Precipitation pattern as a proxy for protein crystallization

Thesis

X-ray crystallography has been the workhorse behind most 3D protein structures, which are crucial in the understanding of their biological function and interaction with other molecules. However, a major rate-limiting step in X-ray crystallography remains obtaining suitable protein crystals. The best approach to crystallise a protein can be summarized as a random-screen-and-wait procedure, with little to no readout from experiments that do not produce crystals. This project aims to make the most out of present screening practice, by establishing objective analyses of sparse-matrix screening experiments that extract informative readouts from this standard front-line experiment, independent of whether it yields visible crystals or not.

We have developed methods to objectively characterize crystallization outcome based on image analysis, enabling several things. Firstly, the ranking of droplets based on their likelihood of crystallinity to increase the efficiency and accuracy of human visual identification of crystals. Secondly, fingerprints of the collective precipitation behaviour of a protein across standard sparse-matrix can be generalised, and compared objectively to fingerprints of historical experiments, to assess crystallizability and infer optimization strategies based on past successes. Thirdly, clear drops can be automatically identified, and mapped to chemical components in a sparse-matrix screen to suggest alternative buffers for protein formulation. Fourthly, TeXRank, a user interface could be developed to present and make all algorithm output accessible for daily use. Fifthly, the associated data mining led us to evaluate the strategies for setting up screening experiments with limited protein samples, based on over ten years of crystallization data at the Structural Genomics Consortium, Oxford. Our methods capitalizes on present day standard screening procedure and hardware to extract useful information, bypassing laborious and subjective evaluation of each droplet.

Authors: Ng, JT

• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: precipitation pattern; machine learning; protein crystallization; pattern recognition
• Subjects: Protein crystallization; Structural biology; Medical sciences