Development of Algorithmic Techniques for Designing Electrochemical DNA Biosensors

Oligonucleotide‐based, electrochemical (E‐DNA) biosensors have been shown to be effective tools for detecting heavy metals, small molecule drugs, and protein targets. There is thus considerable interest in moving these biosensors from the lab bench to applied fields, such as medical, environmental, and food safety diagnostics. A significant hurdle to this transition is the development of effective methods to rapidly and effectively adapt E‐DNA biosensors to the detection of new targets of interest. While great strides have been made in computationally predicting the folded structures of DNA and RNA‐based biosensors, interpreting whether such predicted structures would be effective biosensors and using that knowledge to design functional E‐DNA biosensors ab initio remains challenging. To overcome this we have designed a tool, Fealden, which models potential E‐DNA biosensors using the framework of graph theory and automates the process of structure interpretation and sorting. Using this tool, a researcher can provide a core sequence of interest (such as a DNA recognition element or aptamer) and the software will generate optimized, functional E‐DNA biosensor designs for the sensitive detection of that target. For example, our approach has allowed order‐of‐magnitude improvement in the affinity of biosensors directed towards the antibiotic tobramycin, without human intervention in the design process. This software incorporates literature knowledge of oligonucleotide biosensor design constraints, such as the proximity of appended chemical moieties and predicted folding free energies, and can traverse a search space of thousands to millions of possible sequences to determine the “best candidate” biosensor. 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 Information Support for this work comes from the Metropolitan State University of Denver Provost's and LAS Dean's offices.