Inhibitor Assessment against the LpxC Enzyme of Antibiotic-resistant Acinetobacter baumannii Using Virtual Screening, Dynamics Simulation, and in vitro Assays.
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引用次数: 1
Abstract
Background: Bacterial resistance is currently a significant global public health problem. Acinetobacter baumannii has been ranked in the list of the World Health Organization as the most critical and priority pathogen for which new antibiotics are urgently needed. In this context, computational methods play a central role in the modern drug discovery process. The purpose of the current study was to identify new potential therapeutic molecules to neutralize MDR A. baumannii bacteria.
Methods: A total of 3686 proteins retrieved from the A. baumannii proteome were subjected to subtractive proteomic analysis to narrow down the spectrum of drug targets. The SWISS-MODEL server was used to perform a 3D homology model of the selected target protein. The SAVES server was used to evaluate the overall quality of the model. A dataset of 74500 analogues retrieved from the PubChem database was docked with LpxC using the AutoDock software.
Results: In this study, we predicted a putative new inhibitor for the Lpxc enzyme of A. baumannii. The LpxC enzyme was selected as the most appropriate drug target for A. baumannii. According to the virtual screening results, N-[(2S)-3-amino-1-(hydroxyamino)-1-oxopropan-2-yl]-4-(4-bromophenyl) benzamide (CS250) could be a promising drug candidate targeting the LpxC enzyme. This molecule shows polar interactions with six amino acids and non-polar interactions with eight other residues. In vitro experimental validation was performed through the inhibition assay.
Conclusion: To the best of our knowledge, this is the first study that suggests CS250 as a promising inhibitory molecule that can be exploited to target this gram-negative pathogen.
期刊介绍:
Molecular Informatics is a peer-reviewed, international forum for publication of high-quality, interdisciplinary research on all molecular aspects of bio/cheminformatics and computer-assisted molecular design. Molecular Informatics succeeded QSAR & Combinatorial Science in 2010.
Molecular Informatics presents methodological innovations that will lead to a deeper understanding of ligand-receptor interactions, macromolecular complexes, molecular networks, design concepts and processes that demonstrate how ideas and design concepts lead to molecules with a desired structure or function, preferably including experimental validation.
The journal''s scope includes but is not limited to the fields of drug discovery and chemical biology, protein and nucleic acid engineering and design, the design of nanomolecular structures, strategies for modeling of macromolecular assemblies, molecular networks and systems, pharmaco- and chemogenomics, computer-assisted screening strategies, as well as novel technologies for the de novo design of biologically active molecules. As a unique feature Molecular Informatics publishes so-called "Methods Corner" review-type articles which feature important technological concepts and advances within the scope of the journal.