Talk abstracts

Talk on Friday 03:54-04:06pm 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, School of Medicine, Case Western Reserve University), Courtney Niland (Department of Biochemistry, School of Medicine, Case Western Reserve University), Jing Zhao (Department of Biochemistry, School of Medicine, Case Western Reserve University), David R. Anderson ( Zicklin School of Business, Baruch College, CUNY ), Eckhard Jankowsky (Center for RNA Molecular Biology, School of Medicine, Case Western Reserve University), Michael E. Harris (Department of Biochemistry, School of Medicine, Case Western Reserve University)

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 kinetic analysis to accurately and simultaneously determine kinetic and equilibrium binding constants for large RNA substrates. 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 separated by EMSA using simple competitive binding models. Since the high throughput kinetics gives us the entire affinity distribution, the classical methods such as sequence logos and position weight matrix provide restricted views of specificity that ignore information contain the affinity distribution accordingly. We develop a new multivariable regression method which considers the interaction between different positions. In combination these approaches are providing a comprehensive understanding of how substrate sequence and structure affect binding affinity, association kinetics and catalysis.

Keywords: Enzyme Kinetics, RNase P, high-throughput sequencing