Glycosidase Inhibition: An Assessment of the Binding of 18 Putative Transition-State Mimics
Bibliographic record
Abstract
The inhibition of glycoside hydrolases, through transition-state mimicry, is important both as a probe of enzyme mechanism and in the continuing quest for new drugs, notably in the treatment of cancer, HIV, influenza, and diabetes. The high affinity with which these enzymes are known to bind the transition state provides a framework upon which to design potent inhibitors. Recent work [for example, Bülow, A. et al. J. Am. Chem. Soc. 2000, 122, 8567-8568; Zechel, D. L. et al. J. Am. Chem. Soc. 2003, 125, 14313-14323] has revealed quite confusing and counter-intuitive patterns of inhibition for a number of glycosidase inhibitors. Here we describe a synergistic approach for analysis of inhibitors with a single enzyme 'model system', the Thermotoga maritima family 1 beta-glucosidase, TmGH1. The pH dependence of enzyme activity and inhibition has been determined, structures of inhibitor complexes have been solved by X-ray crystallography, with data up to 1.65 A resolution, and isothermal titration calorimetry was used to establish the thermodynamic signature. This has allowed the characterization of 18 compounds, all putative transition-state mimics, in order to build an 'inhibition profile' that provides an insight into what governs binding. In contrast to our preconceptions, there is little correlation of inhibitor chemistry with the calorimetric dissection of thermodynamics. The ensemble of inhibitors shows strong enthalpy-entropy compensation, and the random distribution of similar inhibitors across the plot of DeltaH degrees a vs TDeltaS degrees a likely reflects the enormous contribution of solvation and desolvation effects on ligand binding.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".