Precipitation pattern as a proxy for protein crystallization

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.</p

Precipitation pattern as a proxy for protein crystallization

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.</p

TeXRank - Precipitation Pattern Libraries

<p>Libraries to be used with TeXRank.</p

Using Textons to Rank Crystallization Droplets by Likely Presence of Crystals

Visual inspection of crystallization experiments is an important yet time-consuming and subjective step in X-ray crystallography. Previous published studies focused on automatically classifying crystallization droplets into distinct experiment outcomes; here, a method is described that instead ranks droplets by their likelihood of containing crystals or microcrystals, thereby prioritizing for visual inspection those images that are most likely to contain useful information. The use of textons is introduced to describe crystallization droplets objectively, allowing them to be scored with the posterior probability of a Random Forest classifier trained against droplets manually annotated for presence or absence of crystals or microcrystals. When images are ranked for viewing according to these scores, so that droplets with probable crystalline behaviour are placed early in the viewing order, then the top 10 wells include at least one human-annotated crystal or microcrystal for 94% of plates in a dataset of 196 plates imaged with a Minstrel HT system. This algorithm is robustly transferable to at least one other imaging system: applying the same parameters trained from Minstrel HT images to a dataset imaged by the Rock-Imager system, human-annotated crystals ranked in the top 10 wells for 90% of plates. Because the shape of the curve of scores is itself a useful overview of the plate’s behaviour, a custom viewer was written to integrate presentation of this curve with the display of images in their ranked order. Evidence is presented that such ranked viewing of images results in faster but more accurate evaluation of drops, in particular for the identification of microcrystals

Image colourisation for early infarct detection

Although early detection of infarct sign can provide early treatment to patients, early infarct detection on brain images is well-known to be difficult due to their subtle signs. A method is proposed to aid the detection of infarct through image colourisation based on Hounsfield units. A test was conducted in a private university to evaluate the algorithm, and the method appeared helpful and robust. The limited results showed that students with and without medical background had improved their ability of detecting early sign by 5.5% with the help of image colourisation