Metadata-Version: 1.1
Name: propkatraj
Version: 0.1.0
Summary: obtain pKas for titreatable residues from a simulation trajectory
Home-page: UNKNOWN
Author: David Dotson
Author-email: dotsdl@gmail.com
License: GPLv3
Description: # 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 3.1](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](http://mdanalysis.org)
        * [pandas](http://pandas.pydata.org/)
        
        See [INSTALL.md](INSTALL.md) for how to install everything.
        
        ## Usage
        
        The `propkatra.get_propka()` function contains all functionality. 
        
            from propkatraj import get_propka
        
        It takes a `MDAnalysis.Universe` instance as argument and runs PROPKA on each
        frame of the trajectory.
        
        
            def 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](./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.
        
        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).
        
        ## Contact
        
        Please raise issues in the
        [issue tracker](https://github.com/Becksteinlab/propkatraj/issues).
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
