Synonymous codons may encode the same amino acid, but host cells do not treat them as interchangeable. Codon preferences vary across species, so a sequence optimized for one organism can express poorly in another.
How this optimization is performed is central to maximizing protein yield. Rule-based algorithms using a codon adaptation index (CAI) can improve expression, but treat optimization as a per-codon problem, missing higher-order features that span codons. Machine learning models consider multiple sequence features, but rely on predefined inputs and narrow training objectives, limiting their ability to capture complex, context-dependent interactions. Large language models (LLMs) take a broader approach, learning patterns across coding sequences that can account for species-specific effects and higher-order, long-range interactions.
This white paper benchmarks these approaches across 32 antibody challenge sequences representing high-, medium-, and low-expression proteins to examine how different optimization strategies affect protein yield.
Download this white paper to learn how
- CAI-based, machine learning, and LLM-driven codon-optimization strategies compare in their effects on protein yield
- LLM-driven approaches perform across a diverse panel of antibody constructs
- Different optimization methods perform on initially low-expressing sequences
