Slow is Smooth
The beauty and complexity of codon optimization lie in the redundancy of the genetic code. With up to six synonymous codons encoding a single amino acid, any protein sequence can be translated from thousands of distinct nucleotide sequences. This degeneracy opens up a vast design space to fine-tune protein expression for optimal functionality, not just maximum quantity.
Rather than treating the coding sequence (CDS) as an isolated unit, we co-optimize it alongside the 5' and 3' untranslated regions (UTRs). A well-optimized CDS must harmonize with its UTRs to ensure smooth ribosome entry and progression, avoiding stalls and traffic jams during translation.
Codon usage must be carefully balanced. Overusing high-frequency codons may accelerate elongation but can overload cellular metabolism and deplete specific tRNA pools. Underusing them, on the other hand, can unnecessarily slow translation. To navigate this trade-off, we leverage codon-level design rules that maintain optimal flow without overwhelming the system.
Secondary structure adds another layer of complexity. Codon choices influence local mRNA folding, and high GC content can create stable hairpins and loops that hinder ribosomal movement. Our algorithms minimize disruptive structures by leveraging design strategies that preserve high translation efficiency.
Ribosome density is equally critical. Excessive ribosome loading on an mRNA can lead to collisions and activate decay pathways. Our protein sequence optimization tool balances codon usage and mRNA structure to prevent these bottlenecks and support continuous, high-fidelity translation.
The ability to generate multiple synonymous DNA sequences for any single amino acid sequence expands the design space enormously. But exploring this space effectively requires more than brute force, it demands strategy.
To do this, we use a genetic algorithm, a population-based optimization method inspired by evolution, to explore a vast landscape of synonymous sequences and identify those that best balance expression efficiency, structural stability, and manufacturability.
It begins with a diverse pool of candidate sequences, all encoding the same protein. Each sequence is evaluated in silico for translation efficiency, structural properties, and manufacturability. The best performers are selected, recombined, and mutated to form the next generation. This iterative process continues until convergence on a set of optimized sequences that balance expression, folding, and translational smoothness without inducing cellular stress.
At the end of this in silico evolution, we return one or multiple optimized CDS sequences paired with their respective UTRs, ready for synthesis and experimental testing.
This approach offers several key advantages. First, it enables rapid exploration of thousands of sequence variants entirely in silico, saving time and resources.
Second, by returning multiple high-quality candidates, it increases the probability of success through parallel testing—more shots on goal.
Finally, experimental feedback can be used to refine Officinae Bio’s predictive models, enabling the development of a dedicated, user-specific model that customizes the design space to meet each customer’s unique requirements.
