Computational discovery of microalgal metabolites as dual-target inhibitors against Plasmodium falciparum glutathione S-transferase and apical membrane antigen 1 for resistance-circumventing antimalarial therapy
Bibliographic record
Abstract
Malaria remains a significant global health problem; with the potential for developing resistance to conventional antimalarials justifying novel therapeutic approaches. This study investigates the potential of microalgal metabolites as dual-inhibitors of Plasmodium falciparum glutathione S-transferase ( Pf GST) and apical membrane antigen 1 (AMA1); two of the most important proteins in parasite survival and host cell invasion. By high-throughput molecular docking simulations; we studied binding energy distributions; conformational characteristics by principal component analysis; pharmacophoric and pharmacokinetic research with structure-activity relationships of its inhibitors. Our findings indicate various patterns of interactions: Pf GST exhibits a unimodal distribution of binding energy with a maximum at -7.2 kcal/mol; whereas AMA1 exhibits a bimodal distribution with minimum at -6.8 and − 8.3 kcal/mol; suggesting various mechanisms of binding. Specifically; platencin was among the most potent dual-inhibitors with binding energies of -7.30 kcal/mol to Pf GST and − 8.20 kcal/mol to AMA1. Pharmacophoric characteristics were found to be the hydrogen-bond acceptors; hydrophobic centers; and aromatic rings as determinants of dual-target activity; the optimal dual-target inhibitory potential occurring with compounds of balanced physicochemical properties. High-resolution molecular interaction mapping validated that while the two targets identify overlapping classes of interactions with the metabolites; Pf GST interaction is dominated by hydrophobic contacts and AMA1 exploits higher electrostatic complementarity and hydrogen-bonding networks. ADMET profiling also revealed favorable drug-likeness in dual-inhibitory compounds of intermediate molecular size (420–500 Da) and moderate lipophilicity (LogP 3–6). This study provides a structural basis for rationale design of antimalarial compounds from microalgal metabolites. Our findings confirm the conformational selection hypothesis; in which these compounds selectively bind to and stabilize protein conformations that inhibit parasite function; possibly circumventing known resistance mechanisms.
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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 | 0.001 |
| 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".