Water Networks and Allosteric Scaffolds ‐ What Does Water Have to do with Protein Dynamics?
Bibliographic record
Abstract
The thermophilic enzyme Fluoroacetate Dehalogenase (FAcD) has been well characterized by crystallography and NMR. FAcD (RPA1163) is a homodimer alpha/beta hydrolase that breaks down haloacetates (including fluoroacetate, iodoacetate, and chloroacetate) into glycolate and a halogen ion. FAcD achieves this by half‐of‐sites activity, where the homodimer adopts a rare asymmetric state that is primed to capture substrate which must then diffuse along an 11 Å cavity before engaging the active site and adopting the Michaelis intermediate. This triggers release of ~30 bound water molecules, thereby entropically favouring the forward reaction. FAcD is thus an ideal model system to pursue paradigm shifting questions regarding allostery, interprotomer communication, and the role of hydrogen bonded water networks in catalysis ‐ all from an ensemble perspective. Here we examine how deuterium water (D 2 O) changes the dynamic and allosteric mechanisms of this system. Compared to water, D 2 O results in an increase in viscosity and has stronger hydrogen bonds. Through 19F NMR kinetic experiments we determine that despite the higher viscosity enzyme activity is increased in the D 2 O environment. Additionally, 19F NMR experiments reveal increased dynamics in D 2 O. This provides key insight into discrete hydrogen‐bonded water networks that are key to stabilization of allosteric processes associated with the reaction coordinate. Support or Funding Information Natural Sciences and Engineering Research Council (NSERC)
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".