/pKa-ANI

Accurate prediction of protein pKa with representation learning

Primary LanguagePythonOtherNOASSERTION

INSTALLATION

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.