Hydrogen–Deuterium Exchange within Adenosine Deaminase, a TIM Barrel Hydrolase, Identifies Networks for Thermal Activation of Catalysis
Bibliographic record
Abstract
Proteins are intrinsically flexible macromolecules that undergo internal motions with time scales spanning femtoseconds to milliseconds. These fluctuations are implicated in the optimization of reaction barriers for enzyme catalyzed reactions. Time, temperature, and mutation dependent hydrogen–deuterium exchange coupled to mass spectrometry (HDX-MS) has been previously employed to identify spatially resolved, catalysis-linked dynamical regions of enzymes. We now extend this technique to pursue the correlation of protein flexibility and chemical reactivity within the diverse and widespread TIM barrel proteins, targeting murine adenosine deaminase (mADA) that catalyzes the irreversible deamination of adenosine to inosine and ammonia. Following a structure–function analysis of rate and activation energy for a series of mutations at a second sphere phenylalanine positioned in proximity to the bound substrate, the catalytically impaired Phe61Ala with an elevated activation energy ( E a = 7.5 kcal/mol) and the wild type (WT) mADA ( E a = 5.0 kcal/mol) were selected for HDX-MS experiments. The rate constants and activation energies of HDX for peptide segments are quantified and used to assess mutation-dependent changes in local and distal motions. Analyses reveal that approximately 50% of the protein sequence of Phe61Ala displays significant changes in the temperature dependence of HDX behaviors, with the dominant change being an increase in protein flexibility. Utilizing Phe61Ile, which displays the same activation energy for k cat as WT, as a control, we were able to further refine the HDX analysis, highlighting the regions of mADA that are altered in a functionally relevant manner. A map is constructed that illustrates the regions of protein that are proposed to be essential for the thermal optimization of active site configurations that dominate reaction barrier crossings in the native enzyme.
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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 | 0.000 | 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 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".