Single-Molecule Kinetic Exploration of Functional Substates in an Evolving Phosphotriesterase
Bibliographic record
Abstract
Enzymes can exist in multiple stable functional states or sub-states, revealing substantial molecule-to-molecule heterogeneity in catalytic rates even within populations expressed from a clonal gene. Increasing evidence suggests that biological systems have evolved to exploit these functional sub-states to regulate reaction rates and enzyme multi-functionality or promiscuity. Thus, understanding such heterogeneity in evolutionary dynamics is central to elucidating the mechanisms underlying enzyme adaptability and evolvability. To address this, we analyze the evolutionary changes in functional sub-states using single-molecule kinetics. We measure the functional sub-states of wild-type phosphotriesterase (PTE) and 18 evolved variants spanning a trajectory from native PTE activity to promiscuous arylesterase function. This is further supported by an investigation into the conformational sub-states of selected variants using molecular dynamics simulations. Our results reveal that functional optimization is tightly coupled with the redistribution of underlying functional and conformational sub-states, particularly involving the open and closed dynamics of functionally essential loop 7. These findings provide direct evidence that enzyme evolution involves coordinated shifts in conformational heterogeneity and its functional manifestations, together shaping enzyme function.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".