Three stop codons (UAA, UAG and UGA) terminate protein synthesis and are almost exclusively recognized by release factors. Here, we design de novo transfer RNAs (tRNAs) that efficiently decode UGA stop codons in Escherichia coli. The tRNA designs harness various functionally conserved aspects of sense-codon decoding tRNAs. Optimization within the TΨC-stem to stabilize binding to the elongation factor, displays the most potent effect in enhancing suppression activity. We determine the structure of the ribosome in a complex with the designed tRNA bound to a UGA stop codon in the A site at 2.9 Å resolution. In the context of the suppressor tRNA, the conformation of the UGA codon resembles that of a sense-codon rather than when canonical translation termination release factors are bound, suggesting conformational flexibility of the stop codons dependent on the nature of the A-site ligand. The systematic analysis, combined with structural insights, provides a rationale for targeted repurposing of tRNAs to correct devastating nonsense mutations that introduce a premature stop codon.

Ribonucleic acid (RNA) is an essential molecule in a wide range of biological functions. In 1990, McCaskill introduced a dynamic programming algorithm for computing the partition function of an RNA sequence. This forward model is widely used for understanding the thermodynamic properties of a given RNA. In this work, we introduce a generalization of McCaskill's algorithm that is well-defined over continuous inputs and is differentiable. This allows us to tackle the inverse folding problem---designing a sequence with desired equilibrium thermodynamic properties---directly using gradient optimization. This has applications to creating RNA-based drugs such as mRNA vaccines. Furthermore, it allows McCaskill's foundational algorithm to be incorporated into machine learning pipelines directly since we have made it end-to-end differentiable. This work highlights how principles from differentiable programming can be translated to existing physical models to develop powerful tools for machine learning. We provide a concrete example by implementing an effective and interpretable RNA design algorithm.

We have implemented a method for the design of RNA sequences that should fold to arbitrary secondary structures. A popular energy model allows one to take the derivative with respect to composition, which can then be interpreted as a force and used for Newtonian dynamics in sequence space. Combined with a negative design term, one can rapidly sample sequences which are compatible with a desired secondary structure via simulated annealing. Results for 360 structures were compared with those from another nucleic acid design program using measures such as the probability of the target structure and an ensemble-weighted distance to the target structure.

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