Quantum-classical hybrid approach for codon optimization and its practical applications

Abstract

AbstractCodon 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 respiratory syndrome coronavirus 2 spike protein in human hosts and insulin inEscherichia coli (E. coli)hosts. Finally, evaluations of several biological metrics demonstrate the effectiveness of our protocol, offering insights into the codon usage patterns governing translational efficiency and adaptation to the genetic code preferences of the host organisms.