Gene design algorithm to aid recombinant protein production in yeast

Ingenza has secured innovation funding to adapt its advanced machine learning platform for gene design. The initiative aims to precisely control recombinant protein expression in the industrial yeast Pichia pastoris. This project marks a significant advancement that promises to expedite the development of therapeutics, enzymes, and other proteins by using machine learning to optimise codon usage. This optimisation ensures compatibility with the production host and maximises production yields.

Ingenza’s codABLE® platform has already revolutionised protein expression in Bacillus subtilis, achieving maximum yields even for challenging proteins and enabling endotoxin-free manufacture of biologics. codABLE® sets itself apart by accurately predicting protein expression from specific gene designs, surpassing conventional codon optimisation algorithms used by commercial DNA synthesis providers. This capability provides a unique advantage in controlling protein expression without altering regulatory regions such as promoters or ribosome binding sequences.

The innovation funding from Innovate UK will enable the Ingenza to establish and refine its proven algorithm in P. pastoris, renowned for its biomanufacturing capabilities. This endeavour involves deploying rapid, ultra-high throughput screening and next-generation sequencing (NGS) to analyse millions of gene variants. The resulting dataset will empower codABLE® to uncover the intricate relationship between codon context and protein expression for valuable protein targets.

Rita Cruz, Head of Strain Development at the Ingenza, remarked: “Ingenza's codABLE® machine learning algorithm represents a paradigm shift in gene design for predictable and optimised recombinant expression, addressing a longstanding challenge in engineering biology. This approach integrates state-of-the-art computational technology with Ingenza’s extensive expertise across multiple biomanufacturing hosts, enhancing our competitiveness and driving innovations in bio-based manufacturing.”

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