LinearCDSfold: a tool for co-optimizing secondary structure stability and codon usage in coding sequence design

Area of Science:

• Bioinformatics
• Computational Biology
• Synthetic Biology

Background:

• Designing mRNA coding sequences (CDSs) for vaccines requires optimizing RNA secondary structure stability (minimum free energy, MFE) and codon usage (codon adaptation index, CAI).
• Existing tools often struggle to balance these competing objectives, necessitating methods for generating Pareto-optimal CDSs where neither objective can be improved without worsening the other.

Purpose of the Study:

• To enhance the LinearCDSfold tool for automatic and efficient generation of Pareto-optimal CDSs.
• To provide a superior alternative for co-optimizing MFE and CAI in mRNA vaccine design.

Main Methods:

• Utilized dynamic programming and beam search techniques within the LinearCDSfold framework.
• Extended LinearCDSfold to generate a set of Pareto-optimal CDSs, considering both MFE and CAI simultaneously.
• Evaluated performance on nine diverse protein sequences.

Main Results:

• The enhanced LinearCDSfold efficiently generates Pareto-optimal CDSs.
• Performance was comparable to the existing tool DERNA in terms of Pareto-optimal set generation.
• LinearCDSfold demonstrated substantially faster runtime compared to DERNA.

Conclusions:

• LinearCDSfold is a powerful and efficient tool for generating Pareto-optimal CDSs for mRNA vaccine development.
• The enhanced version offers a significant speed advantage, accelerating the design process.
• This advancement facilitates the co-optimization of RNA structure and codon usage in synthetic biology applications.