The output of a differential scanning fluorimetry (DSF) assay is a series of melt curves, which need to be interpreted to get value from the assay. An application that translates raw thermal melt curve data into more easily assimilated knowledge is described. This program, called "Meltdown," conducts four main activities-control checks, curve normalization, outlier rejection, and melt temperature (T m ) estimation-and performs optimally in the presence of triplicate (or higher) sample data. The final output is a report that summarizes the results of a DSF experiment. The goal of Meltdown is not to replace human analysis of the raw fluorescence data but to provide a meaningful and comprehensive interpretation of the data to make this useful experimental technique accessible to inexperienced users, as well as providing a starting point for detailed analyses by more experienced users.

There is strong evidence to suggest that a protein sample needs to be well folded and uniform in order to form protein crystals, and it is accepted knowledge that the formulation can have profound effects on the behaviour of the protein sample. The technique of differential scanning fluorimetry (DSF) is a very accessible method to determine protein stability as a function of the formulation chemistry and the temperature. A diverse set of 252 soluble protein samples was subjected to a standard formulation-screening protocol using DSF. Automated analysis of the DSF results suggest that in over 35% of cases buffer screening significantly increases the stability of the protein sample. Of the 28 standard formulations tested, three stood out as being statistically better than the others: these included a formulation containing the buffer citrate, long known to be `protein friendly'; bis-tris and ADA were also identified as being very useful buffers in protein formulations.

The process of producing suitable crystals for X-ray diffraction analysis most often involves the setting up of hundreds (or thousands) of individual crystallization trials, each of which must be repeatedly examined for crystals or hints of crystallinity. Currently, the only real way to address this bottleneck is to use an automated imager to capture images of the trials. However, the images still need to be assessed for crystals or other outcomes. Ideally, there would exist some rapid and reliable machine-analysis tool to translate the images into a quantitative result. However, as yet no such tool exists in wide usage, despite this being a well recognized problem. One of the issues in creating robust automatic image-analysis software is the lack of reliable data for training machine-learning algorithms. Here, a mobile application, Cinder, has been developed which allows crystallization images to be scored quickly on a smartphone or tablet. The Cinder scores are inserted into the appropriate table in a crystallization database and are immediately available to the user through a more sophisticated web interface, allowing more detailed analyses. A sharp increase in the number of scored images was observed after Cinder was released, which in turn provides more data for training machine-learning tools.

The process of macromolecular crystallisation almost always begins by setting up crystallisation trials using commercial or other premade screens, followed by cycles of optimisation where the crystallisation cocktails are focused towards a particular small region of chemical space. The screening process is relatively straightforward, but still requires an understanding of the plethora of commercially available screens. Optimisation is complicated by requiring both the design and preparation of the appropriate secondary screens. Software has been developed in the C3 lab to aid the process of choosing initial screens, to analyse the results of the initial trials, and to design and describe how to prepare optimisation screens.

Many long-standing image processing problems in applied science domains are finding solutions through the application of deep learning approaches to image processing. Here we present one such application; the case of classifying images of protein crystallisation droplets. The Collaborative Crystallisation Centre in Melbourne, Australia is a medium throughput service facility that produces between five and twenty thousand images per day. This submission outlines a reliable and robust machine learning pipeline that autonomously classifies these images using CSIRO's high-performance computing facilities. Our pipeline achieves improved accuracies over existing implementations and delivers these results in real time. We discuss the specific tools and techniques used to construct the pipeline, as well as the methodologies for testing and validating externally developed classification models.