PanDDAs is built and tested on Linux.
It is not guaranteed to work on Mac (although I hope it does). Good luck!
(Windows? Probably not...)
This documentation is for PanDDA v0.2.X.
(and v0.3.X - coming soon!)
PanDDA v0.1.X is now obsolete and should not be used.
What is PanDDAs? The PanDDA (Pan-Dataset Density Analysis) method was developed to analyse the data resulting from crystallographic fragment screening. These experiments result in a large number of datasets that potentially contain weakly bound ligands. The detection and identification of weak signal caused by a binding ligand requires a sensitive and objective data-analysis method.
What's this page for? This page discusses the usage of the pandda programs. The tutorials page guides you through a basic example and gives an overview of the standard pandda protocol; the strategies page contains help for several common situations/approaches; and the manual page provides an inevitably incomplete list of all of the command-line options available.
For more details on the methods and algorithm, please refer to the paper:
For more information about modelling, it may be useful to refer to:
The input to a PanDDA analysis is a series of refined crystallographic datasets of the same crystal system. The datasets do not need to be strictly isomorphous, but for best results they should have the same solvent and buffer molecules. The only systematic difference between the datasets should be the presence of different ligands.
The output of a PanDDA analysis is a series of ligand-bound protein models (modelled manually with coot) and the associated evidence for the bound ligands. Multi-state ensembles are automatically generated, representing the superposition of bound and unbound conformations present in the crystal.
PanDDA is now distributed as part of CCP4 (you may need to update your version of CCP4 to the latest version to install it). Updates will be issued periodically within CCP4, but it is inevitable that the CCP4 version will fall behind the most-up-to-date version: consider updating your version from the code on bitbucket (instructions below).
For developers, the panddas source code is freely available on bitbucket. If you're interested in contributing, please see contact details below.
Due to the method used to bundle PanDDA inside CCP4, updating from the command line may not remove all of the original files from the distribution. So it's best to do an uninstall step first!
> ccp4-python -m pip uninstall panddas
> ccp4-python -m pip install pip --upgrade
> ccp4-python -m pip install numpy --upgrade
> ccp4-python -m pip install panddas
To install the latest developer version: instead of step 3) above, download the newest version from bitbucket. Unzip the download directory and
cd into it:
> cd "/path/to/download/directory"
Then install the new version:
> ccp4-python setup.py install