There is a new version of PanDDA (0.2.12) that works with the newest updates of CCP4 (update 048+).

You can update your version with "pip" using the standard instructions below.

This version will be distributed with CCP4 in the next update (058).




Notices

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.
PanDDA v0.1.X is now obsolete and should not be used.




About

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:

Input

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.

Output

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.




Availability/Download

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). 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).

Latest Release Version

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).


Latest Developer Version

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

Source Code

For developers, the panddas source code is freely available on bitbucket. If you're interested in contributing, please see contact details below.




Version Changes + New Features

Version 0.3.0

Input Changes

  • Reorganisation of the input commands ("phil commands")

New Features

  • More informative error messages when errors occur during alignment!
  • ground_datasets=... option that allows the user to define the datasets to be used for map_characterisation (see strategies page: pandda.analyse).

Bug fixes

  • Map shearing?

Version 0.2.12

(Bug) fixes

  • Fix issues introduced in ccp4 update 048 (the "basic_map" error).
  • Fix compatibility with new version of edstats.
  • Fix compatibility with new versions of numpy.

Improvements

  • pandda.analyse

    • New ways to decide how datasets are processed:
      • ground_state_datasets=[...]
        • Mark which datasets are to be used for map characterisation
        • Only these datasets will be used for characterisation
        • Roughly the opposite of exclude_from_characterisation=[...]
      • only_datasets=[...]
        • Mark which datasets are to be loaded by the program
        • Only these datasets will be loaded from the input folder
        • Roughly the opposite of ignore_datasets=[...]
    • Clearer error messages for missing cryst lines, unit cells, etc.
      • "There is no crystal symmetry for this structure"
      • "There is no unit cell information for this structure"
      • "There is no spacegroup information for this structure"
    • Can now use median map instead of mean map in z-map calculation.
      • average_map=mean_map or average_map=medn_map.
    • Can restrict map characterisation to only part of a structure
      • Two different ways to mask:
        • masks.pdb="/path/to/file.pdb"
        • masks.selection_string="chain A"
      • Do NOT use a very small mask around the binding site as this will cause problems -- you need to include a large enough region (domain or chain) so that the the noise can be correctly characterised in each dataset.
    • Can restrict z-map characterisation to only part of a structure
      • Define mask with:
        • z_map_analysis.masks.selection_string="resseq 1900:1940"
      • Change size of mask around this region:
        • z_map_analysis.masks.outer_mask=10
  • pandda.inspect

    • Generate new ligands from smiles using acedrg
  • giant.datasets.prepare

    • New script for preparing data for pandda.
    • Automates the re-indexing of MTZ files, filling of missing reflections, transfer of R-free flags, and running of refinement pipelines (currently only dimple).
    • Example usages:
      • giant.datasets.prepare reference_pdb=ref.pdb reference_mtz=ref.mtz labelling=foldername data/*/input.mtz
      • giant.datasets.prepare reference_pdb=ref.pdb reference_mtz=ref.mtz labelling=filename data/*.mtz
  • giant.score_model

    • can now supply f_label to select columns for edstats.

Changes to Inputs

  • pandda.analyse

    • Reorganisation of input phil
      • z_map.[...] changed to statistical_maps.[...]
      • min/max_build_datasets moved from analysis.[...] to statistical_maps.[...]
      • structure_factors=[...] moved from maps.[...] to diffraction_data.[...]
      • apply_b_factor_scaling=[...] moved to diffraction_data.[...]
      • checks.[...] moved to diffraction_data.checks.[...]
      • blob_search.[...] renamed to z_map_analysis
    • Variable name changes
      • calculate_first_mean_map_only changed to calculate_first_average_map_only

Version 0.2.11

Bug fixes

  • Fix to giant.score_model_multiple that caused an error when a prefix was supplied.

Improvements

  • Dataset reflection data checks
    • All errors are reported rather than just exiting on first error
    • Errors now contain a detailed list of the missing/invalid reflections

Changes to Defaults

  • giant.merge_conformations
    • prune_duplicates_rmsd = 0.05 (used to be 0.1)
  • giant.quick_refine
    • split_conformations = False (used to be True)

Version 0.2.10

Bug fixes

  • Fix to giant.split_conformations regarding resetting occupancies for output

Improvements

  • pandda.analyse

    • now runs on structures containing non-standard amino acids
  • giant.merge_conformations

    • new option: prune_duplicates_rmsd=[...]
      • controls threshold where alternate conformations are removed/combined

Changes to Inputs

  • giant.merge_conformations
    • Allow the major+minor occupancy multipliers to both equal 1 to allow for pre-calculated occupancies.

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.