Indel‐based targeting of essential proteins in human pathogens that have close host orthologue(s): Discovery of selective inhibitors for<i>Leishmania donovani</i>elongation factor‐1α
Bibliographic record
Abstract
We propose a novel strategy for selective targeting of essential pathogen proteins that contain sizable indels (insertions/deletions) in their sequences compared with their host orthologues. This approach has been tested on elongation factor-1alpha (EF-1alpha) from the protozoan pathogen Leishmania donovani. Leishmania EF-1alpha is 82% identical to the corresponding human orthologue, but possesses a 12 aminoacid sequence deletion compared with human EF-1alpha. We used this indel-differentiated region to design small molecules that selectively bind to leishmania EF-1alpha and not to the human protein. Three unrelated molecules were identified with the capacity to inhibit protein synthesis in leishmania by up to 75% while exhibiting no effect on human protein translation. These candidates may serve as prototypes for future development of antiprotozoan therapeutics. More generally, these findings provide a basis for a novel drug design platform. This platform targets essential pathogen proteins that are highly conserved across species, and consequently would not typically be considered to be conventional drug targets. We anticipate that such indel-directed targeting of essential proteins in microbial pathogens may help address the growing problem of antibiotic resistance.
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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) | 0.000 | 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 | 0.001 |
| 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".