Becksteinlab/propkatraj

Name: propkatraj

Owner: Becksteinlab

Description: pKa estimates for proteins using an ensemble approach

Created: 2017-04-12 20:58:16.0

Updated: 2018-03-22 22:09:55.0

Pushed: 2018-03-15 22:51:00.0

Homepage: null

Size: 251

Language: Python

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README

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 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 (used as a Python package)
• MDAnalysis
• pandas

See INSTALL.md for how to install everything.

Usage

The propkatraj.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 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

See the Jupyter notebook docs/propkatraj-example.ipynb for how to use propkatraj.get_propka 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.

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.

Contact

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