Mix-and-Quench Technology for Efficient and Routine Time-Resolved Crystallograph
Bibliographic record
Abstract
Time-resolved crystallography (TRX) allows observation of conformational states and binding characteristics during enzymatic reactions. By initiating a reaction in crystallo via mixing and diffusion and collecting diffraction data following a time delay, states along the reaction pathway can be captured. When diffraction data is collected at room temperature (at XFELs or synchrotrons), the required beamline apparatus is complex and enormous numbers of crystals are required to determine a structure at each time point. In 2021 we demonstrated1 a vastly more crystal-, protein-, and cost-efficient approach based on the mix-and-quench method. We improved the time resolution from >>1 s achieved in the 1990s to 40 ms, collected diffraction data on a standard cryocrystallography beamline, and obtained complete structures with as few as one crystal per time point. We have since been developing improved hardware for this method with the goal of providing a robust, flexible, and inexpensive platform for routine TRX that can drive broad application of this powerful method. Recently, time-resolved structures for seven time points between 8 ms and 2 s were obtained remotely from three pucks in a single beamtime by a single user, with sample preparation requiring less than one hour per puck. Our current hardware prepares samples with high reliability and high throughput, is easy to use, can deliver 10 ms time resolution, and ongoing improvements should eventually yield 2-3 ms time resolution. The primary obstacle to TRX studies is now a paucity of well-characterized crystal targets.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".