Chemically EngineeredPeptide Efficiently Blocks MalariaParasite Entry into Red Blood Cells

Chemical peptide engineering, enabled by residue insertion, backbone cyclization, and incorporation of an additional disulfide bond, led to a unique cyclic peptide that efficiently inhibits the invasion of red blood cells by malaria parasites. The engineered peptide exhibits a 20-fold enhanced affinity toward its receptor (<i>Pf</i>AMA1) compared to the native peptide ligand (<i>Pf</i>RON2), as determined by surface plasmon resonance. In-vitro parasite growth inhibition assay revealed augmented potency of the engineered peptide. The structure of the <i>Pf</i>AMA1-cyclic peptide complex, predicted by the deep learning-based structure prediction tool ColabFold-AlphaFold2 with protein-cyclic peptide complex offset, provided valuable insights into the observed activity of the peptide analogs. Rational editing of the peptide backbone and side chain described here proved to be an effective strategy for designing peptide-based inhibitors to interfere with disease-related protein–protein interactions.

Publication Details

Journal
Figshare
Published
2025-03-10
DOI
https://doi.org/10.1021/acs.biochem.4c00465.s001
Primary Topic
Plant pathogens and resistance mechanisms
Type
article
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article

Chemically EngineeredPeptide Efficiently Blocks MalariaParasite Entry into Red Blood Cells

Figshare
Plant pathogens and resistance mechanisms
article

Chemically EngineeredPeptide Efficiently Blocks MalariaParasite Entry into Red Blood Cells

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Abstract

Chemical peptide engineering, enabled by residue insertion, backbone cyclization, and incorporation of an additional disulfide bond, led to a unique cyclic peptide that efficiently inhibits the invasion of red blood cells by malaria parasites. The engineered peptide exhibits a 20-fold enhanced affinity toward its receptor (<i>Pf</i>AMA1) compared to the native peptide ligand (<i>Pf</i>RON2), as determined by surface plasmon resonance. In-vitro parasite growth inhibition assay revealed augmented potency of the engineered peptide. The structure of the <i>Pf</i>AMA1-cyclic peptide complex, predicted by the deep learning-based structure prediction tool ColabFold-AlphaFold2 with protein-cyclic peptide complex offset, provided valuable insights into the observed activity of the peptide analogs. Rational editing of the peptide backbone and side chain described here proved to be an effective strategy for designing peptide-based inhibitors to interfere with disease-related protein–protein interactions.

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Plant pathogens and resistance mechanisms
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