Structure-Based Virtual Screening and Pharmacokinetic Prioritization of Natural Compounds with Predicted Binding to the Endothelial Protein C Receptor in Cerebral Malaria

The Endothelial Protein C Receptor (EPCR) is a transmembrane glycoprotein expressed on vascular endothelial cells. In cerebral malaria, binding of Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) variants containing CIDRα1 domains can interfere with these protective interactions. We used virtual screening, molecular docking, MD simulations, and normal mode analysis to assess 87,489 natural compounds from the ZINC database. ZINC04084408, ZINC04084805, and ZINC04085245 were prioritized as candidate EPCR-binding compounds through the computational workflow. Their predicted poses generate hypotheses for experimental evaluation but do not establish inhibition of PfEMP1–EPCR interaction, effects on protein C activation or activated protein C (APC)-mediated signaling, or therapeutic efficacy. Following AutoDock Vina screening, ten compounds shared the lowest reported docking score of −13.9 kcal/mol and were advanced for detailed AutoDock 4.1 docking and pharmacokinetic evaluation. ZINC04084408, ZINC04084805, and ZINC04085245 were subsequently prioritized based on selected physicochemical and pharmacokinetic criteria. These compounds were predicted to occupy the hydrophobic ligand-binding pocket of EPCR. However, the calculations do not demonstrate selective EPCR binding or competitive inhibition of the PfEMP1–EPCR interaction. The predicted low aqueous solubility and model-dependent toxicity results represent important development limitations. The three compounds remain computationally prioritized candidates requiring experimental binding, toxicological, and functional validation.

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Journal
Scientia Pharmaceutica
Published
2026-11-04
DOI
https://doi.org/10.3390/scipharm94040104
Primary Topic
Computational Drug Discovery Methods
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article
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article

Structure-Based Virtual Screening and Pharmacokinetic Prioritization of Natural Compounds with Predicted Binding to the Endothelial Protein C Receptor in Cerebral Malaria

Subhomoi Borkotoky, Savitri Tiwari, Reetesh Kumar, Shivani Malik
Scientia Pharmaceutica
Computational Drug Discovery Methods
article

Structure-Based Virtual Screening and Pharmacokinetic Prioritization of Natural Compounds with Predicted Binding to the Endothelial Protein C Receptor in Cerebral Malaria

Subhomoi Borkotoky, Savitri Tiwari, Reetesh Kumar, Shivani Malik
article en

Abstract

The Endothelial Protein C Receptor (EPCR) is a transmembrane glycoprotein expressed on vascular endothelial cells. In cerebral malaria, binding of Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) variants containing CIDRα1 domains can interfere with these protective interactions. We used virtual screening, molecular docking, MD simulations, and normal mode analysis to assess 87,489 natural compounds from the ZINC database. ZINC04084408, ZINC04084805, and ZINC04085245 were prioritized as candidate EPCR-binding compounds through the computational workflow. Their predicted poses generate hypotheses for experimental evaluation but do not establish inhibition of PfEMP1–EPCR interaction, effects on protein C activation or activated protein C (APC)-mediated signaling, or therapeutic efficacy. Following AutoDock Vina screening, ten compounds shared the lowest reported docking score of −13.9 kcal/mol and were advanced for detailed AutoDock 4.1 docking and pharmacokinetic evaluation. ZINC04084408, ZINC04084805, and ZINC04085245 were subsequently prioritized based on selected physicochemical and pharmacokinetic criteria. These compounds were predicted to occupy the hydrophobic ligand-binding pocket of EPCR. However, the calculations do not demonstrate selective EPCR binding or competitive inhibition of the PfEMP1–EPCR interaction. The predicted low aqueous solubility and model-dependent toxicity results represent important development limitations. The three compounds remain computationally prioritized candidates requiring experimental binding, toxicological, and functional validation.

Scientia PharmaceuticaVol. 94(4)
Galgotias University (IN), Invertis University (IN)
Openalex Percentile: Top 16%
Computational Drug Discovery Methods
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