High-Affinity Nucleic-Acid-Based Receptors for Steroids.

Artificial receptors for hydrophobic molecules usually have moderate affinities and limited selectivities. We describe three new classes of high affinity hydrophobic receptors for nonaromatic steroids based on deoxyribonucleotides, obtained through five high stringency selections coupled with tailored counter-selections. The isolation of multiple classes of high affinity steroid receptors demonstrates the surprising breadth of moderately sized hydrophobic binding motifs (<40 nucleotides) available to natural nucleic acids. Studies of interactions with analogs indicate that two classes, four-way junctions and 4XGN motifs, comprise receptors with shapes that prevent binding of specific steroid conjugates used in counter-selections. Furthermore, they strongly prefer nonhydroxylated steroid cores, which is typical for hydrophobic receptors. The third new class accommodates hydroxyl groups in high-affinity, high-selectivity binding pockets, thus reversing the preferences of the first two classes. The high-affinity binding of aptamers to targets efficiently inhibits double-helix formation in the presence of the complementary oligonucleotides. The high affinity of some of these receptors and tailored elimination of binding through counter-selections ensures that these new aptamers will enable clinical chemistry applications.

In vitro molecular machine learning algorithm via symmetric internal loops of DNA.

Programmable biomolecules, such as DNA strands, deoxyribozymes, and restriction enzymes, have been used to solve computational problems, construct large-scale logic circuits, and program simple molecular games. Although studies have shown the potential of molecular computing, the capability of computational learning with DNA molecules, i.e., molecular machine learning, has yet to be experimentally verified. Here, we present a novel molecular learning in vitro model in which symmetric internal loops of double-stranded DNA are exploited to measure the differences between training instances, thus enabling the molecules to learn from small errors. The model was evaluated on a data set of twenty dialogue sentences obtained from the television shows Friends and Prison Break. The wet DNA-computing experiments confirmed that the molecular learning machine was able to generalize the dialogue patterns of each show and successfully identify the show from which the sentences originated. The molecular machine learning model described here opens the way for solving machine learning problems in computer science and biology using in vitro molecular computing with the data encoded in DNA molecules.