Using textons to rank crystallization droplets by the likely presence of crystals.

The visual inspection of crystallization experiments is an important yet time-consuming and subjective step in X-ray crystallography. Previously published studies have focused on automatically classifying crystallization droplets into distinct but ultimately arbitrary 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 the presence or absence of crystals or microcrystals. Unlike multi-class classification, this two-class system lends itself naturally to unidirectional ranking, which is most useful for assisting sequential viewing because images can be arranged simply by using these scores: this places droplets with probable crystalline behaviour early in the viewing order. Using this approach, the top ten wells included at least one human-annotated crystal or microcrystal for 94% of the plates in a data set of 196 plates imaged with a Minstrel HT system. The algorithm is robustly transferable to at least one other imaging system: when the parameters trained from Minstrel HT images are applied to a data set imaged by the Rock Imager system, human-annotated crystals ranked in the top ten wells for 90% of the plates. Because rearranging images is fundamental to the approach, a custom viewer was written to seamlessly support such ranked viewing, along with another important output of the algorithm, namely the shape of the curve of scores, which is itself a useful overview of the behaviour of the plate; additional features with known usefulness were adopted from existing viewers. Evidence is presented that such ranked viewing of images allows faster but more accurate evaluation of drops, in particular for the identification of microcrystals.

The low-cost Shifter microscope stage transforms the speed and robustness of protein crystal harvesting.

Despite the tremendous success of X-ray cryo-crystallography in recent decades, the transfer of crystals from the drops in which they are grown to diffractometer sample mounts remains a manual process in almost all laboratories. Here, the Shifter, a motorized, interactive microscope stage that transforms the entire crystal-mounting workflow from a rate-limiting manual activity to a controllable, high-throughput semi-automated process, is described. By combining the visual acuity and fine motor skills of humans with targeted hardware and software automation, it was possible to transform the speed and robustness of crystal mounting. Control software, triggered by the operator, manoeuvres crystallization plates beneath a clear protective cover, allowing the complete removal of film seals and thereby eliminating the tedium of repetitive seal cutting. The software, either upon request or working from an imported list, controls motors to position crystal drops under a hole in the cover for human mounting at a microscope. The software automatically captures experimental annotations for uploading to the user's data repository, removing the need for manual documentation. The Shifter facilitates mounting rates of 100-240 crystals per hour in a more controlled process than manual mounting, which greatly extends the lifetime of the drops and thus allows a dramatic increase in the number of crystals retrievable from any given drop without loss of X-ray diffraction quality. In 2015, the first in a series of three Shifter devices was deployed as part of the XChem fragment-screening facility at Diamond Light Source, where they have since facilitated the mounting of over 120 000 crystals. The Shifter was engineered to have a simple design, providing a device that could be readily commercialized and widely adopted owing to its low cost. The versatile hardware design allows use beyond fragment screening and protein crystallography.

Gentle, fast and effective crystal soaking by acoustic dispensing.

The steady expansion in the capacity of modern beamlines for high-throughput data collection, enabled by increasing X-ray brightness, capacity of robotics and detector speeds, has pushed the bottleneck upstream towards sample preparation. Even in ligand-binding studies using crystal soaking, the experiment best able to exploit beamline capacity, a primary limitation is the need for gentle and nontrivial soaking regimens such as stepwise concentration increases, even for robust and well characterized crystals. Here, the use of acoustic droplet ejection for the soaking of protein crystals with small molecules is described, and it is shown that it is both gentle on crystals and allows very high throughput, with 1000 unique soaks easily performed in under 10 min. In addition to having very low compound consumption (tens of nanolitres per sample), the positional precision of acoustic droplet ejection enables the targeted placement of the compound/solvent away from crystals and towards drop edges, allowing gradual diffusion of solvent across the drop. This ensures both an improvement in the reproducibility of X-ray diffraction and increased solvent tolerance of the crystals, thus enabling higher effective compound-soaking concentrations. The technique is detailed here with examples from the protein target JMJD2D, a histone lysine demethylase with roles in cancer and the focus of active structure-based drug-design efforts.

Lessons from ten years of crystallization experiments at the SGC.

Although protein crystallization is generally considered more art than science and remains significantly trial-and-error, large-scale data sets hold the promise of providing general learning. Observations are presented here from retrospective analyses of the strategies actively deployed for the extensive crystallization experiments at the Oxford site of the Structural Genomics Consortium (SGC), where comprehensive annotations by SGC scientists were recorded on a customized database infrastructure. The results point to the importance of using redundancy in crystallizing conditions, specifically by varying the mixing ratios of protein sample and precipitant, as well as incubation temperatures. No meaningful difference in performance could be identified between the four most widely used sparse-matrix screens, judged by the yield of crystals leading to deposited structures; this suggests that in general any comparison of screens will be meaningless without extensive cross-testing. Where protein sample is limiting, exploring more conditions has a higher likelihood of being informative by yielding hits than does redundancy of either mixing ratio or temperature. Finally, on the logistical question of how long experiments should be stored, 98% of all crystals that led to deposited structures appeared within 30 days. Overall, these analyses serve as practical guidelines for the design of initial screening experiments for new crystallization targets.