The Tyrosine Gate as a Potential Entropic Lever in the Receptor-Binding Site of the Bacterial Adhesin FimH
Bibliographic record
Abstract
Uropathogenic Escherichia coli (UPEC) are the major causative agents of urinary tract infections. During infection, UPEC adhere to mannosylated glycoreceptors on the urothelium via the FimH adhesin located at the tip of type 1 pili. Synthetic FimH antiadhesives such as alkyl and phenyl α-D-mannopyranosides are thus ideal candidates for the chemical interception of this crucial step in pathogenesis. The crystal structures of the FimH lectin domain in its ligand-free form and in complexes with eight medium- and high-affinity mannopyranoside inhibitors are presented. The thermodynamic profiles of the FimH-inhibitor interactions indicate that the binding of FimH to α-D-mannopyranose is enthalpy-driven and has a negative entropic change. Addition of a hydrophobic aglycon influences the binding enthalpy and can induce a favorable entropic change. The alleviation of the entropic cost is at least in part explained by increased dynamics in the tyrosine gate (Tyr48 and Tyr137) of the FimH receptor-binding site upon binding of the ligand. Ligands with a phenyl group directly linked to the anomeric oxygen of α-D-mannose introduce the largest dynamics into the Tyr48 side chain, because conjugation with the anomeric oxygen of α-D-mannose forces the aromatic aglycon into a conformation that comes into close contact (≈2.65 Å) with Tyr48. A propargyl group in this position predetermines the orientation of the aglycon and significantly decreases affinity. FimH has the highest affinity for α-D-mannopyranosides substituted with hydrophobic aglycons that are compatible in shape and electrostatic properties to the tyrosine gate, such as heptyl α-D-mannose.
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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".