Validation of Reactivity Descriptors to Assess the Aromatic Stacking within the Tyrosine Gate of FimH
Bibliographic record
Abstract
Antagonists of the FimH adhesin, a protein almost universally present at the extremity of type-1 fimbriae expressed by Escherichia coli, have been abundantly in the spotlight as alternative treatments of urinary tract infections. The antagonists function as bacterial antiadhesives through highly specific α-d-mannose binding in a charged and polar pocket at the tip of the FimH lectin domain and by the stacking of alkyl or aromatic moieties substituted on the mannose with two tyrosine residues (Tyr48 and Tyr137) at the entrance of the mannose-binding pocket. Using high-resolution crystal data, interaction energies are calculated for the different observed aromatic stacking modes between the tyrosines and the antagonist. The dispersion component of the interaction energy correlates with the observed electron density. The quantum chemical reactivity descriptors local hardness and polarizability were successfully validated as prediction tools for ligand affinity in the tyrosine gate of FimH and therefore have potential for rapid drug screening.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".