Automating the design of structure‐switching aptamer biosensors

Abstract

For both personalized medicine and academic research, there is pressing need for tools to enable the rapid interrogation of an array of biological targets associated with diseased states. Electrochemical, DNA‐based (E‐DNA) biosensors utilize the dynamic nature of nucleotide polymers to quantify the concentration of a specific peptide, substrate, or heavy‐metal species contained in a biological sample. A critical limitation of E‐DNA biosensors is the complexity associated with designing a nucleotide sequence that maximizes sensitivity while maintaining specificity. This dilemma is further complicated by the vast number of conformations that can exist for a given sequence. While great strides have been made in computationally predicting the folded structures that DNA and RNA‐based biosensors may assume, interpreting whether such predicted structures would be effective biosensors and using that knowledge to design functional E‐DNA biosensors ab initio remains challenging, suggesting the utility of an in silico approach to E‐DNA sensor design. We have created a program called Fealden, which automates the process of aptamer‐based biosensor generation. Fealden utilizes several programmatic paradigms to build an ideal E‐DNA biosensor sequence; the validity of this sequence is then assessed by assigning thermodynamic values and conformational distance metrics to each possible fold. Using these metrics, Fealden can provide the user with several candidates for an E‐DNA aptamer biosensor that are sensitive to the target of interest. Fealden has already been applied to optimize the detection of the antibiotic tobramycin with minimal human interaction. This work provides insights into applying an algorithmic approach to utilize the wide pool of existing aptamers as E‐DNA biosensors in practical applications spanning such diverse fields as food safety, environmental monitoring, and clinical diagnostics.Support or Funding InformationThis work is supported by internal grant funding from the Metropolitan State University of Denver.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.