Computational and biophysical approaches to protein–protein interaction inhibition of Plasmodium falciparum AMA1/RON2 complex
Bibliographic record
Abstract
Invasion of the red blood cell by Plasmodium falciparum parasites requires formation of an electron dense circumferential ring called the Moving Junction (MJ). The MJ is anchored by a high affinity complex of two parasite proteins: Apical Membrane Antigen 1 (PfAMA1) displayed on the surface of the parasite and Rhoptry Neck Protein 2 that is discharged from the parasite and imbedded in the membrane of the host cell. Structural studies of PfAMA1 revealed a conserved hydrophobic groove localized to the apical surface that coordinates RON2 and invasion inhibitory peptides. In the present work, we employed computational and biophysical methods to identify competitive P. falciparum AMA1-RON2 inhibitors with the goal of exploring the 'druggability' of this attractive antimalarial target. A virtual screen followed by molecular docking with the PfAMA1 crystal structure was performed using an eight million compound collection that included commercial molecules, the ChEMBL malaria library and approved drugs. The consensus approach resulted in the selection of inhibitor candidates. We also developed a fluorescence anisotropy assay using a modified inhibitory peptide to experimentally validate the ability of the selected compounds to inhibit the AMA1-RON2 interaction. Among those, we identified one compound that displayed significant inhibition. This study offers interesting clues to improve the throughput and reliability of screening for new drug leads.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".