Engineering novel RNA Polymerases for RNA vaccine production.

A new generation of vaccines is under development by world-leading pharmaceutical companies. These vaccines are based on RNA - a natural molecule that transfers information from the DNA code into the protein antigen in our own bodies so that we effectively produce our own vaccine. This self produced vaccine then induces an immune response and protects our body during the future exposure to the infection1. Crucially these vaccines don't require cold-chain distribution, which can dramatically increase vaccines availability in developing countries. Another advantage of RNA vaccines is uniform manufacturing capabilities - synthesis of RNA encoding different protein antigens requires the same key steps enabling rapid response in the event of outbreaks and epidemics.
The key limitations in RNA manufacturing are: high cost, low yield, prolonged synthesis time and presence of impurities. These impurities contain short RNAs which are byproducts of premature termination of RNA synthesis2.
By developing novel enzymatic tools for RNA synthesis we can reduce the manufacturing requirements, time, cost and quality of RNA vaccines. This will enable accessibility of vaccines in the developing world due to cheaper prices and overcoming the cold-chain distribution bottleneck.
We intend to improve RNA synthesis efficiency and quality by identifying and engineering novel RNA polymerase enzymes by combining the cutting edge technologies: synthetic biology with high-throughput screening. Using computational approach we have picked and characterized a number of novel RNA polymerases from different organisms. In addition in order to further optimize enzymatic activity we have developed a bespoke high-throughput screen that enables us to pick the best RNA producing enzymes, which synthesise the highest quantity of the full-lenght RNA.
In this PhD project we will create a library of variants of new candidate enzymes and use our novel screen to identify those with improved characteristics. The best candidates will be taked forward to new rounds of directed evolution.We will use data analysis, sequencing and machine learning to predict and test the best combination of the mutations to enhance the final enzyme properties. We will do some proof of principle in vivo translation (production of antigen protein) of the main components of two model vaccines: against tuberculosis and cholera. The results will contribute to speeding up the development of RNA vaccines, their cost reduction and accessibility in the developing contries where the cold-chain disctribution is a major limiting factor.