Penicillin-binding proteins: the master builders and breakers of bacterial cell walls and its interaction with β-lactam antibiotics

Dabhi, Milan; Patel, Rohit; Shah, Vidhi; Soni, Richa; Saraf, Meenu; Rawal, Rakesh; Goswami, Dweipayan

doi:10.1007/s42485-024-00135-x

Cited by 2 publications

(1 citation statement)

References 77 publications

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“…For instance, in Gram-positive bacteria, mutations in genes encoding penicillin-binding proteins (PBPs) can alter the structure of target proteins, reducing the antibiotic’s affinity for its binding site and rendering the bacterium resistant to its action. Similarly, in Gram-negative bacteria, mutations in genes encoding porins, the channels for antibiotic entry into the bacterial cell, can reduce the effectiveness of the antibiotic in crossing the outer membrane and reaching its site of action [ 18 ]. Mutations in genes involved in antibiotic transport and efflux mechanisms can influence the bacterium’s ability to absorb the antibiotic from the surrounding environment or to expel it once absorbed [ 19 ].…”

Section: Genetic Mutations Associated With Antibiotic Resistancementioning

confidence: 99%

Implications of Artificial Intelligence in Addressing Antimicrobial Resistance: Innovations, Global Challenges, and Healthcare’s Future

Branda,

Scarpa

2024

Antibiotics

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Antibiotic resistance poses a significant threat to global public health due to complex interactions between bacterial genetic factors and external influences such as antibiotic misuse. Artificial intelligence (AI) offers innovative strategies to address this crisis. For example, AI can analyze genomic data to detect resistance markers early on, enabling early interventions. In addition, AI-powered decision support systems can optimize antibiotic use by recommending the most effective treatments based on patient data and local resistance patterns. AI can accelerate drug discovery by predicting the efficacy of new compounds and identifying potential antibacterial agents. Although progress has been made, challenges persist, including data quality, model interpretability, and real-world implementation. A multidisciplinary approach that integrates AI with other emerging technologies, such as synthetic biology and nanomedicine, could pave the way for effective prevention and mitigation of antimicrobial resistance, preserving the efficacy of antibiotics for future generations.

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Section: Genetic Mutations Associated With Antibiotic Resistancementioning

confidence: 99%

Implications of Artificial Intelligence in Addressing Antimicrobial Resistance: Innovations, Global Challenges, and Healthcare’s Future

Branda,

Scarpa

2024

Antibiotics

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Cannabidiol Interactions with Outer Membrane Proteins in Salmonella Typhimurium LT2

Ibrahim,

Ndezure,

et al. 2024

Preprint

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Cannabidiol (CBD), the non-psychoactive component of the hemp plant has tremendous potential as a novel antimicrobial agent. This study aimed at understanding the interactions between CBD and the outer membrane proteins (OMPs) of Salmonella Typhimurium LT2. Employing in silico techniques, we analyzed the binding affinities, interaction dynamics, and drug-likeness of CBD with key OMPs such as OmpA, OmpC, OmpD, OmpF, OmpX, and NompC. The molecular docking results showed that CBD exhibits varying binding affinities across the OMPs, with OmpX and NompC showing the highest binding affinity of -6.6 kcal/mol and − 6.4 kcal/mol respectively, indicating strong and stable interactions. The results also revealed several key interactions such as hydrogen bonds, Pi-stacking, and hydrophobic interactions, playing crucial roles in the stability and specificity of these protein-ligand complexes. Notable interactions were identified in OmpA with a binding affinity of -5.9 kcal/mol involving hydrogen bonds at 3.2 Å and Pi-Sigma interactions at 3.4 Å. We included phylogenetic analysis of fifty different strains of Salmonella Typhimurium, and we observed high conservation levels among the OMPs, with a sequence similarity threshold of 90%. This high conservation underscores the potential of CBD to act as a broad-spectrum antimicrobial agent. Furthermore, our comparative structural analysis revealed both conserved and variable regions within the OMPs, highlighting the significance of targeting these regions to mitigate resistance development. Using KEGG Pathway analysis, we analyzed OmpC and OmpF, given their roles in nutrient transport and permeability. The disruption of these pathways by CBD binding could impair the bacteria’s ability to manage environmental stresses and evade host immune responses. Beta-lactam resistance pathway analysis was also considered, we observed that CBD could potentially disrupt resistance mechanisms by binding to OMPs, enhancing the efficacy of existing antimicrobial treatments. In conclusion, our findings suggest that CBD, through its interaction with critical OMPs, has the potential to serve as a potent antimicrobial agent against Salmonella Typhimurium LT2. These findings lay the foundation for further studies of CBD as a novel therapeutic agent in combating bacterial infections and addressing the global challenge of antibiotic resistance.

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Penicillin-binding proteins: the master builders and breakers of bacterial cell walls and its interaction with β-lactam antibiotics

Cited by 2 publications

References 77 publications

Implications of Artificial Intelligence in Addressing Antimicrobial Resistance: Innovations, Global Challenges, and Healthcare’s Future

Implications of Artificial Intelligence in Addressing Antimicrobial Resistance: Innovations, Global Challenges, and Healthcare’s Future

Cannabidiol Interactions with Outer Membrane Proteins in Salmonella Typhimurium LT2

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