A self-immolative Kdn-glycoside substrate enables high-throughput screening for inhibitors of Kdnases
Bibliographic record
Abstract
Aspergillus fumigatus, a filamentous fungus, is an opportunistic pathogen and the major causative agent of the often-fatal disease, invasive aspergillosis (IA). Current treatments for IA are limited due to their high toxicity and/or the emergence of drug resistance; therefore, a need exists for the development of new therapeutics to treat IA. The Kdnase produced by A. fumigatus plays a vital role in maintaining cell wall integrity. As there are no known Kdnases in humans, developing inhibitors of Kdnase from this fungal pathogen is a promising therapeutic approach. The rapid testing of enzymatic activity in a high-throughput screen of large chemical libraries can be an efficient way to find new small molecule lead compounds. Herein we show that a Kdn glycoside with a self-immolative cleavable aglycon is a practical and efficient substrate for a high throughput assay to identify Kdnase inhibitors. We optimized the activity assay and screened over 27,000 compounds from two bioactive chemical libraries as potential inhibitors, and we compared the hit compounds' potency towards Aspergillus terreus and Trichophyton rubrum Kdnases, two other fungal Kdnases. We validated a number of hits and these small molecules are potential leads for the development of novel therapeutics to treat invasive aspergillosis.
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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) | 0.000 | 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".