Parameterizing the Binding Properties of XNA Aptamers Isolated from a Low Stringency Selection.

Machine learning offers a guided approach to aptamer discovery, but more information is needed to develop algorithms that can intelligently identify high-performing aptamers to a broad array of targets. Critical to this effort is the need to experimentally parameterize the difference between low and high affinity binders to a given target. Although classical selection experiments help define the upper limit by converging on a small number of tight binding sequences, very little is known about the lower limit of binding that defines the boundary between binders and nonbinders. Here, we apply a quantitative approach to explore the diversity of aptamers isolated from two identical in vitro selections performed under low stringency conditions. Starting from a library of 1 trillion unique threose nucleic acid (TNA) sequences, 7 rounds of selection were performed to enrich binders to a known aptagenic target. High density sequencing of each round of selection followed by a detailed kinetic analysis of 136 TNA aptamers yielded a narrow range of equilibrium dissociation constants ( K D = ∼ 1-15 nM) that were consistent between two experimental replicates. These findings offer insights into the lower limit of binding that may be expected for aptamers generated against aptagenic targets and could provide useful constraints for evaluating the results of experimental and computational approaches.