CONFORMATIONAL FLUCTUATIONS RELATED TO CATALYSIS IN HUMAN RIBONUCLEASE SUPERFAMILY
Bibliographic record
Abstract
The high catalytic efficiency of enzymes is a result of structural and dynamical effects. The role of enzyme structure in catalysis has been understood for some time. The enzymes of the ribonuclease A (RNase) family possess identical or similar active site residues and conserved fold architecture. However, their catalytic efficiency differs by 10 5 –10 6 fold. Moreover, the rate of dynamics among the RNases ranges from microsecond to second time‐scale, a factor 10 6 difference. Recent investigations provide insights into the role of conformational fluctuations (dynamics at multiple time‐scales) in enzyme catalysis. The fluctuations allow enzymes to sample inter‐converting conformations (called as conformational sub‐states ). These conformational sub‐states are proposed to contain features that can promote the function of an enzyme. Therefore, correlating the role of time dependent dynamics in sampling functionally relevant conformational sub‐states would enable a better understanding of the catalytic mechanism of the various RNases. NMR relaxation dispersion experiments show distinct patterns of dynamical variations among the RNases clustered into sub‐families having different biological functions within the same fold. A combination of theoretical modeling, computer simulations and higher order statistics has enabled the identification of conformational sub‐states that regulate the mechanism substrate binding, catalysis and product removal in representative members of each sub‐family found in the human genome. The representative members of each sub‐family have diverse conformational sub‐states associated with each step of the catalytic cycle suggesting a possible correlation between dynamics and function. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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) | 0.001 | 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".