Simulation-guided DNAzyme based nanomachine design for identifying single nucleotide variants

Abstract

Single nucleotide variants (SNVs) are essential biomarkers both clinically and biologically, and the specific hybridization of matched sequences is crucial to discriminate SNVs. However, the similar sequence would hybridize to probes incorrectly and cause false positive. Herein, we designed a DNAzyme-based nanomachine guided by thermodynamic and kinetic parameters to identify SNVs. The standard Gibbs free energy of the internal loop in the blocked DNAzyme was rationally taken into consideration and a quasi-steady 'toehold exchange' reaction was designed. These simulations in silico enable an optimal performance to discriminate SNVs for various conditions. Additionally, we introduced a DNA fuel to enable non-covalent DNA catalysis reaction, which can tune the performance of nanomachine by changing the concentration of DNA fuel. In this way, we make a satisfactory trade-off between sensitivity and specificity to discriminate SNVs. Collectively, this nanomachine produced discrimination factors between 5 and 31, the detection limit of 0.1 % variant alleles can also be identified. Our work demonstrated a pathway assist to the design of DNA nanomachine through simulation in silico, which hold great potential for the application of complex DNA nanomachines and biosensors.