propkatraj 2.0.1

Obtain pKas for titreatable residues from a simulation trajectory.

README: propkatraj

propkatraj.py can be used to computationally estimate pKa values for protein residues. We use an ensemble approach where many different conformations are sampled with equilibrium molecular dynamics simulations. We then apply the fast heuristic pKa predictor PROPKA 3 to individual frames of the trajectory. By analysing the statistics of the pKa predictions a more consistent picture emerges than from a pKa prediction of a single static conformation.

Required software

• PROPKA 3 (used as a Python package)
• MDAnalysis
• pandas

See INSTALL.md for how to install everything.

Usage

The propkatraj.PropkaTraj class contains all functionality. Import it with

from propkatraj import PropkaTraj

It takes a MDAnalysis.AtomGroup or MDAnalysis.Universe instance as an argument to initialize and runs PROPKA on each frame of the trajectory when calling the run() method. See help(PropkaTraj) for more details.

pkatraj = PropkaTraj(atomgroup, select='protein', skip_failure=False)

   Runs :program:`propka` on the titrateable residues of the selected AtomGroup
   on each frame in the trajectory.
   
   Parameters
   ----------
   atomgroup : :class:`MDAnalysis.Universe` or :class:`MDAnalysis.AtomGroup`
       Group of atoms containing the residues for pKa analysis. Please note
       that :class:`MDAnalysis.UpdatingAtomGroup` are not supported and will
       be automatically converted to :class:`MDAnalysis.AtomGroup`.
   select : str
       Selection string to use for selecting a subsection of atoms to use
       from the input ``atomgroup``. Note: passing non-protein residues to
       :program:`propka` may lead to incorrect results (see notes). [`protein`]
   skip_failure : bool
       If set to ``True``, skip frames where :program:`propka` fails. A list
       of failed frames is made available in
       :attr:`PropkaTraj.failed_frames_log`. If ``False`` raise a
       RuntimeError exception on those frames. [`False`]


    Notes
    -----
    Currently only the default behaviour supplemented with the `--quiet` flag
    of :program:`propka` is used.

    Temporary :program:`propka` files are written in the current working
    directory. This will leave a ``current.pka`` and ``current.propka_input``
    file. These are the temporary files for the final frame and can be removed
    safely.

    Current known issues:

    1. Due to the current behaviour of the MDAnalysis PDBWriter, non-protein
       atoms are written to PDBs using `ATOM` records instead of `HETATM`.
       This is likely to lead to undefined behaviour in :program:`propka`,
       which will likely expect `HETATM` inputs. We recommend users to only
       pass protein atoms for now. See the following issue for more details:
       https://github.com/Becksteinlab/propkatraj/issues/24


pkatraj.run()

   Perform the calculation

   Parameters
   ----------
   start : int, optional
      start frame of analysis
   stop : int, optional
      stop frame of analysis
   step : int, optional
      number of frames to skip between each analysed frame
   verbose : bool, optional
      Turn on verbosity

Calling the run() method creates a pandas.DataFrame, accessed through results.pkas, which contains the time as the first column and the residue numbers as subsequent columns. For each time step, the predicted pKa value for this residue is stored. Process the DataFrame to obtain statistics as shown in the Documentation. For example, you can get a summary of the statistics of the timeseries in the following manner:

pkatraj.results.pkas.describe()

Documentation

See the Jupyter notebook docs/propkatraj-example.ipynb for how to use propkatraj.PropkaTraj on an example trajectory and how to plot the data with seaborn.

Citation

If you use propkatraj in published work please cite Reference 1 for PROPKA 3.1 and Reference 2 for the ensemble method itself. Reference 3 is for the software if you need a specific software citation.

1. C. R. Søndergaard, M. H. M. Olsson, M. Rostkowski, and J. H. Jensen. Improved treatment of ligands and coupling effects in empirical calculation and rationalization of pKa values. J Chemical Theory and Computation, 7(7):2284–2295, 2011. doi: 10.1021/ct200133y.

2. C. Lee, S. Yashiro, D. L. Dotson, P. Uzdavinys, S. Iwata, M. S. P. Sansom, C. von Ballmoos, O. Beckstein, D. Drew, and A. D. Cameron. Crystal structure of the sodium-proton antiporter NhaA dimer and new mechanistic insights. J Gen Physiol, 144(6):529–544, 2014. doi: 10.1085/jgp.201411219.

3. David Dotson, Irfan Alibay, Rick Sexton, Shujie Fan, Armin Zijajo, Oliver Beckstein. (2020). Becksteinlab/propkatraj: 1.1.x. Zenodo. https://doi.org/10.5281/zenodo.3228425

Contact

Please raise issues in the issue tracker.