7 @@ import propka.lib
from propka.lib import info, warning
from propka.atom import Atom
from propka.conformation_container import Conformation_container
from propka.conformation_container import ConformationContainer
expected_atom_numbers = {'ALA':5,
'ARG':11,
@@ -39,7 +39,7 @@ def read_pdb(pdb_file, parameters, molecule):
lines = get_atom_lines_from_pdb(pdb_file, ignore_residues = parameters.ignore_residues, keep_protons = molecule.options.keep_protons, chains=molecule.options.chains)
for (name, atom) in lines:
if not name in conformations.keys():
conformations[name] = Conformation_container(name=name, parameters=parameters, molecular_container=molecule)
conformations[name] = ConformationContainer(name=name, parameters=parameters, molecular_container=molecule)
conformations[name].add_atom(atom)
# make a sorted list of conformation names
@@ -260,7 +260,7 @@ def read_input(input_file, parameters,molecule):
lines = get_atom_lines_from_input(input_file)
for (name, atom) in lines:
if not name in conformations.keys():
conformations[name] = Conformation_container(name=name, parameters=parameters, molecular_container=molecule)
conformations[name] = ConformationContainer(name=name, parameters=parameters, molecular_container=molecule)
conformations[name].add_atom(atom)
# make a sorted list of conformation names