pkaani 0.1.10

A Python package to calculate pKa values for proteins

INSTALLATION (using PyPI)

Simply use the command pip install pkaani. Note that this allows users to run the calculate_pka function from within a python script, but the command line usage of pkaani, which involves the use of sander to do energy minimization of the structure before doing calculate_pka, will return a ModuleNotFound error because ambertools is not installed.

To install ambertools, you can use the command:

conda install conda-forge::ambertools

Note that the use of energy minimization CAN have a significant impact on the pKa results! However, we still wanted to have pKa-ANI available using purely pip install and separate from the pre-processing energy minimization - this will be used to integrate pKa-ANI with pdb2pqr.

Python script usage

The snippet below shows how to use the calculate_pka function within a python script - the input argument is a list of strings which are PDB file names in the working directory.

from pkaani.pkaani import calculate_pka as calculate_pkaani
  
pKas = calculate_pkaani(["1brs.pdb","6oge_de.pdb"])

INSTALLATION (from source code)

Navigate to this repository for the source code: https://github.com/isayevlab/pKa-ANI/tree/main

Prior to the installation of pKa-ANI, users should make sure they have installed conda.

To install pKa-ANI, navigate to the directory of the source that you've downloaded and;

conda env create -f pkaani_env.yaml

This will create a conda environment named pkaani and install all required packages. After the environment is created;

conda activate pkaani 
python setup.py install

PREREQUISITES:

• miniconda/anaconda

If pkaani_env.yaml is not used, users should make sure the following packages are installed.

• python=3.8
• numpy
• scipy
• pytorch
• torchani=2.2.0
• scikit-learn=1.0.2
• ase
• joblib
• ambertools
• setuptools=58.2.0

Other libraries the system may require : os,math,sys,io,csv,getopt,shutil,urllib.request,warnings

USAGE

pKa-ANI requires PDB files to have H atoms that are added with default ionization states of residues: ASP, GLU, LYS, TYR, HIE.

Due to this reason, input PDB file(s) are prepared before the calculation of pKa values (output PDB file 'PDBID_pkaani.pdb').

We would like to warn users, that our models are trained to predict pKa values for apo-proteins. Due to this, any residue that is not an aminoacid is removed from PDB file(s) during the preparation.

Example command line usages:

• If PDB file doesnt exist, it is downloaded and prepared for pKa calculations.

pkaani -i 1BNZ
      
pkaani -i 1BNZ.pdb

• Multiple files can be given as inputs

pkaani -i 1BNZ,1E8L

• If a specific directory is wanted:

pkaani -i path_to_file/1BNZ
      
pkaani -i path_to_file/1BNZ,path_to_file/1E8L

Arguments:

-h: Help

-i: Input files. Inputs can be given with or without file extension (.pdb). 
    If PDB file is under a specific directory (or will be downloaded) the path                 
    can also be given as path_to_file/PDBFILE. Multiple PDB files can be given 
    by using "," as separator (i.e. pkaani -i 1BNZ,1E8L).

CITATION

Gokcan, H.; Isayev, O. Prediction of Protein p K a with Representation Learning. Chem. Sci. 2022, 13 (8), 2462–2474. https://doi.org/10.1039/D1SC05610G.

LICENSING

Please read LICENSE file.