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mRNA codon optimization with quantum computers

Author

Listed:
  • Dillion M Fox
  • Kim M Branson
  • Ross C Walker

Abstract

Reverse translation of polypeptide sequences to expressible mRNA constructs is a NP-hard combinatorial optimization problem. Each amino acid in the protein sequence can be represented by as many as six codons, and the process of selecting the combination that maximizes probability of expression is termed codon optimization. This work investigates the potential impact of leveraging quantum computing technology for codon optimization. A Quantum Annealer (QA) is compared to a standard genetic algorithm (GA) programmed with the same objective function. The QA is found to be competitive in identifying optimal solutions. The utility of gate-based systems is also evaluated using a simulator resulting in the finding that while current generations of devices lack the hardware requirements, in terms of both qubit count and connectivity, to solve realistic problems, future generation devices may be highly efficient.

Suggested Citation

  • Dillion M Fox & Kim M Branson & Ross C Walker, 2021. "mRNA codon optimization with quantum computers," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-16, October.
    • Handle: RePEc:plo:pone00:0259101
    DOI: 10.1371/journal.pone.0259101
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    References listed on IDEAS

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    1. Gary Kochenberger & Jin-Kao Hao & Fred Glover & Mark Lewis & Zhipeng Lü & Haibo Wang & Yang Wang, 2014. "The unconstrained binary quadratic programming problem: a survey," Journal of Combinatorial Optimization, Springer, vol. 28(1), pages 58-81, July.
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