Phenotypeca’s yeast codon optimisation and gene design algorithms are being automated in a prestigious Royal Society Short Industry Fellowship Project, which has been awarded to Dr Tobias von der Haar, Director of Research and Reader in Systems Biology at the University of Kent’s Division of Natural Sciences. Tobias’ research group has a long-standing interest in studying how evolution produces efficient genes in nature. This project aims to apply the knowledge gained from work with natural genes to achieve a more efficient design of the synthetic genes used in Phenotypeca’s industrial processes.
Codons are the three-letter (trinucleotide) sequences in DNA and RNA that code for specific amino acids within proteins. There are 64 different codons possible from the four DNA bases (A, C, G, and T) in the genetic code. 61 codons specify which of the 20 amino acids are added to protein polypeptides being synthesised by ribosomes inside the yeast cell, while the remaining three are stop signals. The speed and efficiency of recombinant protein production by Phenotypeca’s yeast cell factories can be maximised if the correct codon is used for each amino acid. While codon choice directly affects product yields from Phenotypeca’s yeast, it can also improve product quality because efficient protein synthesis and folding can reduce undesirable post-translational modifications.
“Phenotypeca is delighted to work with Tobias von der Haar, a leading world expert in coding sequence design for recombinant protein production with a wealth of knowledge of ribosome speed models for Saccharomyces cerevisiae (baker’s yeast). This collaboration will ensure the optimal gene design of expression constructs for our customers’ proteins”, says Chris Finnis, Phenotypeca’s Research Director. “Automation of software algorithms for in-house use also ensures the confidentiality of our customers’ protein sequences in our PhenoStart™ and PhenoDev™ expression services.”
Phentoypeca's technology relies on screening billions of yeast genome variants to identify the best variants for making a protein of interest. To exploit the potential of this approach, the synthetic genetic sequence encoding the protein of interest must be both genetically stable and optimised to prevent the sequence itself from becoming a bottleneck. For COVID-19 antigen production and our Vax-Hub VLP vaccine projects with Oxford's Jenner Institute, this approach was applied, resulting in coding sequences with optimal product yield.
Automation of current gene design processes will increase throughput and completion times for our expression studies by reducing the FTE time required and increasing the quality and number of genes designed. This will effectively allow specialist codon-optimisation to be performed in-house by Phenotypeca’s research staff and adaption of the current methods ready for high-throughput automated studies with machine learning/AI in the future.
As an academic-industry collaboration supported by The Royal Society, this project strengthens Phenotypeca’s links to Tobias’ research group and other valuable recombinant protein expression expertise at the University of Kent, including the Kent Fungal Group. As well as allowing the software design in Kent, this project provides for knowledge exchange and travel between the sites to support future collaborations and publications. Synthetic genes will then be fed into Phenotypeca’s existing workflows in Nottingham to generate the laboratory results needed for software validation and address underlying scientific questions on the adaptability of yeast to different codon usage.