Molecular insights into the inhibition of plasmepsins by HIV-1 protease inhibitors: Implications for antimalarial drug discovery
Bibliographic record
Abstract
1. Abstract Malaria is a deadly disease, and the worldwide emergence of drug resistance in the Plasmodium parasites demands the development of novel and potent antimalarials. HIV-1 protease inhibitors (HIV-1 PIs) alleviate the Plasmodium pathogenesis during malaria/HIV-1 co-infection plausibly by inhibiting vacuolar plasmepsins (PMs), the hemoglobin degrading proteases from P. falciparum . All five FDA-approved HIV-1 PIs tested against PMII exhibit the K i values in the low micromolar range of which RTV and LPV display the highest inhibition activity. Both inhibitors impede in vitro growth of P. falciparum at low micromolar IC 50 values. We report the first crystal structures of PMII complexed with RTV and LPV that reveal the binding mode and interactions of the inhibitors in the active site as well as elucidate the mechanism underlying their differential potency. The conformational flexibility of the P4 group in RTV allows it to explore multiple regions of the S4 pocket. The present study establishes vacuolar PMs as potential drug targets of HIV-1 PIs. The molecular details explaining the inhibitory mechanism of HIV-1 PIs might be crucial in designing novel and potent antimalarial analogs. This work strengthens the prospect of drug repurposing as an alternative strategy towards antimalarial treatments and provides an opportunity to tackle malaria and HIV-1 co-infection.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".