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Revision as of 18:17, 17 October 2018

NUS Singapore Science: InterLab

Enzyme
Kinetics

Enzyme Kinetics and Least Squares Regression
The efficiency of our RESCUE system is likely to be dependent on multiple factors such as mismatch distance, length of spacer regions as with ADAR-dCas13b constructs (Cox et al., 2016), as well as the relative concentrations of the substrates/enzymes. The concept of regression models can be utilized to identify and evaluate the significance of these factors from experimental results. As such, a early build of an enzyme kinetics regression model on dCas13b-APOBEC editing efficiency may help us to gain further insights of the RESCUE system.
Goal
  1. 1. Simulate the RESCUE system under different relative concentrations of substrates and enzymes to determine the concentrations that might yield maximum efficiency.
  2. 2. Determine the change in the binding and catalytic efficiency when spacer length and mismatch distance is varied, which will help in the design of gRNA for more efficient base editing.
Our assumptions of the model are as follows:
  1. 1. Association between dCas13b and gRNA is reversible and precedes enzyme-gRNA complex association with substrate mRNA. This is because dCas13b requires the gRNA to bind to the correct target sequence.
  2. 2. Once the Enzyme-gRNA-Substrate-mRNA trinity complex (ERS) is formed, the reaction will proceed in a single direction to produce the cleaved product.
First, we build upon a kinetics model for our fusion protein product.


Figure 1: Overall biochemical kinetics equation of the RESCUE system. The coefficients (parameters) of the various reaction rates are denoted as k­I, where i represents the differing stages. The growth rates of each biological substrates are denoted by Gi, and while the decay rates are denoted as Di. In particular, K2/k-2 denotes the binding efficiency (dissociation constant) and k3 denotes catalytic efficiency.