Identification and Validation of Compounds Targeting <i>Leishmania major</i> Leucyl-Aminopeptidase M17
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Leishmaniasis is a neglected tropical disease; there is currently no vaccine and treatment is reliant upon a handful of drugs suffering from multiple issues including toxicity and resistance. There is a critical need for development of new fit-for-purpose therapeutics, with reduced toxicity and targeting new mechanisms to overcome resistance. One enzyme meriting investigation as a potential drug target in Leishmania is M17 leucyl-aminopeptidase (LAP). Here, we aimed to chemically validate LAP as a drug target in L. major through identification of potent and selective inhibitors. Using RapidFire mass spectrometry, the compounds DDD00057570 and DDD00097924 were identified as selective inhibitors of recombinant Leishmania major LAP activity. Both compounds inhibited in vitro growth of L. major and L. donovani intracellular amastigotes, and overexpression of Lm LAP in L. major led to reduced susceptibility to DDD00057570 and DDD00097924, suggesting that these compounds specifically target Lm LAP. Thermal proteome profiling revealed that these inhibitors thermally stabilized two M17 LAPs, indicating that these compounds selectively bind to enzymes of this class. Additionally, the selectivity of the inhibitors to act on Lm LAP and not against the human ortholog was demonstrated, despite the high sequence similarities LAPs of this family share. Collectively, these data confirm Lm LAP as a promising therapeutic target for Leishmania spp. that can be selectively inhibited by drug-like small molecules.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".