Temperature-dependent hydrogen deuterium exchange shows impact of analog binding on adenosine deaminase flexibility but not embedded thermal networks
Bibliographic record
Abstract
The analysis of hydrogen deuterium exchange by mass spectrometry as a function of temperature and mutation has emerged as a generic and efficient tool for the spatial resolution of protein networks that are proposed to function in the thermal activation of catalysis. In this work, we extend temperature-dependent hydrogen deuterium exchange from apo-enzyme structures to protein–ligand complexes. Using adenosine deaminase as a prototype, we compared the impacts of a substrate analog (1-deaza-adenosine) and a very tight-binding inhibitor/transition state analog (pentostatin) at single and multiple temperatures. At a single temperature, we observed different hydrogen deuterium exchange-mass spectrometry properties for the two ligands, as expected from their 10 6 -fold differences in strength of binding. By contrast, analogous patterns for temperature-dependent hydrogen deuterium exchange mass spectrometry emerge in the presence of both 1-deaza-adenosine and pentostatin, indicating similar impacts of either ligand on the enthalpic barriers for local protein unfolding. We extended temperature-dependent hydrogen deuterium exchange to a function-altering mutant of adenosine deaminase in the presence of pentostatin and revealed a protein thermal network that is highly similar to that previously reported for the apo-enzyme (Gao et al., 2020, JACS 142, 19936-19949). Finally, we discuss the differential impacts of pentostatin binding on overall protein flexibility versus site-specific thermal transfer pathways in the context of models for substrate-induced changes to a distributed protein conformational landscape that act in synergy with embedded protein thermal networks to achieve efficient catalysis.
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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) | 0.001 | 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".