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The Evolution of Codon Optimization: From Promise to Pragmatism

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Codon optimization, a technique for enhancing protein expression by modifying gene sequences, has been a cornerstone of molecular biology since the 1980s. The discovery of codon usage bias [1] led to early manual optimization strategies, which evolved into sophisticated computational tools by the 2000s [2], [3]. These advancements aimed to improve heterologous protein expression, often in biopharmaceutical production [4].

However, the journey of codon optimization hasn’t been without challenges. While many successes have been reported, disappointments are commonplace, and studies have shown that “optimized” sequences sometimes underperform their wild-type counterparts [4], [5], [6], [7]. These setbacks highlight the complexity of the relationship between codon usage and protein production — especially in higher eukaryotes — prompting researchers to consider factors beyond simple codon frequency, including: mRNA secondary structures, cryptic promoters, microRNA binding sites, internal ribosome entry sites, cryptic splice sites, and the positional impact of codon usage on co-translational folding [8].

A crucial factor often overlooked is the impact of gene synthesis constraints on codon optimization. Traditional gene synthesis methods struggle with sequences containing high or low GC content, repetitive elements, or strong secondary structures [9]. Consequently, the “codon optimization” tools offered by major gene synthesis vendors generally prioritize manufacturability over other considerations. This compromise can result in suboptimal sequences for protein expression or function, as the tools are designed primarily to ensure successful synthesis rather than optimal function.

Recent advancements in gene synthesis technology, however, are set to revolutionize this landscape. At Ansa Biotechnologies, we’ve developed a new gene synthesis service that dramatically expands the range of sequences that can be reliably produced. Our service can handle sequences with extreme GC content, repeats, and hairpins that have traditionally been challenging for incumbent gene synthesis vendors.

This capability has significant implications for codon optimization. With Ansa’s technology, researchers now have much greater control over codon selection in their sequences, providing access to sequences that are predicted to have better expression than the “optimal” designs limited by other vendors’ synthesis capabilities. More fundamentally, by removing longstanding synthesis constraints, researchers will now be able to more fully explore the complex relationship between coding sequence and function than has been possible in the past. This exploration will generate new datasets to train machine learning models that can better predict and optimize protein expression, function, and other properties such as immunogenicity, leading to a new generation of codon optimization strategies with superior performance and reliability.

In conclusion, while codon optimization has had important role in biotechnology for decades, it remains hit-or-miss, with reliability historically limited by biological unknowns and gene synthesis constraints. However, technological advancements in gene synthesis are opening up exciting new possibilities. As researchers gain access to a broader sequence space and tools that can design sequences without concern for manufacturability, we can expect to see renewed innovation in codon optimization strategies, potentially leading to significant improvements in protein production and function across a wide range of applications. At Ansa, we’re excited to help make that vision of the future possible by providing researchers with the optimal sequences for their experiments— not the sequences that are easiest to manufacture.

By Dan Lin-Arlow, Ansa CSO and Co-founder

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