Multi-seed searching algorithm for integrated codon optimization of mRNA stability and translational efficiency in vaccine design

Messenger RNA (mRNA) vaccines have revolutionized vaccinology with their rapid development cycles and adaptability, yet their broad application is constrained by unresolved challenges in balancing mRNA structural stability and translational efficiency. Here, we introduce a groundbreaking multi-seed searching algorithm for mRNA codon optimization, an innovative framework that synergistically co-optimizes minimum free energy and codon adaptation index through adaptive integration of simulated annealing and genetic algorithms. This novel approach enhances global search capability to escape local optima, a critical limitation of existing tools. Evaluations across long therapeutic mRNA sequences and short peptides (neoantigens from bladder cancer and melanoma) reveal our algorithm outperforms state-of-the-art LinearDesign, delivering superior balanced improvements in both stability and translational efficiency validating its unique ability to navigate the inherent trade-offs between these two key metrics. Built on this algorithm, the Optiseed platform introduces transformative features including customizable scoring functions, flexible parameters for tailored optimization, and support for integrating untranslated regions (UTRs), poly(A) tails, and other elements to enable end-to-end vaccine construct design. This innovation addresses the rigidity of conventional tools, empowering precise, context-specific optimization. Optiseed represents a robust, scalable solution for mRNA vaccine codon optimization. Its superior performance across diverse sequences underscores its potential to accelerate mRNA-based therapeutic development, particularly in personalized cancer immunotherapy, while offering a framework adaptable for other applications such as infectious disease vaccine design.