Enzyme Kinetics by Isothermal Titration Calorimetry: Allostery, Inhibition, and Dynamics
Bibliographic record
Abstract
Isothermal titration calorimetry (ITC) involves accurately measuring the heat that is released or absorbed in real time when one solution is titrated into another. This technique is usually used to measure the thermodynamics of binding reactions. However, there is mounting interest in using it to measure reaction kinetics, particularly enzymatic catalysis. This application of ITC has been steadily growing for the past two decades, and the method is proving to be sensitive, generally applicable, and capable of providing information on enzyme activity that is difficult to obtain using traditional biochemical assays. This review aims to give a broad overview of the use of ITC to measure enzyme kinetics. It describes several different classes of ITC experiment, their strengths and weaknesses, and recent methodological advancements. A summary of applications in the literature is given and several examples where ITC has been used to investigate challenging aspects of enzyme behavior are presented in more detail. These include examples of allostery, where small-molecule binding outside the active site modulates activity. We describe the use of ITC to measure the strength, mode (i.e., competitive, uncompetitive, or mixed), and association and dissociation kinetics of enzyme inhibitors. Further, we provide examples of ITC applied to complex, heterogeneous mixtures, such as insoluble substrates and live cells. These studies exemplify the wide range of problems where ITC can provide answers, and illustrate the versatility of the technique and potential for future development and applications.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| 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".