Gentle, fast and effective crystal soaking by acoustic dispensing
Bibliographic record
Abstract
Synopsis A high-throughput method is described for crystal soaking using acoustic droplet ejection, and its effectiveness demonstrated. Abstract Bright light sources, agile robotics, and fast detectors are continually reducing the time it takes to perform an X-ray diffraction experiment, making high throughput experiments more feasible than ever. But this is also pushing the upstream bottleneck towards sample preparation, even for robust and well characterised crystal systems. Crystal soaking is routinely used to generate protein-ligand complex structures, yet protein crystals are often sensitive to changes in solvent composition, and frequently require gentle or careful stepwise soaking techniques, limiting overall throughput. Here, we describe the use of acoustic droplet ejection for soaking of protein crystals with small molecules, and show that it is both gentle on crystals and allows very high throughput, with 1000 unique soaks easily performed in under 10 minutes. In addition to having very low compound consumption (tens of nanolitres per sample), the positional precision of acoustic droplet ejection enables targeted placement of the compound/solvent away from crystals and towards drop edges, allowing for gradual diffusion of solvent across the drop. This ensures both an improvement in reproducibility of X-ray diffraction and an increased solvent tolerance of the crystals, thus enabling higher effective compound soaking concentrations. We detail the technique here with examples from the protein target JMJD2D, a histone lysine demethylase, having roles in cancer and the focus of active structure based drug design efforts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".