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

Area of Science:

• Biotechnology
• Bioinformatics
• Vaccinology

Background:

• Messenger RNA (mRNA) vaccines offer rapid development and adaptability but face challenges in balancing mRNA stability and translation efficiency.
• Existing codon optimization tools struggle with local optima, limiting their effectiveness for complex mRNA sequences.

Purpose of the Study:

• To introduce a novel multi-seed searching algorithm for mRNA codon optimization.
• To develop a computational framework that synergistically co-optimizes minimum free energy and codon adaptation index.
• To create the Optiseed platform for end-to-end mRNA vaccine construct design.

Main Methods:

• Developed a multi-seed searching algorithm integrating simulated annealing and genetic algorithms.
• Implemented adaptive integration for enhanced global search capability, overcoming local optima limitations.
• Evaluated the algorithm on long therapeutic mRNA sequences and short neoantigen peptides from cancer.

Main Results:

• The novel algorithm outperforms state-of-the-art LinearDesign in balancing mRNA stability and translational efficiency.
• Demonstrated superior performance across diverse sequences, including therapeutic mRNAs and cancer neoantigens.
• The Optiseed platform offers customizable features for tailored mRNA optimization.

Conclusions:

• Optiseed provides a robust, scalable solution for mRNA codon optimization, addressing limitations of conventional tools.
• The algorithm's ability to navigate trade-offs accelerates mRNA therapeutic development, especially in personalized cancer immunotherapy.
• The framework is adaptable for diverse applications, including infectious disease vaccine design.