Implementing computational methods in tandem with synonymous gene recoding for therapeutic development

Synonymous gene recoding, the substitution of synonymous variants into the genetic sequence, has been used to overcome many production limitations in therapeutic development. However, the safety and efficacy of recoded therapeutics can be difficult to evaluate because synonymous codon substitutions can result in subtle, yet impactful changes in protein features and require sensitive methods for detection. Given that computational approaches have made significant leaps in recent years, we propose that machine-learning (ML) tools may be leveraged to assess gene-recoded therapeutics and foresee an opportunity to adapt codon contexts to enhance some powerful existing tools. Here, we examine how synonymous gene recoding has been used to address challenges in therapeutic development, explain the biological mechanisms underlying its effects, and explore the application of computational platforms to improve the surveillance of functional variants in therapeutic design. Synonymous variants, previously considered to be silent genetic modifiers, have been implicated in many diseases.Synonymous gene recoding, the substitution of synonymous variants into genetic sequences, can have significant consequences on the protein despite preserving the primary amino acid sequence.Improvements in the biomanufacturing process of recombinant biologics have been driven by synonymous gene recoding, such as enhanced protein expression.Gene recoding has been used in the development of many current biologics and gene therapies, and in the attenuation of viruses for vaccine development.Recent advances in computational machine-learning (ML) and deep-learning (DL) platforms have improved proficiencies in assessing gene-recoded sequences and may be useful tools for evaluating synonymous gene-recoded therapeutics.