Deciphering the Details of RNA Aminoglycoside Interactions: From Atomistic Models to Biotechnological Applications

Abstract

<p>Aminoglycosides are a class of antibiotics functioning through binding to 16S rRNA A-site and inhibiting the bacterial translation. However, the continuous emergence of drug-resistant strains makes the development of new and more potent antibiotics necessary. Aminoglycosides are also known to interact with various biologically crucial RNA molecules other than 16S rRNA A-site and inhibit their functions. As a result, they are considered as the single most important model to understand the principles of RNA small molecule recognition. The detailed understanding of these interactions is necessary for the development of novel antibacterial, antiviral or even anti-oncogenic agents.</p> <p>In our studies, we have studied both the natural aminoglycoside targets like Rev responsive element (RRE), trans-activating region (TAR) of HIV-1 and thymidylate synthase mRNA 5' untranslated (UTR) region as well as the in vitro selected neomycin, tobramycin and kanamycin RNA aptamers. By this way, we think we have covered a variety of binding pockets to figure out the critical nucleic acid residues playing essential role in aminoglycoside recognition. Along with all these RNAs, we studied more than 10 aminoglycoside ligands to pinpoint the chemical groups in close contact with RNAs. To determine thermodynamic parameters for these interactions, we utilized isothermal titration calorimetry (ITC) assay by which we found that the majority of these interactions are enthalpy driven. More specifically, RNA aminoglycoside interactions are mainly derived by electrostatic and hydrogen binding interactions. Our studies indicated that the amino groups on the first ring of the aminoglycosides are essential for high affinity binding whereas having bulky groups on ring II sterically eliminate their interactions with RNAs. RNA binding trend of aminoglycosides are as follows: neomycin-B > ribostamycin > kanamycin-B > tobramycin > paromomycin > sisomicin > gentamicin > kanamycin-A > geneticin > amikacin > netilmicin. Aminoglycoside binding to the aptamer was shown highly buffer dependent. This phenomenon was analyzed in five different buffers and found that cacodylate-based buffer changes the specificity of the aptamer.</p> <p>In addition to ITC, we have used molecular docking to specifically find out the chemical groups in these interactions. We have specified the nucleic acid residues interacting with aminoglycosides.</p> <p>In parallel, molecular dynamics (MD) simulations of neomycin RNA aptamer with neomycin-B in an all-atom platform in GROMACS were carried out. The results showed a mobile structure consistent with the ability of this aptamer to interact with a wide range of ligands. From molecular docking and MD simulations, we identified the neomycin-B aptamer residues that might contribute to its ligand selectivity and designed a series of new aptamers accordingly. Also, A16 was found to be flexible, which was confirmed by 2AP fluorescence studies. In this analysis, the buffer dependence was also confirmed against neomycin-B, ribostamycin and paromomycin.</p> <p>One of the challenges in therapeutics is the emergence of resistant cells. They become reistant to the drugs via changing the target site, or enzymatically modifying the drug, or producing drug pumps to export the drugs. To overcome the very last challenge, we are utilizing RNA-aminoglycoside partners to keep high intracellular drug concentration and increase the efficacy of aminoglycosides against bacteria. We called the system as DRAGINs (Drug binding aptamers for growing intracellular numbers). We express these RNAs in bacteria and detect their growth rate in order to evaluate their response to different concentration of aminoglycosides. In this study, we found that we could successfully decrease the IC50 values by 2 to 5 fold with the help of aminoglycoside-binding RNA aptamers. Finally, we are mathematically modeling the effect of aptamers on IC50 values of drugs with the use of four-compartment model.</p> <p>In our research group, we are utilizing these RNA-aminoglycoside partners to develop tags for detecting RNA in vivo and in real time. We called this system as intracellular multiaptamer genetic tags (IMAGEtags).</p>