Elongation Factor Tu’s Nucleotide Binding Is Governed by a Thermodynamic Landscape Unique among Bacterial Translation Factors
Bibliographic record
Abstract
Molecular switches such as GTPases are powerful devices turning "on" or "off" biomolecular processes at the core of critical biological pathways. To develop molecular switches de novo, an intimate understanding of how they function is required. Here we investigate the thermodynamic parameters that define the nucleotide-dependent switch mechanism of elongation factor (EF) Tu as a prototypical molecular switch. EF-Tu alternates between GTP- and GDP-bound conformations during its functional cycle, representing the "on" and "off" states, respectively. We report for the first time that the activation barriers for nucleotide association are the same for both nucleotides, suggesting a guanosine nucleoside or ribose-first mechanism for nucleotide association. Additionally, molecular dynamics (MD) simulations indicate that enthalpic stabilization of GDP binding compared to GTP binding originates in the backbone hydrogen bonding network of EF-Tu. In contrast, binding of GTP to EF-Tu is entropically driven by the liberation of bound water during the GDP- to GTP-bound transition. GDP binding to the apo conformation of EF-Tu is both enthalpically and entropically favored, a feature unique among translational GTPases. This indicates that the apo conformation does not resemble the GDP-bound state. Finally, we show that antibiotics and single amino acid substitutions can be used to target specific structural elements in EF-Tu to redesign the thermodynamic landscape. These findings demonstrate how, through evolution, EF-Tu has fine-tuned the structural and dynamic features that define nucleotide binding, providing insight into how altering these properties could be exploited for protein engineering.
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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 | 0.001 | 0.001 |
| 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".