Mix-and-Quench Technology for Efficient and Routine Time-Resolved Crystallograph
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it