Metadata-Version: 2.1
Name: propkatraj
Version: 1.0.2
Summary: obtain pKas for titreatable residues from a simulation trajectory
Home-page: UNKNOWN
Author: David Dotson
Author-email: dotsdl@gmail.com
License: GPLv3
Project-URL: Documentation, https://github.com/Becksteinlab/propkatraj/blob/master/README.md
Project-URL: Source, https://github.com/Becksteinlab/propkatraj
Project-URL: Issue Tracker, https://github.com/Becksteinlab/propkatraj/issues
Description: # README: propkatraj
        [![DOI](https://zenodo.org/badge/88095629.svg)](https://zenodo.org/badge/latestdoi/88095629)
        
        `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 3.1](https://github.com/jensengroup/propka-3.1) 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 3.1](https://github.com/jensengroup/propka-3.1) (used as a
          Python package)
        * [MDAnalysis](https://mdanalysis.org)
        * [pandas](https://pandas.pydata.org/)
        
        See
        [INSTALL.md](https://github.com/Becksteinlab/propkatraj/blob/master/INSTALL.md)
        for how to install everything.
        
        ## Usage
        
        The `propkatraj.get_propka()` function contains all
        functionality. Import it with
        
        ```python
        from propkatraj import get_propka
        ```
        
        It takes a `MDAnalysis.Universe` instance as an argument and runs PROPKA on each
        frame of the trajectory.
        
        ```
        get_propka(universe, sel='protein', start=None, stop=None, step=None)
        
           Get and store pKas for titrateable residues near the binding site.
           
           Parameters
           ----------
           universe : :class:`MDAnalysis.Universe`
        	   Universe to obtain pKas for.
           sel : str, array_like
        	   Selection string to use for selecting atoms to use from given
        	   ``universe``. Can also be a numpy array or list of atom indices to use.
           start : int
        	   Frame of trajectory to start from. `None` means start from beginning.
           stop : int
        	   Frame of trajectory to end at. `None` means end at trajectory end.
           step : int
        	   Step by which to iterate through trajectory frames. propka is slow,
        	   so set according to how finely you need resulting timeseries.
        
           Results
           -------
           pkas : :class:`pandas.DataFrame`
        	   DataFrame giving estimated pKa value for each residue for each
        	   trajectory frame. Residue numbers are given as column labels, times as
        	   row labels.
        ```
        
        The function returns a
        [pandas.DataFrame](http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe)
        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](#Documentation).
        
        
        ## Documentation
        
        See the Jupyter notebook
        [docs/propkatraj-example.ipynb](https://nbviewer.jupyter.org/github/Becksteinlab/propkatraj/blob/master/docs/propkatraj-example.ipynb)
        for how to use `propkatraj.get_propka` on an example trajectory and
        how to plot the data with [seaborn](https://seaborn.pydata.org/).
        
        ## 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](https://doi.org/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](https://doi.org/10.1085/jgp.201411219).
        
        3. Oliver Beckstein, David Dotson, Rick Sexton, Shujie Fan, and Armin Zijajo. 
           (2019, May 24). Becksteinlab/propkatraj: 1.0.0 (Version release-1.0.0). 
           Zenodo. http://doi.org/10.5281/zenodo.3228426
        
        ## Contact
        
        Please raise issues in the
        [issue tracker](https://github.com/Becksteinlab/propkatraj/issues).
        
Platform: UNKNOWN
Classifier: Development Status :: 6 - Mature
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v2 (GPLv2)
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown; variant=GFM
