Structure-guided approaches to targeting stress responses in human fungal pathogens
Bibliographic record
Abstract
Treating fungal infections is challenging, in part due to the emergence of resistance to our limited arsenal of antifungal agents, necessitating the development of novel therapeutic options. Whereas conventional antifungal strategies target proteins or cellular components essential for fungal growth, an attractive alternative strategy involves targeting proteins that regulate fungal virulence or antifungal drug resistance, such as regulators of fungal stress responses. Stress response networks enable fungi to adapt, grow, and cause disease in humans and include regulators that are highly conserved across eukaryotes as well as those that are fungal-specific. This review highlights recent developments in elucidating crystal structures of fungal stress response regulators and emphasizes how this knowledge can guide the design of fungal-selective inhibitors. We focus on the progress that has been made with highly conserved regulators, including the molecular chaperone Hsp90, the protein phosphatase calcineurin, and the small GTPase Ras1, as well as with divergent stress response regulators, including the cell wall kinase Yck2 and trehalose synthases. Exploring structures of these important fungal stress regulators will accelerate the design of selective antifungals that can be deployed to combat life-threatening fungal diseases.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".