Module protkit.tools.propka_adaptor
Implements class PropkaAdaptor. The PropkaAdaptor class provides an interface
to the propka software, which is used to calculate pKa values of ionizable
residues and protein-ligand complexes based on the 3D structure.
To use the PropkaAdaptor class, you need to have the propka software installed. The propka software is automatically installed as a PyPi package when you install Protkit.
The propka software was developed by the Jensen Lab at the University of Copenhagen.
The source code is available at: https://github.com/jensengroup/propka
Documentation is available at: https://propka.readthedocs.io/en/latest/
If you use propka in your research, please cite the following papers:
Olsson, M. H. M., Søndergaard, C. R., Rostkowski, M., & Jensen, J. H. (2011). PROPKA3: Consistent Treatment of Internal and Surface Residues in Empirical pKa Predictions. Journal of Chemical Theory and Computation, 7(2), 525-537. https://doi.org/10.1021/ct100578z
Søndergaard, C. R., Olsson, M. H. M., Rostkowski, M., & Jensen, J. H. (2011). Improved Treatment of Ligands and Coupling Effects in Empirical Calculation and Rationalization of pKa Values. Journal of Chemical Theory and Computation, 7(7), 2284-2295. https://doi.org/10.1021/ct200133y
Expand source code
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Authors: Fred Senekal (FS), Claudio Jardim (CJ)
# Contact: fred@silicogenesis.com
# License: GPLv3
"""
Implements class `PropkaAdaptor`. The PropkaAdaptor class provides an interface
to the propka software, which is used to calculate pKa values of ionizable
residues and protein-ligand complexes based on the 3D structure.
To use the PropkaAdaptor class, you need to have the propka software installed.
The propka software is automatically installed as a PyPi package when you install
Protkit.
The propka software was developed by the Jensen Lab at the University of Copenhagen.
The source code is available at:
https://github.com/jensengroup/propka
Documentation is available at:
https://propka.readthedocs.io/en/latest/
If you use propka in your research, please cite the following papers:
Olsson, M. H. M., Søndergaard, C. R., Rostkowski, M., & Jensen, J. H. (2011).
PROPKA3: Consistent Treatment of Internal and Surface Residues in Empirical pKa Predictions.
Journal of Chemical Theory and Computation, 7(2), 525-537.
https://doi.org/10.1021/ct100578z
Søndergaard, C. R., Olsson, M. H. M., Rostkowski, M., & Jensen, J. H. (2011).
Improved Treatment of Ligands and Coupling Effects in Empirical Calculation and Rationalization of pKa Values.
Journal of Chemical Theory and Computation, 7(7), 2284-2295.
https://doi.org/10.1021/ct200133y
"""
import tempfile
from typing import Dict
from protkit.file_io import PDBIO
from protkit.structure import Protein
from propka.run import single
class PropkaAdaptorError(Exception):
pass
class PropkaAdaptor():
"""
Adapter class for the propka software.
"""
def __init__(self,
ph: float = 7.0):
"""
Constructs all the necessary attributes for the PropkaAdaptor object.
Args:
ph (float): The pH value.
"""
super().__init__()
self.ph = ph
def _get_pka(self, molecule) -> Dict[str, float]:
"""
Extracts the pKa values from the propka output.
Args:
molecule (MolecularContainer): The propka molecule.
Returns:
dict: The pKa values.
Notes:
The implementation was adapted from
write_pka(), get_summary_string(), get_summary_section() in Propka.
"""
pka_values = {}
conformation = "AVR"
for group in molecule.conformations[conformation].groups:
if not group.coupled_titrating_group:
# Propka computes pKa values for additional group types as well,
# for example N+. Further work is needed if we want to include
# these as well.
# if group.residue_type in molecule.version.parameters.write_out_order:
if group.residue_type in ["ASP", "GLU", "HIS", "CYS", "TYR", "LYS", "ARG", "SER"]:
pka_values[group.label] = group.pka_value
return pka_values
def calculate_pka(self,
protein: Protein,
output_pdb_file_path: str = None) -> Protein:
"""
Calculates the pKa values of ionizable residues in a protein structure.
Args:
protein (Protein): The protein.
output_pdb_file_path (str): Optional. The path to the output PDB file.
Returns:
Protein: The protein with pKa values.
"""
if output_pdb_file_path is None:
temp_file_pdb = tempfile.NamedTemporaryFile(delete=True, suffix=".pdb")
output_pdb_file_path = temp_file_pdb.name
PDBIO.save(protein, output_pdb_file_path)
# TODO: Propka offers the ability to read data from a string stream.
# In case that the PDB file is not written to disk, we can use this feature.
molecule = single(output_pdb_file_path, optargs=[f"--pH={self.ph}"], write_pka=False)
pkas = self._get_pka(molecule)
for label in pkas:
residue_type, residue_no, chain_id = label.split()
pka = pkas[label]
chain = protein.get_chain(chain_id)
# TODO: Need to verify how Propka handles residues with insertion codes.
residue = chain.get_residue_by_sequence_code(residue_no)
assert residue.residue_type == residue_type
residue.set_attribute("pka", pka)
protein_out = protein.copy()
return protein_outClasses
class PropkaAdaptor (ph: float = 7.0)-
Adapter class for the propka software.
Constructs all the necessary attributes for the PropkaAdaptor object.
Args
ph:float- The pH value.
Expand source code
class PropkaAdaptor(): """ Adapter class for the propka software. """ def __init__(self, ph: float = 7.0): """ Constructs all the necessary attributes for the PropkaAdaptor object. Args: ph (float): The pH value. """ super().__init__() self.ph = ph def _get_pka(self, molecule) -> Dict[str, float]: """ Extracts the pKa values from the propka output. Args: molecule (MolecularContainer): The propka molecule. Returns: dict: The pKa values. Notes: The implementation was adapted from write_pka(), get_summary_string(), get_summary_section() in Propka. """ pka_values = {} conformation = "AVR" for group in molecule.conformations[conformation].groups: if not group.coupled_titrating_group: # Propka computes pKa values for additional group types as well, # for example N+. Further work is needed if we want to include # these as well. # if group.residue_type in molecule.version.parameters.write_out_order: if group.residue_type in ["ASP", "GLU", "HIS", "CYS", "TYR", "LYS", "ARG", "SER"]: pka_values[group.label] = group.pka_value return pka_values def calculate_pka(self, protein: Protein, output_pdb_file_path: str = None) -> Protein: """ Calculates the pKa values of ionizable residues in a protein structure. Args: protein (Protein): The protein. output_pdb_file_path (str): Optional. The path to the output PDB file. Returns: Protein: The protein with pKa values. """ if output_pdb_file_path is None: temp_file_pdb = tempfile.NamedTemporaryFile(delete=True, suffix=".pdb") output_pdb_file_path = temp_file_pdb.name PDBIO.save(protein, output_pdb_file_path) # TODO: Propka offers the ability to read data from a string stream. # In case that the PDB file is not written to disk, we can use this feature. molecule = single(output_pdb_file_path, optargs=[f"--pH={self.ph}"], write_pka=False) pkas = self._get_pka(molecule) for label in pkas: residue_type, residue_no, chain_id = label.split() pka = pkas[label] chain = protein.get_chain(chain_id) # TODO: Need to verify how Propka handles residues with insertion codes. residue = chain.get_residue_by_sequence_code(residue_no) assert residue.residue_type == residue_type residue.set_attribute("pka", pka) protein_out = protein.copy() return protein_outMethods
def calculate_pka(self, protein: Protein, output_pdb_file_path: str = None) ‑> Protein-
Calculates the pKa values of ionizable residues in a protein structure.
Args
protein:Protein- The protein.
output_pdb_file_path:str- Optional. The path to the output PDB file.
Returns
Protein- The protein with pKa values.
Expand source code
def calculate_pka(self, protein: Protein, output_pdb_file_path: str = None) -> Protein: """ Calculates the pKa values of ionizable residues in a protein structure. Args: protein (Protein): The protein. output_pdb_file_path (str): Optional. The path to the output PDB file. Returns: Protein: The protein with pKa values. """ if output_pdb_file_path is None: temp_file_pdb = tempfile.NamedTemporaryFile(delete=True, suffix=".pdb") output_pdb_file_path = temp_file_pdb.name PDBIO.save(protein, output_pdb_file_path) # TODO: Propka offers the ability to read data from a string stream. # In case that the PDB file is not written to disk, we can use this feature. molecule = single(output_pdb_file_path, optargs=[f"--pH={self.ph}"], write_pka=False) pkas = self._get_pka(molecule) for label in pkas: residue_type, residue_no, chain_id = label.split() pka = pkas[label] chain = protein.get_chain(chain_id) # TODO: Need to verify how Propka handles residues with insertion codes. residue = chain.get_residue_by_sequence_code(residue_no) assert residue.residue_type == residue_type residue.set_attribute("pka", pka) protein_out = protein.copy() return protein_out
class PropkaAdaptorError (*args, **kwargs)-
Common base class for all non-exit exceptions.
Expand source code
class PropkaAdaptorError(Exception): passAncestors
- builtins.Exception
- builtins.BaseException