(251a) Computational Design of Orthogonal Ribosomes In Bacterial Hosts

Gene expression involves two steps, transcription and translation. While a number of genetic tools exist for reprogramming transcription in cells, far fewer tools exist for translation. Of the tools available in bacteria, the most popular are riboregulators, both cis and trans-activating, and orthogonal ribosomes (o-ribosomes), also known as specialized ribosomes. In terms of reprogramming translation, o-ribosomes are especially powerful as they enable one to partially decouple translation from the native protein synthesis machinery. In particular, o-ribosomes can translate genes with altered Shine-Dalgarno (SD) sequences not recognized by host ribosomes. Because of this fact, o-ribosomes can be used to explore translational regulatory mechanisms such as coupling and also can be used to probe ribosome structure. Furthermore, o-ribosomes can be used to explore gene expression dynamics as they can be used to tune translation rates. Finally, o-ribosomes may have application in synthetic biology as they can be used to introduce new functionality within cells.

To date, o-ribosome design has either involved ad hoc or random mutagenesis-based approaches. While these approaches clearly have been successful, one question is whether a rational, computational-based strategy could be employed in design. In particular, a computational approach would enable one to explicitly explore the different elements and associated hypotheses that factor into o-ribosome design. In this work, we propose a computational strategy for designing o-ribosomes in bacteria. The basic approach in our algorithm involves enumerating all possible ASD-SD pairs and then selecting those that minimally interfere with the translation of native mRNA. To demonstrate the utility of our algorithm, we experimentally tested a number of computationally designed o-ribosomes in E. coli. In the process, we were able to test a number of hypotheses regarding o-ribosome functionality. These findings should complement existing approaches based on random mutagenesis and screening. Finally, because selection is performed on the computer, this computational approach may also be useful for designing o-ribosomes in organisms where genetic screens are more difficult to implement than in E. coli. To test this hypothesis, we also used the algorithm to design o-ribosomes in Bacillus subtilis. We were able to experimentally validate these designs, though they performed less well than their counterparts in E. coli.