A Structural Perspective on the Temperature-dependent Activity of Enzymes
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.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| 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".