Development of RNA Aptamers through Supervised Learning and Capture-SELEX strategy

Abstract

Aptamers are small oligonucleotides that are capable of binding specifically to a target, with impressive potential for analysis, diagnostics, and therapeutics applications. Aptamers are selected in vitro from large nucleic acid combinatorial libraries using an iterative selection process called SELEX (Systematic Evolution of Ligands by EXponential enrichment). In vitro selected RNA aptamers can be assembled into programmable biosensors for a broad range of synthetic biology applications requiring the detection and quantification of small molecule metabolites. Aptamers have been around for three decades now, however, sensors that work in proof-of-concept studies, mainly performed in academia, do not necessarily match the conditions, the statistical relevance, the compatibility with routine equipment found in e.g., clinical diagnostic departments, and the scale required for an industrial and/or clinical application. A major obstacle to the broader development of aptamer metabolite biosensors as tools for industrial biotechnology and translational medicine stems from uncertainty in the outcomes of in vitro selection and the aptamer-ligand binding affinities that can be obtained. We present, to our knowledge, the first approach for a priori estimation of RNA aptamer binding affinities and a virtual screening tool against small molecule targets. We illustrate the generalizability of this tool to identifyin silico metabolites and biomarkers that could be targeted with aptamer biosensing. Further, we outline an RNA Capture-SELEX strategy to generate in vitro structure switching aptamers which could be incorporated into genetic switches for regulating gene expression; since in classical SELEX only binding and not conformational changes are selected which is an important property of ‘sensing-actuation’ devices that represent an important class of genetic devices which can detect, report on and act on environmental and intracellular signals, including small molecules and proteins, to study and control cellular functions.