Modelling Gene Translation into Protein

A new predictive model for more efficient production of proteins as pharmaceuticals, industrial biocatalysts or numerous other engineering biology applications.

Introduction

Efficient expression of proteins using engineered microbes underpins a broad range of commercial applications. Proteins themselves may be used as therapeutics, or industrial enzymes or they may be used concertedly to produce valuable molecules such as pharmaceuticals, chemicals or polymers.

Challenge

Ingenza wanted to improve the success rate that a given gene design would direct efficient protein production in an engineered, industrially-suitable microbe. The efficiency of translation, the process by which proteins are synthesised using information encoded in mRNA, is critically influenced by the composition and context of mRNA “codons”, nucleic acid triplets specifying proteins amino acid sequence. For optimal efficiency, the usage and context of mRNA codons must closely align with the cell’s ability to supply tRNA molecules, delivering specified amino acids to the nascent protein being synthesised on the ribosome. Bringing greater predictability to this process would increase and accelerate success in engineering biology - a “holy grail” that has persistently eluded researchers.

Solution

Ingenza teamed up with Professors Ian Stansfield and Carmen Romano at the University of Aberdeen to apply and iterate novel models of mRNA translation, addressing what stresses high levels of protein production induced in the cells. The Aberdeen researchers had  developed the baseline model which was able to predict which tRNAs were being depleted and this was then validated for multiple commercial targets using lab experimentation.

Outcome

Ingenza assessed seven iterations of the Stansfield/Romano model using proven industrial protein production strains, establishing where tRNA limitation could impede protein production. The company now has proprietary mathematical models for yeast and bacterial hosts that can predict mRNA design to overcome tRNA limitations. Stimulated by project success Ingenza continued to research predictive algorithms of mRNA codon usage/context, achieving major uplifts in the production of proteins as biologics, industrial enzymes and novel COVID-19 protein-subunit vaccines.

This project has also allowed Prof Stansfield to demonstrate his expertise in whole cell modelling, which has resulted in invitations to prestigious consortia and research bodies in the area.