Compact RNA sensors for increasingly complex functions of multiple inputs

Inventors & their inventions

Axial: https://linktr.ee/axialxyz

Axial partners with great founders and inventors. We invest in early-stage life sciences companies such as Appia Bio, Seranova Bio, Delix Therapeutics, Simcha Therapeutics, among others often when they are no more than an idea. We are fanatical about helping the rare inventor who is compelled to build their own enduring business. If you or someone you know has a great idea or company in life sciences, Axial would be excited to get to know you and possibly invest in your vision and company. We are excited to be in business with you — email us at info@axialvc.com

This paper describes the work of Christian Choe, Johan Andreasson and their colleagues in designing compact RNA sensors capable of performing increasingly complex logic computations. They presented a series of RNA design challenges to the Eterna citizen science community, starting with simple single-input sensors and building up to sensors that can compute logic gates, ratio sensing, and a 3-gene tuberculosis diagnostic score.

For each challenge, Eterna players successfully designed RNA sensors that met or exceeded the performance of previous nucleic acid logic systems, with activation ratios approaching the limits of the experimental assays used. The most complex sensor designed was able to compute a tuberculosis diagnostic score based on the expression ratio of three genes ([GBP5][DUSP3]/[KLF2]^2). This 85 nucleotide RNA sensor could accurately categorize tuberculosis samples in flow cytometry tests.

Analysis of successful player strategies revealed common design heuristics like "kernel attractors" and "domain matching secondary structure design" (DMSSD). The authors incorporated these strategies into an algorithm called Nucleologic that automates the design of complex nucleic acid sensors. Nucleologic was able to generate compact RNA and DNA sensors that performed comparably to the top human-designed sensors for the 3-gene tuberculosis score.

In summary, this collaborative crowdsourcing effort demonstrates that complex logic computations approaching the theoretical limits can be encoded in short single-strand nucleic acid molecules. The automated Nucleologic algorithm also provides a path to quickly designing nucleic acid sensors for multi-gene disease signatures and other complex molecular logic tasks.

https://www.biorxiv.org/content/10.1101/2024.01.04.572289v1