Peptidases: promising antifungal targets of the human fungal pathogen, <i>Cryptococcus neoformans</i>
Bibliographic record
Abstract
Cryptococcus neoformans is a globally important fungal pathogen, primarily inflicting disease on immunocompromised individuals. The widespread use of antifungal agents in medicine and agriculture supports the development of antifungal resistance through evolution, and the emergence of new strains with intrinsic resistance drives the need for new therapeutics. For C. neoformans, the production of virulence factors, including extracellular peptidases (e.g., CnMpr-1 and May1) with mechanistic roles in tissue invasion and fungal survival, constitute approximately 2% of the fungal proteome and cover five classes of enzymes. Given their role in fungal virulence, peptidases represent promising targets for anti-virulence discovery in the development of new approaches against C. neoformans. Additionally, intracellular peptidases, which are involved in resistance mechanisms against current treatment options (e.g., azole drugs), as well as capsule biosynthesis and elaboration of virulence factors, present additional opportunities to combat the pathogen. In this review, we highlight key cryptococcal peptidases with defined or predicted roles in fungal virulence and assess sequence alignments against their human homologs. With this information, we define the feasibility of the select peptidases as “druggable” targets for inhibition, representing prospective therapeutic options against the deadly fungus.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".