Measuring Rapid Time-Scale Reaction Kinetics Using Isothermal Titration Calorimetry
Bibliographic record
Abstract
Isothermal titration calorimetry (ITC) is a powerful tool for acquiring both thermodynamic and kinetic data for biological interactions including molecular recognition and enzymatic catalysis. ITC-based kinetics measurements typically focus on reactions taking place over long time scales (tens of minutes or hours) in order to avoid complications due to the finite length of time needed detect heat flow in the calorimeter cell. While progress has been made toward analyzing more rapid reaction kinetics by ITC, the capabilities and limitations of this approach have not been thoroughly tested to date. Here, we report that the time resolution of commercial instruments is on the order of 0.2 s or less. We successfully performed rapid ITC kinetics assays with durations of just tens of seconds using the enzyme trypsin. This is substantially shorter than previous ITC enzyme measurements. However, we noticed that for short reaction durations, standard assumptions regarding the ITC instrument response led to significant deviations between calculated and measured ITC peak shapes. To address this issue, we developed an ITC empirical response model (ITC-ERM) that quantitatively reproduces ITC peak shapes for all reaction durations. Applying the ITC-ERM approach to another enzyme (prolyl oligopeptidase), we unexpectedly discovered non-Michaelis-Menten kinetics in short time-scale measurements that are absent in more typical long time-scale experiments and are obscured in short time-scale experiments when standard assumptions regarding the instrument response are made. This highlights the potential of ITC measurements of rapid time scale kinetics in conjunction with the ITC-ERM approach to shed new light on biological dynamics.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".