Poster abstracts

Poster number 76 submitted by Hsuan-Chun Lin

Next-generation tools for RNA enzymology: Determination of rate and equilibrium constants for large populations of RNA substrate variants using high throughput sequencing.

Hsuan-Chun Lin (Department of Biochemistry, Case Western Reserve University), Ulf-Peter Guenther (Center for RNA Molecular Biology, Case Western Reserve University), Courtney Niland (Department of Biochemistry, Case Western Reserve University), Vernon Anderson (Department of Biochemistry, Case Western Reserve University), Eckhard Jankowsky (Center for RNA Molecular Biology, Case Western Reserve University), Michael Harris (Department of Biochemistry, Case Western Reserve University)

Abstract:
RNA processing enzymes and catalytic RNAs perform essential roles throughout biology and are often the targets of therapeutic intervention for treating human disease. Structure-function studies of RNA binding and RNA-processing reactions, in which the effects of specific variations in sequence on specific reaction parameters such as binding kinetics, equilibrium binding affinity and catalytic rate, have provided deep insights into biological function to be gained. Nonetheless, our perspective is severely limited by the relatively small number of sequence variants that can be analyzed. Using RNase P processing of pre-tRNA as an experimental system are developing a set of tools based on high-throughput sequencing and competitive substrate kinetic analysis to accurately and simultaneously determine kinetic and equilibrium binding constants for large RNA substrate. Next generation sequencing is used to follow the distribution of sequences in substrate and product populations as a function of reaction progress. Competitive substrate kinetic analyses are used to calculate rate constants for all members of the substrate population from these data. For RNase P the resulting high-density structure-function data sets are providing unique insights into patterns of molecular recognition and the nature of specificity in RNA-protein interactions. Although powerful, an inherent limitation of competitive multiple turnover kinetics is that product inhibition, inactive substrate populations and multiphasic kinetics can limit precision. Using single turnover reactions which conform more directly to simple exponential kinetics should allow high resolution data sets to be gained for both binding kinetics and effects on catalysis. A similar approach is being developed to determine equilibrium binding constants by analyzing the distribution of sequences in free and bound populations analyzed using simple competitive binding models. Here, free and bound populations are separated and purified by native gel electrophoresis. In combination these approaches are providing a comprehensive understanding of how substrate sequence and structure affect binding affinity, association kinetics and catalysis.

Keywords: RNA enzymology, high throughput sequencing, equilibrium binding