Codon optimisation for maximising gene expression in multiple species and microbial consortia

Abstract

MotivationCodon optimisation, the process of adapting the codon composition of a coding sequence, is often used in synthetic biology to increase expression of a heterologous protein. Recently, a number of synthetic biology approaches that allow synthetic constructs to be deployed in multiple organisms have been published. However, so far, design tools for codon optimisation have not been updated to reflect these new approaches. ApproachWe designed an evolutionary algorithm (EA) to design coding sequences (CDSs) that encode a target protein for one or more target organisms, based on the Chimera average repetitive substring (ARS) metric -- a correlate of gene expression. A parameter scan was then used to find optimal parameter sets. Using the optimal parameter sets, three heterologous proteins were repeatedly optimised Bacillus subtilis 168 and Escherichia coli MG1655. The ARS scores of the resulting sequences were compared to the ARS scores of coding sequences that had been optimised for each organism individually (using Chimera Map). ResultsWe demonstrate that an EA is a valid approach to optimising a coding sequence for multiple organisms at once; both crossover and mutation operators were shown to be necessary for the best performance. In some scenarios, the EA generated CDSs that had higher ARS scores than CDSs optimised for the individual organisms, suggesting that the EA exploits the CDS design space in a way that Chimera Map does not. Availability and implementationThe implementation of the EA, with instructions, is available on GitHub: https://github.com/intbio-ncl/chimera_evolve.