This review underscores the potential of BamA as a next-generation antibacterial target and outlines future directions for the development of effective therapeutics against multidrug-resistant Gram-negative bacteria.
Abstract
The rapid emergence of antimicrobial resistance (AMR) among Gram-negative pathogens has intensified the need for novel therapeutic strategies targeting previously underexploited bacterial systems. The β-barrel assembly machinery (BAM) complex, and particularly its central component BamA, has emerged as a highly promising antibacterial target due to its essential role in outer membrane protein (OMP) biogenesis and its accessibility at the bacterial surface. This review provides a comprehensive analysis of BamA structure, function, and mechanism, with emphasis on the dynamic lateral gate and β-augmentation processes that drive OMP folding and insertion. Recent advances in structural biology have enabled detailed characterization of BamA conformational states, offering critical insights into its druggability. We further present an integrated overview of the current BamA inhibitor landscape, including macrocyclic peptides, peptidomimetics, and small molecules, highlighting key structure–activity relationships (SAR) and binding modes. In addition, mechanisms of resistance, challenges associated with outer membrane (OM) permeability, and pharmacokinetic limitations are critically discussed. Particular attention is given to emerging design strategies that integrate conformational targeting, β-strand mimicry, and permeability optimization. Collectively, this review underscores the potential of BamA as a next-generation antibacterial target and outlines future directions for the development of effective therapeutics against multidrug-resistant Gram-negative bacteria.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
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It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.