Functionally Important Conformational Sub‐States in Human Ribonuclease Family
Bibliographic record
Abstract
Eight members of the human pancreatic ribonuclease (RNase) family have diverse biological functions including angiogenesis and host defense (cytotoxic and anti‐pathogen activities) but share common structural scaffold and catalyze the hydrolysis of ribonucleic acid (RNA). However, the catalytic efficiency and rate of dynamics among these enzymes differ by more than a million (10 6 ) folds. Emerging evidence from other enzyme systems propose that internal motions drive the sampling of short‐lived minor population of conformations (called as sub‐states ) that can contain features to promote various steps during the function of an enzyme. Therefore, quantitatively characterizing the conformational sub‐states in the catalytic cycle of enzymes, including substrate binding, structural rearrangements leading to the transition state, product formation and product release will enable us to obtain detailed insights in the role of enzyme dynamics in catalysis by RNases. We have used a combination of theoretical modeling, computer simulations, steady state kinetics and NMR experiments. Our studies indicate that RNases superfamily can be classified into sub‐families with designated biological functions and conserved dynamical behavior. Further, computer simulations show diverse binding preferences across the human RNase family and the conformational sub‐states in various steps of the catalytic cycle differ among the members belonging to each of the sub‐family. Moreover, we observe a ~10 6 fold difference in the catalytic rates of the human RNases. Additionally, we are using hybrid QM/MM method to model the catalysis of the single stranded RNA that would enable us to identify the distinct conformational sub‐states sampled by each of the members of human RNase family in their respective minimum energy reaction pathway. Results show interesting differences in the conformational sub‐states in the members of human RNases family. This abstract is from the Experimental Biology 2019 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 | 0.000 |
| 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 | 0.000 |
| 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".