Therapeutic potential of antimicrobial peptides against pathogenic protozoa

Hagemann, Corina Lobato; Macedo, Alexandre José; Tasca, Tiana

doi:10.1007/s00436-024-08133-0

Cited by 3 publications

(1 citation statement)

References 102 publications

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“…Similarly, there has been an increase in the number of studies related to the search for new therapies against Chagas disease. The use of antimicrobial peptides, such as mastoparan or crotalicidin ( Hagemann et al, 2024 ), tryptophan derivatives, such as melatonin ( Manful et al, 2024 ), or pharmacological repositioning, cholesterol-lowering treatments or antihistaminics, have shown good results in the treatment of Trypanosoma cruzi ( Porta et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Acrylonitrile derivatives: In vitro activity and mechanism of cell death induction against Trypanosoma cruzi and Leishmania amazonensis

Bethencourt-Estrella,

Delgado-Hernández,

López-Arencibia

et al. 2024

International Journal for Parasitology: Drugs and Drug Resistan

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Section: Introductionmentioning

confidence: 99%

Acrylonitrile derivatives: In vitro activity and mechanism of cell death induction against Trypanosoma cruzi and Leishmania amazonensis

Bethencourt-Estrella,

Delgado-Hernández,

López-Arencibia

et al. 2024

International Journal for Parasitology: Drugs and Drug Resistan

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Cloning, expression, and purification of an α-carbonic anhydrase from Toxoplasma gondii to unveil its kinetic parameters and anion inhibition profile.

De Luca,

Giovannuzzi,

Capasso

et al. 2024

Journal of Enzyme Inhibition and Medicinal Chemistry

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Machine Learning-Driven Discovery and Evaluation of Antimicrobial Peptides from Crassostrea gigas Mucus Proteome

Song,

Liu,

Jin

et al. 2024

Marine Drugs

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Marine antimicrobial peptides (AMPs) represent a promising source for combating infections, especially against antibiotic-resistant pathogens and traditionally challenging infections. However, traditional drug discovery methods face challenges such as time-consuming processes and high costs. Therefore, leveraging machine learning techniques to expedite the discovery of marine AMPs holds significant promise. Our study applies machine learning to develop marine AMPs, focusing on Crassostrea gigas mucus rich in antimicrobial components. We conducted proteome sequencing of C. gigas mucous proteins, used the iAMPCN model for peptide activity prediction, and evaluated the antimicrobial, hemolytic, and cytotoxic capabilities of six peptides. Proteomic analysis identified 4490 proteins, yielding about 43,000 peptides (8–50 amino acids). Peptide ranking based on length, hydrophobicity, and charge assessed antimicrobial potential, predicting 23 biological activities. Six peptides, distinguished by their high relative scores and promising biological activities, were chosen for bactericidal assay. Peptides P1 to P4 showed antimicrobial activity against E. coli, with P2 and P4 being particularly effective. All peptides inhibited S. aureus growth. P2 and P4 also exhibited significant anti-V. parahaemolyticus effects, while P1 and P3 were non-cytotoxic to HEK293T cells at detectable concentrations. Minimal hemolytic activity was observed for all peptides even at high concentrations. This study highlights the potent antimicrobial properties of naturally occurring oyster mucus peptides, emphasizing their low cytotoxicity and lack of hemolytic effects. Machine learning accurately predicted biological activity, showcasing its potential in peptide drug discovery.

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Therapeutic potential of antimicrobial peptides against pathogenic protozoa

Cited by 3 publications

References 102 publications

Acrylonitrile derivatives: In vitro activity and mechanism of cell death induction against Trypanosoma cruzi and Leishmania amazonensis

Acrylonitrile derivatives: In vitro activity and mechanism of cell death induction against Trypanosoma cruzi and Leishmania amazonensis

Cloning, expression, and purification of an α-carbonic anhydrase from Toxoplasma gondii to unveil its kinetic parameters and anion inhibition profile.

Machine Learning-Driven Discovery and Evaluation of Antimicrobial Peptides from Crassostrea gigas Mucus Proteome

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