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      Impact of Ribosomal Modification on the Binding of the Antibiotic Telithromycin Using a Combined Grand Canonical Monte Carlo/Molecular Dynamics Simulation Approach

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          Abstract

          Resistance to macrolide antibiotics is conferred by mutation of A2058 to G or methylation by Erm methyltransferases of the exocyclic N6 of A2058 (E. coli numbering) that forms the macrolide binding site in the 50S subunit of the ribosome. Ketolides such as telithromycin mitigate A2058G resistance yet remain susceptible to Erm-based resistance. Molecular details associated with macrolide resistance due to the A2058G mutation and methylation at N6 of A2058 by Erm methyltransferases were investigated using empirical force field-based simulations. To address the buried nature of the macrolide binding site, the number of waters within the pocket was allowed to fluctuate via the use of a Grand Canonical Monte Carlo (GCMC) methodology. The GCMC water insertion/deletion steps were alternated with Molecular Dynamics (MD) simulations to allow for relaxation of the entire system. From this GCMC/MD approach information on the interactions between telithromycin and the 50S ribosome was obtained. In the wild-type (WT) ribosome, the 2′-OH to A2058 N1 hydrogen bond samples short distances with a higher probability, while the effectiveness of telithromycin against the A2058G mutation is explained by a rearrangement of the hydrogen bonding pattern of the 2′-OH to 2058 that maintains the overall antibiotic-ribosome interactions. In both the WT and A2058G mutation there is significant flexibility in telithromycin's imidazole-pyridine side chain (ARM), indicating that entropic effects contribute to the binding affinity. Methylated ribosomes show lower sampling of short 2′-OH to 2058 distances and also demonstrate enhanced G2057-A2058 stacking leading to disrupted A752-U2609 Watson-Crick (WC) interactions as well as hydrogen bonding between telithromycin's ARM and U2609. This information will be of utility in the rational design of novel macrolide analogs with improved activity against methylated A2058 ribosomes.

          Author Summary

          Bacterial resistance to antibiotics is a serious public health problem that requires the continuous development of new antibiotics. Bacteria acquire resistance to macrolide antibiotics by (1) effluxing the drug from the cell, (2) modifying the drug, or (3) modifying the drug target (i.e., the 50S subunit of the ribosome) to abrogate or completely abolish binding. While newer antibiotics are able to avoid the first two mechanisms, they remain unable to overcome resistance due to ribosomal modification, particularly due to methyltransferase (i.e., erm) enzymes. We have applied computer-aided drug design methods designed explicitly for studies of the ribosome to better understand the relationship between modification of the ribosome by erms and the binding of telithromycin, a 3 rd generation ketolide antibiotic derived from erythromycin. While we confirm that ribosomal modification leads to decreased binding due to disruption of key interactions with the drug, we find these modifications effect a structural rearrangement of the entire region of the ribosome responsible for binding macrolide antibiotics. This information will be useful in the design of novel antibiotics that are effective against resistant bacteria possessing modified ribosomes.

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          Most cited references61

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          Development and current status of the CHARMM force field for nucleic acids.

          The CHARMM27 all-atom force field for nucleic acids represents a highly optimized model for investigations of nucleic acids via empirical force field calculations. The force field satisfactorily treats the A, B, and Z forms of DNA as well as RNA, and it also useful for nucleosides and nucleotides. In addition, it is compatible with the CHARMM force fields for proteins and lipids, allowing for simulation studies of heterogeneous systems. Copyright 2001 John Wiley & Sons, Inc. Biopolymers (Nucleic Acid Sci) 56: 257-265, 2001
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            Improved treatment of the protein backbone in empirical force fields.

            Empirical force field-based calculations of proteins, including protein-folding studies, have improved our understanding of the relationship of their structure to their biological function. However, limitations in the accuracy of empirical force fields in the treatment of the peptide backbone exist. Presented is a grid correction approach to improve the treatment of the peptide backbone phi/psi conformational energies. Inclusion of this correction with the CHARMM22 all-atom protein force field is shown to lead to significant improvement in the treatment of the conformational energies of both the peptide model compound, the alanine dipeptide, and of proteins in their crystal environment. The developed approach is suggested to lead to significant improvements in the accuracy of empirical force fields to treat peptides and proteins.
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              Optimization of the CHARMM additive force field for DNA: Improved treatment of the BI/BII conformational equilibrium.

              The B-form of DNA can populate two different backbone conformations: BI and BII, defined by the difference between the torsion angles ε and ζ (BI = ε-ζ 0). BI is the most populated state, but the population of the BII state, which is sequence dependent, is significant and accumulating evidence shows that BII affects the overall structure of DNA, and thus influences protein-DNA recognition. This work presents a reparametrization of the CHARMM27 additive nucleic acid force field to increase the sampling of the BII form in MD simulations of DNA. In addition, minor modifications of sugar puckering were introduced to facilitate sampling of the A form of DNA under the appropriate environmental conditions. Parameter optimization was guided by quantum mechanical data on model compounds, followed by calculations on several DNA duplexes in the condensed phase. The selected optimized parameters were then validated against a number of DNA duplexes, with the most extensive tests performed on the EcoRI dodecamer, including comparative calculations using the Amber Parm99bsc0 force field. The new CHARMM model better reproduces experimentally observed sampling of the BII conformation, including sampling as a function of sequence. In addition, the model reproduces the A form of the 1ZF1 duplex in 75 % ethanol, and yields a stable Z-DNA conformation of duplex (GTACGTAC) in its crystal environment. The resulting model, in combination with a recent reoptimization of the CHARMM27 force field for RNA, will be referred to as CHARMM36.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                June 2013
                June 2013
                13 June 2013
                : 9
                : 6
                : e1003113
                Affiliations
                [1 ]Department of Pharmaceutical Sciences, University of Maryland, Baltimore, Maryland, United States of America
                [2 ]Department of Chemistry, Temple University, Philadelphia, Pennsylvania, United States of America
                CNAG - Centre Nacional d'Anàlisi Genòmica and CRG - Centre de Regulació Genòmica, Spain
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: RBA ADM. Performed the experiments: MCS PL ADM. Analyzed the data: MCS PL RBA ADM. Contributed reagents/materials/analysis tools: MCS PL ADM. Wrote the paper: MCS PL RBA ADM.

                Article
                PCOMPBIOL-D-13-00342
                10.1371/journal.pcbi.1003113
                3681621
                23785274
                b0bd68b2-48c1-4ffe-86c0-d6d3314dcf68
                Copyright @ 2013

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 25 February 2013
                : 7 May 2013
                Page count
                Pages: 14
                Funding
                Funding from the NIH is acknowledged (AI080968 and GM070855, http://www.nih.gov/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Biophysics
                Computational Biology
                Microbiology
                Chemistry
                Computational Chemistry

                Quantitative & Systems biology
                Quantitative & Systems biology

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