Please note: PanDDAs currently only works on Linux.

An improved version that works on Linux and Mac should be available early 2017.


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 tutorial guides you through a basic example; the manual guides you through the most common use cases; and the options page provides a near-complete list of all of the options available. For more details on the methods and algorithm, please refer to the paper:


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.


As part of CCP4

PanDDA is now distributed as part of CCP4 (you may need to update your version of CCP4 to the latest version to install it).

Source Code

For developers, the panddas source code is available at bitbucket.


Email:  n.m.pearce or frank.vondelft

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