ScholarWorks@성균관대학교

초록

Codon optimization is crucial for gene expression in heterologous hosts with varying genetic codes and codon usage, potentially resulting in enhanced protein expression and stability. Traditionally, the codon optimization problem has been solved using classical numerical techniques; however, with recent advancements, quantum algorithms deployed on quantum computers have been adopted for this purpose. This study proposes a codon sequence search protocol tailored to host preferences. Specifically, codon optimization is formulated as a constrained quadratic binary problem and solved using a quantum-classical hybrid approach, integrating quantum annealing with the Lagrange multiplier method. The proposed methodology is then applied to two real-world scenarios: optimizing the codon sequence of the severe acute respiratory syndrome coronavirus 2 spike protein in human hosts and insulin in Escherichia coli (E. coli) hosts. Finally, we evaluated the effectiveness of our protocol by analyzing several biological metrics and validating the RNA stability. It demonstrates the efficacy of the codon sequence in the target system and offers insights into the evolutionary and codon usage preferences.

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