Quantification of the specificity of RNA binding proteins and RNA processing enzymes is essential to understanding their fundamental roles in biological processes. High Throughput Sequencing Kinetics (HTS-Kin) uses high throughput sequencing and internal competition kinetics to simultaneously monitor the processing rate constants of thousands of substrates by RNA processing enzymes. This technique has provided unprecedented insight into the substrate specificity of the tRNA processing endonuclease ribonuclease P.
High-Throughput Sequencing Kinetics (HTS-Kin) measures processing rates of thousands of RNA substrates using internal competition kinetics.
In this study, researchers from Case Western Reserve University School of Medicine investigate the accuracy and robustness of measurements associated with each step of the HTS-Kin procedure. They examine the effect of substrate concentration on the observed rate constant, determine the optimal kinetic parameters, and provide guidelines for reducing error in amplification of the substrate population. Importantly, the researchers find that high-throughput sequencing, and experimental reproducibility contribute their own sources of error, and these are the main sources of imprecision in the quantified results when otherwise optimized guidelines are followed.
(A) Reaction coordinate diagram depicting the processing of multiple pre-tRNA substrates by RNase P. As the reaction progresses, the activation energy for kcat/Km determines the relative rate of product formation; thus favorable substrates (blue) are depleted more quickly, while unfavorable substrates (orange) are minimally processed and accumulate transiently relative to the wildtype substrate (black). (B) The substrate and product at different time points in the reaction are separated on a denaturing polyacrylamide gel (left), and the residual susbtrate population isolated for high-througput sequencing. Plotting the normalized reads for each substrate variant from Illumina sequencing shows that as the reaction progresses, susbtrates with fast krel values are depleted from the residual substrate population, while those with slow krel accumulate (right). (C) An affinity distribution measured using HTS-Kin using a pre-tRNAMet N(-1) to N(-6) randomized population is shown as the number of substrate variants with a given krel value and depicts the entire range of effects of this variation on enzyme processing. By definition, the wildtype pre-tRNA has a krel of 1 and substrates are calibrated to this as either faster (krel>1) or slower (krel<1) than the reference.