Mohseni, A.; Wheeldon, I.; Lonardi, S.

The design of maximally divergent DNA sequences translating into the same protein is a critical problem in synthetic biology. Current design tools that rely on heuristics or machine learning often fail to effectively minimize the length of shared subsequences between the gene copies, compromising strain stability. Here, we introduce SIRIUS, a combinatorial optimization algorithm designed to generate maximally divergent coding sequences for a given protein of interest. Leveraging integer linear programming enforcing host-specific codon usage thresholds, SIRIUS stabilizes synthetic constructs and broadens the accessible design space for robust and scalable synethtic biology. Experimental results show that SIRIUS produces diverse sequences with fewer shared subsequences than existing methods. SIRIUS is freely available on GitHub at https://github.com/ucrbioinfo/sirius.