Obscure crystals: Extracting extra from X-ray data

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  • Published: May 1, 2017
  • Author: David Bradley
  • Channels: X-ray Spectrometry
thumbnail image: Obscure crystals: Extracting extra from X-ray data

Diamond approach

‘Event maps’ generated for different values of BDC, reveal the minor state optimally for only one value of BDC (Credit: Pearc et al, Nature Commun)

A new method allows scientists to extract previously hidden information from X-ray diffraction data on proteins and other biological molecules. The technique allows the difference between a target with and without its substrate to be more precisely refined in drug discovery.

Writing in the journal Nature Communications, Nicholas Pearce of the University of Oxford and his colleagues explain that in macromolecular crystallography, finding changed states (for example, when a substrate or ligand, binds to a receptor or enzyme, is usually difficult unless the signal to noise ratio is really strong. Commonly, ambiguous density is seen because different molecular states are only present in a fraction of sites within an actual crystal.

Researchers attempting to use such crystallographic information to develop new drug candidates can study the proteins with atomic detail but if the signals are weak, the results can be obscure. As such, existing analysis algorithms for examining the differences between bound and unbound macromolecule can only extract so much information when so much of it is obscured.

PanDDA for data

Now, the new Pan-Dataset Density Analysis (PanDDA) method extracts the picture of the bound compound with exceptionally clear and unambiguous detail. To do so, PanDDA first finds the source of the noise and then simply cancels it from the data. The system exploits the Diamond Light Source's ability to repeat dozens or even hundreds of measurements very rapidly, the data from which a noise correction can be applied in three-dimensions.

Macromolecular crystallography is, of course, a powerful technique for 3D structure determination and is considered a work-horse of rational drug design. "The problem of identifying binding events in crystallographic datasets can feel like looking for a needle in a haystack," explains Pearce. "In the case of the data we were analysing, it was even worse, because we had hundreds of haystacks, and didn't know which of them contained needles."


The team exploited the fact that most of the measurements were from "empty" crystals that didn’t contain a ligand bound to the protein, this allowed them to characterise the unbound form by simply looking for datasets that were different from the ones in which the ligand was bound to the protein. "Often in crystallography you can miss 'weak' bound forms, because each measurement is a superposition of the bound and unbound forms," adds Pearce. "This is akin to multiple sheets of tracing paper, each with one of at least two images, all overlaid on top of each other." He further explains that, "When trying to identify the image on only one of the 'sheets', it gets confused by what shows through from all the other sheets, so the image becomes susceptible to interpretation errors."

In order to get around this problem, Pearce and his colleagues developed a method that extracts the right set of "sheets" from the superposition. "Once we'd done that, interpreting the bound form becomes much easier, and enables us to confidently interpret the data, and build models of the interesting states in the data," he adds. The research used some 860 datasets of which only 75 contain a bound form of interest to the researchers.

The method is conceptually quite simple: treat the confusing superpositions as nothing more than a background correction problem. "However, an accurate estimate of the background is crucial, and in practice this was unthinkable until the advent of the new robotic technology offered by Diamond, which makes it routine to make such large numbers of measurements," adds Oxford's Frank von Delft. The researchers anticipate that their approach will lead to a sea change in how crystallographic models are generated and allow researchers to explore more poorly ordered crystals.

Related Links

Nature Commun 2017, 8, online: "A Multi-Crystal Method for Extracting Obscured Crystallographic States from Conventionally Uninterpretable Electron Density"

Article by David Bradley

The views represented in this article are solely those of the author and do not necessarily represent those of John Wiley and Sons, Ltd.

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