mRNA technology has revolutionized vaccine development, protein replacement therapies, and cancer immunotherapies, offering rapid production and precise control over sequence and efficacy. However, the inherent instability of mRNA poses significant challenges for drug storage and distribution, particularly in resource-limited regions. Co-optimizing RNA structure and codon choice has emerged as a promising strategy to enhance mRNA stability while preserving efficacy. Given the vast sequence and structure design space, specialized algorithms are essential to achieve these qualities. Recently, several effective algorithms have been developed to tackle this challenge that all use similar underlying principles. We call these specialized methods mRNA folding algorithms as they generalize classical RNA folding algorithms. Initial laboratory testing of mRNA folding optimized mRNA vaccines, such as those encoding SARS-CoV-2 spike and VZV gE, has shown promising improvements in both in-solution stability and immunogenicity. While these biological properties are beginning to be evaluated experimentally, a comprehensive in silico analysis of the underlying principles, performance, and limitations of these design algorithms is equally essential. Thus, this review aims to provide an in-depth understanding of these algorithms, identify opportunities for improvement, and benchmark existing software implementations in terms of scalability, correctness, and feature support.
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