This page is still under construction.
There are several areas where I've used dummy images of pandas as placeholders.
I hope you accept my sincerest apologies, and enjoy the panda pictures in the meantime.
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 the most common use case, and for a description of all of the options please refer to the manual. 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 models of the ligand-bound protein structures (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 distributed as part of CCP4. Update your CCP4 to the latest version and everything should be available.
For developers, the panddas source code is available at bitbucket.
Email: firstname.lastname@example.org or email@example.com