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1.
PLoS One ; 19(5): e0301292, 2024.
Article in English | MEDLINE | ID: mdl-38743671

ABSTRACT

Enterococcus faecalis, a Gram-positive bacterium, poses a significant clinical challenge owing to its intrinsic resistance to a broad spectrum of antibiotics, warranting urgent exploration of innovative therapeutic strategies. This study investigated the viability of phage therapy as an alternative intervention for antibiotic-resistant E. faecalis, with a specific emphasis on the comprehensive genomic analysis of bacteriophage SAM-E.f 12. The investigation involved whole-genome sequencing of SAM-E.f 12 using Illumina technology, resulting in a robust dataset for detailed genomic characterization. Bioinformatics analyses were employed to predict genes and assign functional annotations. The bacteriophage SAM-E.f 12, which belongs to the Siphoviridae family, exhibited substantial potential, with a burst size of 5.7 PFU/infected cells and a latent period of 20 min. Host range determination experiments demonstrated its effectiveness against clinical E. faecalis strains, positioning SAM-E.f 12 as a precise therapeutic agent. Stability assays underscore resilience across diverse environmental conditions. This study provides a comprehensive understanding of SAM-E.f 12 genomic composition, lytic lifecycle parameters, and practical applications, particularly its efficacy in murine wound models. These results emphasize the promising role of phage therapy, specifically its targeted approach against antibiotic-resistant E. faecalis strains. The nuanced insights derived from this research will contribute to the ongoing pursuit of efficacious phage therapies and offer valuable implications for addressing the clinical challenges associated with E. faecalis infections.


Subject(s)
Bacteriophages , Enterococcus faecalis , Genome, Viral , Enterococcus faecalis/virology , Enterococcus faecalis/genetics , Bacteriophages/genetics , Animals , Mice , Phage Therapy , Host Specificity/genetics , Gram-Positive Bacterial Infections/microbiology , Gram-Positive Bacterial Infections/therapy , Whole Genome Sequencing , Genomics/methods , Siphoviridae/genetics
2.
Article in English | MEDLINE | ID: mdl-38523540

ABSTRACT

BACKGROUND: Drug-resistant Staphylococcus aureus represents a substantial healthcare challenge worldwide, and its range of available therapeutic options continues to diminish progressively. Thus, this study aimed to identify potential inhibitors against FemA, a crucial protein involved in the cell wall biosynthesis of S. aureus. MATERIALS AND METHODS: The screening process involved a comprehensive structure-based virtual screening on the StreptomDB database to identify ligands with potential inhibitory effects on FemA using AutoDock Vina. The most desirable ligands with the highest binding affinity and pharmacokinetic properties were selected. Two ligands with the highest number of hydrogen bonds and hydrophobic interactions were further analyzed by molecular dynamics (MD) using the GROMACS version 2018 simulation package. RESULTS: Six H-donor conserved residues were selected as protein active sites, including Arg- 220, Tyr-38, Gln-154, Asn-73, Arg-74, and Thr-24. Through virtual screening, a total of nine compounds with the highest binding affinity to the FemA protein were identified. Frigocyclinone and C21H21N3O4 exhibited the highest binding affinity and demonstrated favorable pharmacokinetic properties. Molecular dynamics analysis of the FemA-ligand complexes further indicated desirable stability and reliability of complexes, reinforcing the potential efficacy of these ligands as inhibitors of FemA protein. CONCLUSION: Our findings suggest that Frigocyclinone and C21H21N3O4 are promising inhibitors of FemA in S. aureus. To further validate these computational results, experimental studies are planned to confirm the inhibitory effects of these compounds on various S. aureus strains. Combining computational screening with experimental validation contributes valuable insights to the field of drug discovery in comparison to the classical drug discovery approaches.

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