Targeting tRNA-synthetase interactions towards novel therapeutic discovery against eukaryotic pathogens
Bibliographic record
Abstract
The development of chemotherapies against eukaryotic pathogens is especially challenging because of both the evolutionary conservation of drug targets between host and parasite, and the evolution of strain-dependent drug resistance. There is a strong need for new nontoxic drugs with broad-spectrum activity against trypanosome parasites such as Leishmania and Trypanosoma. A relatively untested approach is to target macromolecular interactions in parasites rather than small molecular interactions, under the hypothesis that the features specifying macromolecular interactions diverge more rapidly through coevolution. We computed tRNA Class-Informative Features in humans and independently in eight distinct clades of trypanosomes, identifying parasite-specific informative features, including base pairs and base mis-pairs, that are broadly conserved over approximately 250 million years of trypanosome evolution. Validating these observations, we demonstrated biochemically that tRNA:aminoacyl-tRNA synthetase (aaRS) interactions are a promising target for anti-trypanosomal drug discovery. From a marine natural products extract library, we identified several fractions with inhibitory activity toward Leishmania major alanyl-tRNA synthetase (AlaRS) but no activity against the human homolog. These marine natural products extracts showed cross-reactivity towards Trypanosoma cruzi AlaRS indicating the broad-spectrum potential of our network predictions. We also identified Leishmania major threonyl-tRNA synthetase (ThrRS) inhibitors from the same library. We discuss why chemotherapies targeting multiple aaRSs should be less prone to the evolution of resistance than monotherapeutic or synergistic combination chemotherapies targeting only one aaRS.
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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) | 0.001 | 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".