Catalytic Proficiency of Ubiquitin Conjugation Enzymes: Balancing p<i>K</i><sub>a</sub> Suppression, Entropy, and Electrostatics
Bibliographic record
Abstract
Biological organisms orchestrate coordinated responses to external stimuli through temporal fluctuations in protein-protein interaction networks using molecular mechanisms such as the synthesis and recognition of polyubiquitin (polyUb) chains on signaling adaptor proteins. One of the pivotal chemical steps in ubiquitination involves reaction of a lysine amino group with a thioester group on an activated E2, or ubiquitin conjugation enzyme, to form an amide bond between Ub and a target protein. In this study, we demonstrate a nominal 14-fold range for the rate of the chemical step, k(cat), catalyzed by different E2 enzymes using non-steady-state, single-turnover assays. However, the observed range for k(cat) is as large as ∼100-fold for steady-state, single-turnover assays. Biochemical assays were used in combination with measurement of the underlying protein-protein interaction kinetics using NMR line-shape and ZZ-exchange analyses to determine the rate of polyUb chain synthesis catalyzed by the heterodimeric E2 enzyme Ubc13-Mms2. Modest variations in substrate affinity and k(cat) can achieve functional diversity in E2 mechanism, thereby influencing the biological outcomes of polyubiquitination. E2 enzymes achieve reaction rate enhancements through electrostatic effects such as suppression of substrate lysine pK(a) and stabilization of transition states by the preorganized, polar enzyme active site as well as the entropic effects of binding. Importantly, modestly proficient enzymes such as E2s maintain the ability to tune reaction rates; this may confer a biological advantage for achieving specificity in the diverse cellular roles for which these enzymes are involved.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".