Unravelling the mysteries of sub-second biochemical processes using time-resolved mass spectrometry
Bibliographic record
Abstract
Many important chemical and biochemical phenomena proceed on sub-second time scales before entering equilibrium. In this mini-review, we explore the history and recent advancements of time-resolved mass spectrometry (TRMS) for the characterization of millisecond time-scale chemical reactions and biochemical processes. TRMS allows for the simultaneous tracking of multiple reactants, intermediates and products with no chromophoric species required, high sensitivity and temporal resolution. The method has most recently been used for the characterization of several short-lived reaction intermediates in rapid chemical reactions. Most of the reactions that occur in living organisms are accelerated by enzymes, with pre-steady state kinetics only attainable using time-resolved methods. TRMS has been increasingly used to monitor the conversion of substrates to products and the resulting changes to the enzyme during catalytic turnover. Early events in protein folding systems have also been elucidated, along with the characterization of dynamics and transient secondary structures in intrinsically disordered proteins. In this review, we will highlight representative examples where TRMS has been applied to study these phenomena.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".