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Abstract Structured RNA lies at the heart of many central biological processes, from gene expression to catalysis. RNA structure prediction is not yet possible due to a lack of high-quality reference data associated with organismal phenotypes that could inform RNA function. We present GARNET (Gtdb Acquired RNa with Environmental Temperatures), a new database for RNA structural and functional analysis anchored to the Genome Taxonomy Database (GTDB). GARNET links RNA sequences to experimental and predicted optimal growth temperatures of GTDB reference organisms. Using GARNET, we develop sequence- and structure-aware RNA generative models, with overlapping triplet tokenization providing optimal encoding for a GPT-like model. Leveraging hyperthermophilic RNAs in GARNET and these RNA generative models, we identify mutations in ribosomal RNA that confer increased thermostability to theEscherichia coliribosome. The GTDB-derived data and deep learning models presented here provide a foundation for understanding the connections between RNA sequence, structure, and function. 

The genetic code defines how information in the genome is translated into protein. Aside from a handful of isolated exceptions, this code is universal. Researchers have developed techniques to artificially expand the genetic code, repurposing codons and translational machinery to incorporate nonstandard amino acids (nsAAs) into proteins. A key challenge for robust genetic code expansion is orthogonality; the engineered machinery used to introduce nsAAs into proteins must co-exist with native translation and gene expression without cross-reactivity or pleiotropy. The issue of orthogonality manifests at several levels, including those of codons, ribosomes, aminoacyl-tRNA synthetases, tRNAs, and elongation factors. In this concept paper, we describe advances in genome recoding, translational engineering and associated challenges rooted in establishing orthogonality needed to expand the genetic code.