Talk Abstracts

Talk on Saturday 10:30-10:45am submitted by Emily Fairchild

Predictive computational method development for T-box riboswitch drug discovery

Emily A. Fairchild (Department of Chemistry & Biochemistry, Ohio University & Department of Chemistry, Leipzig University), Destini McCartney (Department of Chemistry & Biochemistry, Ohio University), Danushika Hearth (Molecular & Cellular Biology Program, Ohio University & Department of Chemistry & Biochemistry, Ohio University), Sebastian Schmutzler (Department of Chemistry, Leipzig University), Ralf Hoffmann (Department of Chemistry, Leipzig University), Jennifer V. Hines (Molecular & Cellular Biology Program, Ohio University & Department of Chemistry & Biochemistry, Ohio University)

Abstract:
The T- Box Riboswitch is a novel antibiotic target for Gram positive bacteria found at the 5´- end of crucial genes for cell survival and uses charged and uncharged tRNA as a ligand molecule.¹ The antiterminator of the T-Box Riboswitch binds to the acceptor end of tRNA and has a highly conserved sequence and structure.¹ To develop a computational workflow to map possible antiterminator-ligand interactions, we chose to investigate deca-peptides due to ease of synthesis and diversity of functional groups. Our goal is to develop an automatable workflow (utilizing Schrödinger software) with broad applicability to RNA targets and diverse ligands. We selected 16 deca-peptides to synthesize based on our existing established docking protocols.^2,3 We then used data from primary screening specificity assays to inform the refinement of the computational method and the subsequent data analysis. Analysis of the docking data focused on the location of the peptide and the interactions it made with the antiterminator. We used statistical methods (PCA and k-medoids clustering analysis) to group compounds based on distance and scoring function data. The training set of peptides was also tested in an in vitro T-Box riboswitch transcription readthrough assay⁴ for effectiveness on the riboswitch function. The correlation between the computational and experimental results will be discussed.

References:
(1) Zhang, J.; Ferré-D’Amaré, A. R. Structure and Mechanism of the T-Box Riboswitches. Wiley Interdiscip. Rev. RNA 2015, 6 (4), 419–433. https://doi.org/10.1002/wrna.1285.
(2) Orac, C. M.; et.al. Synthesis and Stereospecificity of 4,5-Disubstituted Oxazolidinone Ligands Binding to T-Box Riboswitch RNA. J. Med. Chem. 2011, 54 (19), 6786–6795. https://doi.org/10.1021/jm2006904.
(3) Armstrong, I.; et.al. RNA Drug Discovery: Conformational Restriction Enhances Specific Modulation of the T-Box Riboswitch Function. Bioorg. Med. Chem. 2020, 28 (20), 115696. https://doi.org/10.1016/j.bmc.2020.115696.
(4) Zeng, C.; et.al. Factors That Influence T Box Riboswitch Efficacy and tRNA Affinity. Bioorg. Med. Chem. 2015, 23 (17), 5702–5708. https://doi.org/10.1016/j.bmc.2015.07.018.

Keywords: RNA Drug Design, T-Box Riboswitch, Computational Drug Design