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.
The documentation on these pages is for PanDDA v0.2.X.
PanDDA v0.1.X is now obsolete and should not be used.
PanDDAs is written and tested on Mac and Linux. It is not tested on Windows.
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).
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!
1) On the command line, uninstall the current version of PanDDA:
> ccp4-python -m pip uninstall panddas
2) It is normally then required to update a couple of things:
> ccp4-python -m pip install pip --upgrade
> ccp4-python -m pip install numpy --upgrade
3) Install the newest released version using pip:
> ccp4-python -m pip install panddas
4) Science! (hopefully).
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
For developers, the panddas source code is freely available on bitbucket. If you're interested in contributing, please see contact details below.
pandda version 0.1 contains a series of major issues regarding the usability of the output maps. Though these maps are generated correctly, they are not properly aligned to the input models. This fundamentally affects the ability of the experimental evidence for the ligands -- the event maps -- to be disseminated and used for validating any model by a third party.
pandda version 0.2 and higher have fixed these issues - please use these instead.
Newer versions also have improved diagnostics for detecting artefacts in the data that can make it difficult to detect ligands.