A Structural Perspective on the Temperature-dependent Activity of Enzymes
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Bibliographic record
Abstract
Enzymes utilize thermal energy to complete their catalytic cycle and are optimized to maximize reaction rates at biological temperatures. Enzyme-catalyzed reaction rates increase with temperature, but the temperature-dependent structural and dynamic changes contributing to increased turnover remain poorly understood. Here, we used multi-temperature X-ray crystallography to record structural changes from -20°C to 40°C of a mesophilic enzyme in complex with inhibitors mimicking substrate-, intermediate-, and product-bound states. These structures reveal dynamic changes to inhibitors, substrates, and catalytically relevant loop motifs as they increasingly populate competent conformations with increasing temperature. Multi- temperature kinetic data is often modelled using the Arrhenius equation with the assumption that a linear Arrhenius plot implies a temperature-independent energy landscape. Our structural data shows remodeling corresponding to a changing energy landscape even in temperature ranges where kinetic measurements show linear Arrhenius/Eyring behavior. Simple analysis indicates that linear Arrhenius/Eyring behavior can still be observed when the underlying activation energy (Ea) / enthalpy and entropy (ΔH and ΔS) vary with temperature. Even small temperature variations lead to large deviations in (apparent) Ea, ΔH and ΔS values derived from linear fits and may generate trends in fit parameters obtained from, e.g., families of related enzymes — such as apparent enthalpy-entropy compensation — that are disconnected from those of the underlying parameters. Our results showcase the application of temperature in near-atomic resolution structural studies to understand the dynamic nature of enzymes, reveal structural origins of rate-determining steps, and gives new evidence to suggest that the models and assumptions from previous eras may not apply to our modern physical framework of understanding.
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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