Targeting dual substrate pockets of colistin resistance conferring MCR-1 of Escherichia coli with natural products: insights from high throughput virtual screening and molecular dynamics simulations.
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引用次数: 0
Abstract
Most Gram-negative multidrug-resistant bacterial infections, including those caused by E. coli, are treated with the last-line drug colistin. Resistance to colistin is primarily developed through the plasmid-mediated mcr-1 gene. MCR-1 is a zinc-dependent metalloprotein with two substrate binding sites: one for ethanolamine and another for lipid A. The two binding sites are approximately 12 Å apart. In this work, we explored the potential of natural compounds as inhibitors of MCR-1 activity using high-throughput virtual screening. Potential inhibitor candidates were selected on clustering of the top-scoring compounds and further validation by molecular dynamics simulations. Based on the binding scores and stability in MD simulations, the findings suggest three potential compounds (CNP0334463, CNP0338640, and CNP0239461) that can bind to MCR-1 straddling the two substrate pockets. These molecules belong to the chemical classes of organooxygen as well as benzene and substituted derivatives. The identified dual-site inhibitors have a higher affinity than those binding to individual substrate sites. Representative structures identified from the Gibbs free energy surface, based on the first two principal components, reveal a conformational switch between loop and β-sheet in functionally important regions. The dynamic cross-correlation matrix plot showed correlated and anti-correlated motions related to the conformational switch, zinc ion binding, and inhibitors occupying dual sites. Taken together, our results indicate that the design of dual-site inhibitors may be a novel approach to inhibit MCR-1 activity.
期刊介绍:
The Journal of Biomolecular Structure and Dynamics welcomes manuscripts on biological structure, dynamics, interactions and expression. The Journal is one of the leading publications in high end computational science, atomic structural biology, bioinformatics, virtual drug design, genomics and biological networks.