Beta lactamase
This tutorial follows the same steps as this command line tutorial , but here processing will be driven graphically through the DIALS User Interface, Dui2.
The following example uses a Beta-lactamase dataset collected using beamline I04 at Diamond Light Source, and reprocessed especially for these tutorials.
[Hint:] If you are physically at Diamond on the CCP4 Workshop, then this data is already available in your training data area. After typing
module load ccp4-workshopyou'll be moved to a working folder, with the data already located in thetutorial-data/summedsub directory.
The data is otherwise available for download from here. We'll only be using the first run of data in this tutorial,C2sum_1.tar, extracted to a tutorial-data/summed subdirectory.
When DUI starts up you will initially be presented by a window like the following.
At this stage you can resize various parts of the interface until you are comfortable with the layout, but you can’t do much else until the dataset is imported by DIALS. To do this, click on the "Select file(s)" button, navigate to the location of the images and select any one of them. Dui2 will automatically determine the filename template and will show that with a wildcard in the text box. If there are problems with this template it is possible to edit this before importing the images. Otherwise, just click the “Run” button to proceed.
What happens then is that that the metadata are read for all the images in the dataset. If these are consistent, then the dataset is imported and initial models for the “Beam”, “Scan” and “Detector” are created. The images are now also displayed within the “Image” tab. You can adjust the contrast and colour scheme by controls under the “Display info ...” pull-down.
The first “real” task in any processing using DIALS is the spot finding. To run this job, click on the “find spots” button at the upper-left of the window. When you do this you will see a new node will be created in the “History Tree”. This node is currently green, which indicates that it has not been run yet. By contrast, the "Import" step is blue, which means this has been run. In general, it is always possible to navigate between each step of processing by clicking on the relevant position in the history. Advanced users will find this gives a great deal of control, allowing them to keep track of complex history, including parallel branches.
Spot-finding, like most of the other processing steps in Dui2, presents user parameters at two levels of detail. The “Simple” tab contains the basic parameters that are the most commonly changed. The “Advanced” tab contains all of those again, plus other parameters that may be required for expert use with challenging data sets. In many cases, however, the default settings are fine.
Note that spot-finding is done on every image in the dataset. This means the job can take some time, but by default it will be run in parallel using multiple processors. To proceed, press the “Run” button below the input parameters.
Once the job is finished, the image viewer will display small blue boxes around the pixels that have been marked as strong. It is also useful to click on the “Report” tab and scroll down to the “Analysis of strong reflections”. This shows a graph of the number of strong spots found per image. In this case there is a pretty steady rate of around 150 spots found on each image. If instead we had seen the number of strong spots drop off over the dataset, or otherwise show large variability we would start to worry about issues such as radiation damage or a poorly-centred crystal.
The cyan button at the bottom left of the graph opens a help window with a description of how the appearance of this plot may be affected by various data collection issues. In the “Log Text” window you can see the text output from the dials.find_spots program, which also includes an ASCII-art version of this plot.
The default parameters for spot finding usually do a good job for Pilatus images, such as these. However they may not be optimal for data from other detector types, such as CCDs or image plates. If you have a case where spot-finding has gone badly, it may be helpful to debug using the dials.image_viewer and dials.reciprocal_lattice_viewer, which can be launched via buttons shown on the “Tools” tab.
In particular, the effect of changing the spot-finding parameters can be explored interactively with the dials.image_viewer. The image mode buttons at the bottom of the “Settings” window allow a preview of how the parameters affect the spot finding algorithm. The final image, (“threshold”) is the one on which spots were found, so ensuring this produces peaks at real diffraction spot positions will give the best chance of success.
The second external viewer, the dials.reciprocal_lattice_viewer, displays the strong spots in 3D, after mapping them from their detector positions to reciprocal space. In a favourable case you should be able to see the crystal’s reciprocal lattice by eye in the strong spot positions. Some practice may be needed in rotating the lattice to an orientation that shows off the periodicity in reciprocal lattice positions.
Although the reciprocal spacing is visible, in this data, there are clearly some systematic distortions. These will be solved during indexing.
The next step will be indexing of the strong spots. Click on the “Index” button to move on to this step, and form a new node in the history tree. Here we see that the simple parameters allows to select between different “Indexing Methods”, the default of which is the 3D FFT algorithm. The other options include the 1D FFT (DPS) algorithm and a special version of the 3D FFT called real_space_grid_search, which is particularly useful for narrow wedges containing multiple lattices, but requires a known cell and space group to be set under the “Advanced” parameters. If we do know the cell and space group, these can also be set as hints for any of the other indexing algorithms. This can help in difficult cases and will be used to constrain the lattice during refinement. Otherwise indexing and refinement will be carried out in the primitive lattice using space group "P 1" .
In this case, keep the method set to the default "fft3d" and click “Run” to start the indexing job. Once the job has finished running, you can see in the “Report” tab if you expand the "Experimental geometry" field, that the experimental models have now been completed with a “Crystal” model.
Now let’s click through the rest of the tabs of output. First, on the “Image” tab you will now see that indexed strong spots are assigned Miller indices. By default only the nearest one to the mouse cursor is shown, but this can be changed under the “Display” settings. If you also click on the “Predictions” checkbox, under “Reflection Type” you will in addition see centroid positions and Miller indices for all predicted reflections, not just the strong spots.
XXXXXXXXXXXXXXXXXXXXXXXXXXXXX
YYYYYYYYYYYYYYYYYYYYYYYYYYYYYY
ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ