Conformational Motions Impacting Function in an Enzyme Superfamily
Bibliographic record
Abstract
Correlation between conformational dynamics and enzyme function has been well established for discrete enzyme systems. However, approaches for characterizing dynamical properties across diverse sequence homologs within a family and their correlation with enzyme activity remain challenging. Members of the pancreatic‐type ribonuclease (ptRNase) superfamily share similarities in structure and fold, but display large variations in conformational dynamics, catalytic efficiencies, and tissue specific biological activities, making them ideal model systems for probing the relationship between conformational motions and function. As a step towards determining the relationship between dynamics, catalytic mechanism and catalytic efficiency for various members of this broad vertebrate family, we have performed the systematic characterization of the intrinsic dynamics of over twenty RNases with experimentally solved structures over a wide range of time‐scales by integrating molecular dynamics simulations and NMR relaxation dispersion experiments. Our results show distinct patterns of dynamical variations between the canonical RNases clustered on taxonomic groups. We show that conformational motions on the catalytically relevant micro‐ to milli‐second timescale are significantly different for RNases sharing a common fold. Interestingly, sequences sharing similar conformational exchange on the catalytic timescale also share similar biological functions. These results suggest that selective pressure for the conservation of specific atomic‐scale dynamical behaviors, among other factors, may potentially impact distinct biological functions within the same fold. Further experiments are required to characterize this correlation between conserved dynamical properties and biological 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 | 0.001 |
| 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 | 0.000 | 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".