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Protein crystallography is used for the determination of the structure of biological molecules with atomic detail. This is invaluable for understanding the function of proteins and their role in biology. For this, crystals must be formed from a purified target protein. Crystallisation of proteins is a major bottleneck in protein crystallography and usually requires crystals of dimensions >2 microns, however, larger crystals are often pursued. Nanocrystals have submicron dimensions making them challenging to identify since they are not normally visible using light microscopy.
X-ray diffraction of single crystals can be used to identify these protein nanocrystals but diffraction is weak due to their size. Collecting diffraction images from clusters/slurries of crystals results in powder diffraction - protein crystals have a characteristic diffraction pattern which distinguishes them from other potential sources of diffraction. While stronger than from single crystals, the signals from these data are usually still weaker than large protein crystals, making classification of the diffraction pattern as protein diffraction difficult.
Currently, identification of these nanocrystals must be made by manual data processing and visual inspection on a case by case basis. Such decision making can potentially be automated using machine learning. A powerful aspect of machine learning is the utilisation of the data to improve the decision making process.
The aim of the project is to develop an automated way to classify diffraction from slurries of nanocrystals. This will be done through the preparation of a test set of protein nanocrystals and collection of powder diffraction data from nanocrystals to build a database of powder diffraction images. These data would then be used to develop a machine learning algorithm for classifying whether the diffraction images represent protein nanocrystals or not. The software will eventually be integrated into the VMXi-->VMXm beamline workflow for nanocrystal identification, sample preparation, data collection and structure determination.
Project duration: 12 weeks
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