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

• MDAnalysis
• pandas

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.failedframeslog. 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</code></pre>
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:

<pre><code class="lang-python">pkatraj.results.pkas.describe()</code></pre>
Documentation
See the example usage page 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.
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.
   
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.
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.