An Active Site Water Broadens Substrate Specificity in <i>S</i>-Ribosylhomocysteinase (LuxS): A Docking, MD, and QM/MM Study
Bibliographic record
Abstract
Type-2 quorum sensing (QS-2) is a cell-cell signaling process known to be used by a number of pathogenic bacteria and to play important roles in their population growth and virulence. S-ribosyl homocysteinase (LuxS) is a key enzyme in the formation of the signaling molecule of QS-2, autoinducer II (AI-2). In this study, substrate (S-ribosylhomocysteine: SRH) binding and possible initial reaction steps of its catalytic mechanism leading to formation of a putative 2-keto-SRH intermediate have been examined. Specifically, docking and MD simulations were used to gain insights into the structure of the active-site-bound substrate complex. An ONIOM QM/MM hybrid method was then used to elucidate the mechanism of the first stage of the enzyme catalytic process. It is shown that the substrate may bind within the active site when its ribosyl moiety is in the α- (α-SRH) or β-furanose (β-SRH) configuration or as a linear aldose (linear-SRH). The α-SRH complex is preferred, lying 47.5 kJ mol(-1) lower in energy than the next lowest energy initial complex β-SRH. However, the MD and QM/MM calculations indicate that an active site water stably locates within the active site and that it can facilitate ring-opening of either α-SRH or β-furanose, leading to formation of a common active-site-bound 2-keto-SRH intermediate, without the need to pass through a linear aldose SRH configuration. Hence, regardless of the ribose's configuration within the bound SRH substrate, LuxS is able to catalyze the conversion of SRH to a common 2-keto-SRH intermediate.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".