sxRNA Switches: Hypothesis Through Automated Design Via a Genetic Algorithm Approach

Abstract

The following document is meant to represent an overview of my work on structurally interacting RNA (sxRNA), which has already resulted in three publications with another two in preparation. Where appropriate, some text and data from these publications have been reproduced here. Ribonucleic Acid (RNA) is one of the fundamental macromolecules present in living systems. It can be found in all cells as varying length polymer chains composed of four primary bases (adenine, cytosine, guanine, uracil) capable of numerous modifications. Though generally characterized as an information carrier, RNA is a versatile molecule that exhibits both intra and inter-strand base pairing to form complex structures. Similar to protein, the particular shape of an RNA structure in combination with some degree of sequence specificity, can dictate its function (RNA binding protein recognition sites, ribozyme activity, aptameric affinity, etc.). Structurally interacting RNA (sxRNA) is a molecular switch technology that exploits predictable intermolecular RNA base pairing to form an otherwise absent functional structure in one RNA strand when it interacts with a specific, targeted second strand. Originally proposed as a potential regulatory mechanism in natural systems, we used characteristics of predicted pairings in that context to engineer purely synthetic sxRNA switches that have been successfully tested. There are many non-coding RNAs associated with pathological conditions, the ability to use these as triggers for sxRNA opens the door to potential applications ranging from diagnostics to therapeutics. Furthermore, other prospective triggers (including those synthetically designed) may allow use of the technology as a molecular tool for a variety of purposes including as an alternative to antibiotic selection in cell line development. The typical trigger sequences targeted by sxRNA switches are at least 20 bases in length. Combinatorial options with regard to structure positioning and base composition produce an enormous number of potential sxRNA sequences for any given target. Exhaustively examining these for feasible candidates (i.e., analyzing predicted interactions with unintended targets) is computationally impossible with current systems. Evolutionary computing is a subfield of artificial intelligence (AI) that has been inspired by biology. Genetic algorithms are a type of evolutionary algorithm and apply operators (such as recombination and mutation) to find candidate solutions to an optimization problem. The presented dissertation will describe the original sxRNA research as well as the development and testing of a genetic algorithm that automates the production of new sxRNA switch candidates. This algorithm takes into consideration factors that were previously impossible to account for in manual designs.