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#
# * This library is free software; you can redistribute it and/or
# * modify it under the terms of the GNU Lesser General Public
# * License as published by the Free Software Foundation; either
# * version 2.1 of the License, or (at your option) any later version.
# *
# * This library is distributed in the hope that it will be useful,
# * but WITHOUT ANY WARRANTY; without even the implied warranty of
# * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# * Lesser General Public License for more details.
#
#propka3.0, revision 182 2011-08-09
#-------------------------------------------------------------------------------------------------------
#-- --
#-- PROPKA: A PROTEIN PKA PREDICTOR --
#-- --
#-- VERSION 3.0, 01/01/2011, COPENHAGEN --
#-- BY MATS H.M. OLSSON AND CHRESTEN R. SONDERGARD --
#-- --
#-------------------------------------------------------------------------------------------------------
#
#
#-------------------------------------------------------------------------------------------------------
# References:
#
# Very Fast Empirical Prediction and Rationalization of Protein pKa Values
# Hui Li, Andrew D. Robertson and Jan H. Jensen
# PROTEINS: Structure, Function, and Bioinformatics 61:704-721 (2005)
#
# Very Fast Prediction and Rationalization of pKa Values for Protein-Ligand Complexes
# Delphine C. Bas, David M. Rogers and Jan H. Jensen
# PROTEINS: Structure, Function, and Bioinformatics 73:765-783 (2008)
#
# PROPKA3: Consistent Treatment of Internal and Surface Residues in Empirical pKa predictions
# Mats H.M. Olsson, Chresten R. Sondergard, Michal Rostkowski, and Jan H. Jensen
# Journal of Chemical Theory and Computation, 7, 525-537 (2011)
#-------------------------------------------------------------------------------------------------------
import math
from .lib import pka_print
max_intrinsic_pKa_diff = 2.0
min_interaction_energy = 0.5
max_free_energy_diff = 1.0
min_swap_pka_shift = 1.0
#pH = 7.0
pH = 'variable'
reference = 'neutral'
min_pka = 0.0
max_pka = 10.0
do_intrinsic = False
do_pair_wise = False
do_prot_stat = True
#pkas 0-10 average?
def is_coupled_pairwise(residue1, residue2):
interaction1 = get_interaction(residue1,residue2)
interaction2 = get_interaction(residue2,residue1)
interaction_free_pka1 = residue1.pKa_pro - interaction1
interaction_free_pka2 = residue2.pKa_pro - interaction2
max_interaction = max(interaction1,interaction2)
factor_diff_intrinsic_pka = get_pka_diff_factor(interaction_free_pka1, interaction_free_pka2)
factor_interaction = get_interaction_factor(max_interaction)
res = factor_diff_intrinsic_pka*factor_interaction
return (res,interaction_free_pka1,interaction_free_pka2, max_interaction)
def is_coupled_intrinsic_pka(residue1, residue2):
""" Checks if residue1 and residue2 are coupled """
# calculate intrinsic pKa's, if not already done
for residue in [residue1, residue2]:
if not hasattr(residue, 'intrinsic_pKa'):
residue.calculateIntrinsicPKA()
# check if intrinsic pKa's are similar
factor_diff_intrinsic_pka = get_pka_diff_factor(residue1.intrinsic_pKa, residue2.intrinsic_pKa)
#check if residues have electrostatic interaction
interaction_energy = max(get_interaction(residue1,residue2), get_interaction(residue2,residue1))
factor_interaction = get_interaction_factor(interaction_energy)
res = factor_diff_intrinsic_pka * factor_interaction
return (res, interaction_energy)
def is_coupled_protonation_state_probability(protein, residue1, residue2, options=None):
interaction_energy = max(get_interaction(residue1,residue2), get_interaction(residue2,residue1))
if interaction_energy<=min_interaction_energy:
return {'coupling_factor':-1.0}
# calculate intrinsic pKa's, if not already done
for residue in [residue1, residue2]:
if not hasattr(residue, 'intrinsic_pKa'):
residue.calculateIntrinsicPKA()
use_pH = pH
if pH == 'variable':
use_pH = min(residue1.pKa_pro, residue2.pKa_pro)
#default_energy = protein.calculateFoldingEnergy(pH=use_pH, reference="neutral")
default_energy = protein.calculateFoldingEnergy(pH=use_pH, options=options)
default_pka1 = residue1.pKa_pro
default_pka2 = residue2.pKa_pro
# check that pka values are within relevant limits
if max(default_pka1, default_pka2) < min_pka or min(default_pka1, default_pka2) > max_pka:
return {'coupling_factor':-1.0}
# Swap interactions and re-calculate pKa values
swap_interactions(residue1, residue2,verbose=False)
residue1.calculateTotalPKA()
residue2.calculateTotalPKA()
# store swapped energy and pka's
swapped_energy = protein.calculateFoldingEnergy(pH=use_pH, options=options)
swapped_pka1 = residue1.pKa_pro
swapped_pka2 = residue2.pKa_pro
pka_shift1 = swapped_pka1 - default_pka1
pka_shift2 = swapped_pka2 - default_pka2
# Swap back to original protonation state
swap_interactions(residue1, residue2,verbose=False)
residue1.calculateTotalPKA()
residue2.calculateTotalPKA()
if abs(default_energy - swapped_energy)<=max_free_energy_diff:
if max(abs(pka_shift1), abs(pka_shift2)) >= min_swap_pka_shift:
if abs(residue1.intrinsic_pKa - residue2.intrinsic_pKa) <= max_intrinsic_pKa_diff:
factor = get_free_energy_diff_factor(default_energy, swapped_energy)*\
get_pka_diff_factor(residue1.intrinsic_pKa, residue2.intrinsic_pKa)*\
get_interaction_factor(interaction_energy)
return {'coupling_factor':factor,
'default_energy':default_energy,
'swapped_energy':swapped_energy,
'interaction_energy':interaction_energy,
'swapped_pka1':swapped_pka1,
'swapped_pka2':swapped_pka2,
'pka_shift1':pka_shift1,
'pka_shift2':pka_shift2,
'pH':use_pH}
return {'coupling_factor':-1.0}
def get_pka_diff_factor(pka1, pka2):
intrinsic_pka_diff = abs(pka1-pka2)
res = 0.0
if intrinsic_pka_diff <= max_intrinsic_pKa_diff:
res = 1-(intrinsic_pka_diff/max_intrinsic_pKa_diff)**2
return res
def get_free_energy_diff_factor(energy1, energy2):
free_energy_diff = abs(energy1-energy2)
res = 0.0
if free_energy_diff <= max_free_energy_diff:
res = 1-(free_energy_diff/max_free_energy_diff)**2
return res
def get_interaction_factor(interaction_energy):
res = 0.0
interaction_energy = abs(interaction_energy)
if interaction_energy >= min_interaction_energy:
res = (interaction_energy-min_interaction_energy)/(1.0+interaction_energy-min_interaction_energy)
return res
def identify_coupled_residues(protein, options=None):
""" Finds coupled residues in protein """
verbose=options.display_coupled_residues
if True:
pka_print('')
pka_print(' Detecting coupled residues')
pka_print(' Maximum pKa difference: %4.2f pKa units'%max_intrinsic_pKa_diff)
pka_print(' Minimum interaction energy: %4.2f pKa units'%min_interaction_energy)
pka_print(' Maximum free energy diff.: %4.2f pKa units'%max_free_energy_diff)
pka_print(' Minimum swap pKa shift: %4.2f pKa units'%min_swap_pka_shift)
pka_print(' pH: %6s '%str(pH))
pka_print(' Reference: %s'%reference)
pka_print(' Min pKa: %4.2f'%min_pka)
pka_print(' Max pKa: %4.2f'%max_pka)
pka_print('')
# make a single list of all residues in the protein
all_residues = []
for chain in protein.chains:
for residue in chain.residues:
if not residue in protein.residue_dictionary["ION"]:
all_residues.append(residue)
# find coupled residues
for i in range(len(all_residues)):
for j in range(len(all_residues)):
if i==j:
break
swap = 0
if do_intrinsic:
(coupling_factor_intrinsic_pka, interaction_energy ) = is_coupled_intrinsic_pka(all_residues[i],all_residues[j])
if coupling_factor_intrinsic_pka>0.0:
swap=1
pka_print(' %s and %s coupled (intrinsic pKa): %4.2f | intrinsic pkas: %5.2f - %5.2f = %5.2f | int. energy: %4.2f'%(all_residues[i],
all_residues[j],
coupling_factor_intrinsic_pka,
all_residues[i].intrinsic_pKa,
all_residues[j].intrinsic_pKa,
all_residues[i].intrinsic_pKa-
all_residues[j].intrinsic_pKa,
interaction_energy))
if do_pair_wise:
(coupling_factor_pairwise, int_free_pka1, int_free_pka2, interaction_energy) = is_coupled_pairwise(all_residues[i],all_residues[j])
if coupling_factor_pairwise>0.0:
swap=1
pka_print(' %s and %s coupled (pair wise) : %4.2f | int. free pkas: %5.2f - %5.2f = %5.2f | int. energy: %4.2f'%(all_residues[i],
all_residues[j],
coupling_factor_pairwise,
int_free_pka1, int_free_pka2,
int_free_pka1- int_free_pka2,
interaction_energy))
if do_prot_stat:
data = is_coupled_protonation_state_probability(protein, all_residues[i], all_residues[j], options=options)
if data['coupling_factor'] >0.0:
swap=1
all_residues[i].coupled_residues.append(all_residues[j])
all_residues[j].coupled_residues.append(all_residues[i])
protein.coupled_residues = True
#pka_print('Coupled residue of', all_residues[i],':', all_residues[j])
#pka_print('Coupled residue of', all_residues[j],':', all_residues[i])
if verbose:
pka_print(make_data_to_string(data,all_residues[i],all_residues[j]))
if swap and verbose:
# swap...
swap_interactions(all_residues[i],all_residues[j])
# ...and swap back
swap_interactions(all_residues[i],all_residues[j], verbose=False)
return
def make_data_to_string(data, residue1, residue2):
s = """ %s and %s coupled (prot.state): %5.2f
Energy levels: %6.2f, %6.2f (difference: %6.2f) at pH %6.2f
Interaction energy: %6.2f
Intrinsic pka's: %6.2f, %6.2f (difference: %6.2f)
Swapped pKa's: %6.2f, %6.2f (difference: %6.2f, %6.2f)"""%(residue1,
residue2,
data['coupling_factor'],
data['default_energy'], data['swapped_energy'],
data['default_energy'] - data['swapped_energy'],
data['pH'],
data['interaction_energy'],
residue1.intrinsic_pKa,
residue2.intrinsic_pKa,
residue1.intrinsic_pKa-residue2.intrinsic_pKa,
data['swapped_pka1'],
data['swapped_pka2'],
data['pka_shift1'],
data['pka_shift2'])
return s
def get_interaction(residue1, residue2, include_side_chain_hbs = True):
determinants = residue1.determinants[2]
if include_side_chain_hbs:
determinants = residue1.determinants[0] + residue1.determinants[2]
interaction_energy = 0.0
for det in determinants:
if residue2.label == det.label:
interaction_energy += det.value
pka_print(' '.join((str(residue1), str(residue2), str(interaction_energy))))
return interaction_energy
def swap_interactions(residue1, residue2, include_side_chain_hbs = True, verbose=True):
if verbose:
pka_print(' '+'-'*113)
tagged_pka_print(' Original|',residue1.getDeterminantString(), [residue1.label, residue2.label])
tagged_pka_print(' Original|',residue2.getDeterminantString(), [residue1.label, residue2.label])
# swap the interactions!
transfer_determinant(residue1.determinants[2], residue2.determinants[2], residue1.label, residue2.label)
if include_side_chain_hbs:
transfer_determinant(residue1.determinants[0], residue2.determinants[0], residue1.label, residue2.label)
#re-calculate pKa values
residue1.calculateTotalPKA()
residue2.calculateTotalPKA()
if verbose:
tagged_pka_print(' Swapped |',residue1.getDeterminantString(), [residue1.label, residue2.label])
tagged_pka_print(' Swapped |',residue2.getDeterminantString(), [residue1.label, residue2.label])
pka_print(' '+'='*113)
pka_print('')
return
def transfer_determinant(determinants1, determinants2, label1, label2):
# find out what to transfer...
from1to2 = []
from2to1 = []
for det in determinants1:
if det.label == label2:
from1to2.append(det)
for det in determinants2:
if det.label == label1:
from2to1.append(det)
# ...and transfer it!
for det in from1to2:
det.label = label1
determinants2.append(det)
determinants1.remove(det)
for det in from2to1:
det.label = label2
determinants1.append(det)
determinants2.remove(det)
return
def tagged_pka_print(tag, s, labels):
s = "%s %s"%(tag,s)
s = s.replace('\n','\n%s '%tag)
for label in labels:
s = s.replace(label, '\033[31m%s\033[30m'%label)
pka_print(s)
return
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