Why target RNA?
RNA is involved, directly or indirectly, in nearly every known disease. Up to 93% of the human genome is transcribed into RNA, yet less than 2% of that is translated into proteins. Drug discovery efforts over the last fifty years have almost exclusively focused on those 2%.
RNA as a therapeutic target remains almost entirely unexploited. It presents a novel route both to drug the undruggable, and to enhance existing therapeutic interventions. The Nymirum platform is designed to enable this paradigm.
Much like proteins, RNAs can adopt complex multi-dimensional structures which are involved in a host of cellular functions. Unlike proteins, RNA has its own unique set of challenges that must be addressed.
RNA is exceptionally dynamic, a “moving” drug target, which limits the use of conventional structure determination methods and necessitates deep understanding of which conformations will lead to a desired outcome.
Selectivity is even more crucial, and must be achieved at the target, and even conformational, level.
Chemical space exceeds 10^63 small molecules, but most physical libraries are much smaller (10^4 - 10^6) and biased towards a few protein classes. A much larger chemical space must be tested to find novel and selective RNA binders with drug-like properties.
Dynamic Atomic-Scale RNA Drug Targets (DARTs)
We leverage orthogonal sources of experimental data and computational methods to distill large and complex RNAs to the key motifs required for desired biological activity, assess its druggability, and generate a dynamic, atomic-scale, structure of the key element(s).
Scale and Enrichment
We integrate scalable experimental data with physical methods and ML to evaluate billions of compounds in a matter of days to identify novel chemotypes and match them to their corresponding DART conformations. Enriching hits by over 2 orders of magnitude over conventional screens and enabling targeted medicinal chemistry.
The Nymirum platform is tailored for each target. It learns and dramatically
improves based on success and failure, which results in increasingly accurate predictions and faster turn-around times.
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Ganser, L.R., Lee, J., Rangadurai, A. et al. High-performance virtual screening by targeting a high-resolution RNA dynamic ensemble. Nat Struct Mol Biol 25, 425–434 (2018). https://doi.org/10.1038/s41594-018-0062-4
Ganser, L.R., Kelly, M.L., Herschlag, D. et al. The roles of structural dynamics in the cellular functions of RNAs. Nat Rev Mol Cell Biol 20, 474–489 (2019). https://doi.org/10.1038/s41580-019-0136-0