Background: Internal benchmarking initiative.
Goal: Assess ConvergeGEO™ performance against leading commercial sequence-optimization platforms.
Approach: Zero-shot generative molecular design vs. established commercial algorithms.
Outcome: Statistically superior yields across multiple therapeutic modalities; outperforming GenScript, Twist, IDT, and ThermoFisher.

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The Context: Hitting the Ceiling of Standard Optimization

For biopharmaceutical companies and Contract Research Organizations (CROs), maximizing protein yield is the single most effective lever for reducing Cost of Goods Sold (COGS) and accelerating development. For years, the industry has relied on standard commercial algorithms provided by gene synthesis vendors to optimize transcript sequences.

While these tools are widely used, they rely on heuristic approaches, such as codon optimization tables, that have largely hit a performance plateau.

The Challenge

The objective was a rigorous, head-to-head "stress test": could ConvergeGEO™ outperform the industry’s most trusted commercial tools without any prior training on the specific molecules?

This benchmark had to be Zero-Shot. ConvergeGEO™ needed to generate optimized sequences "straight out of the box," proving that a foundation model can generalize across protein classes and deliver superior yields where traditional algorithms fail to improve.

The Solution: A Head-to-Head Showdown

We designed a benchmarking study comparing ConvergeGEO™ against the sequence-optimization tools of four widely used commercial platforms: GenScript, Twist, IDT, and ThermoFisher.

We selected four distinct therapeutic proteins to represent a range of modalities:
• A Hormone: Erythropoietin (EPO)
• A Cytokine: GM-CSF
• Two Antibodies: Rituximab and Trastuzumab (IgGs).

All constructs were expressed side-by-side in HEK293 cells under identical standardized conditions. To ensure a fair comparison across proteins with vastly different expression baselines, we normalized purified yields using z-scores.

Results

ConvergeGEO™ demonstrated superior performance, outperforming the industry standards.
In a statistical analysis of the results, ConvergeGEO™ delivered the highest average and median normalized yields across all proteins tested. A one-way ANOVA confirmed that this performance gap was statistically significant (p = 0.012), proving that sequence optimization alone can drive meaningful improvements.

The raw expression data highlighted the robust effectiveness of the Converge platform:
• Rituximab: 264 mg/L
• EPO: 176 mg/L
• Trastuzumab: 130 mg/L
• GM-CSF: 120 mg/L

Crucially, ConvergeGEO™ achieved the highest aggregate Z-scores, demonstrating notable platform robustness. While comparator methods displayed performance variance optimizing specific molecules effectively, while underperforming on others, ConvergeGEO™ maintained high baseline expression across the diverse protein set. This statistical consistency suggests a reliable, generalized capacity for supporting manufacturability across broad therapeutic pipelines.

Scientific Insight: The Zero-Shot Advantage

What makes these results remarkable is the zero-shot nature of the application. Unlike traditional methods that rely on fixed rules, ConvergeGEO™ leverages a foundation model trained on vast biological datasets to "understand" the language of gene expression.
This allows the platform to predict highly efficient sequences for totally unseen proteins without requiring indication-specific training data. This generalizability simulates the real-life needs of R&D teams who often work with novel molecules where historical training data does not exist.

Our platform

ConvergeGEO™ is our gene expression optimization solution, built to maximize protein yield in a host-specific context. It uses generative modeling and structural analysis to optimize codon usage, UTRs and promoter regions for efficient expression. Our models account for the unique expression profiles and codon preferences of different organisms, including bacterial, yeast and mammalian systems, to ensure optimal output tailored to the production host. 
We support expression systems based on CHO, HEK293, E. coli, Pichia, S. cerevisiae, A. oryzae.

Conclusion

This benchmarking study confirms that ConvergeGEO™ is not just an alternative to standard commercial tools, it is a significant upgrade. By consistently delivering higher protein yields across multiple modalities without the need for specific fine-tuning, ConvergeGEO™ offers a robust solution for enhancing recombinant protein production.

This translates into direct value for the end customer, reducing manufacturing costs, increasing efficiency, and salvaging low-expressing candidates.

For CROs and biopharma developers, switching from legacy heuristics to generative optimization represents a clear path to gaining a competitive advantage in a crowded market.

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