Yield enhancement of recombinant enzyme production

Many natural proteins that have biotechnological or pharmaceutical applications cannot be produced in the desired quantities, or are only stable to a limited extent. This results in higher production costs and a larger ecological footprint. This project is testing computer- aided methods to increase the stability and yield of production. These methods have been trained using machine learning to predict which amino acids the building blocks of proteins need to be replaced to increase stability and yield while maintaining functionality and structure. Using two model proteins, we will evaluate different methods for designing and evaluating the designed sequences, and identify the most effective methods. A set of designed proteins, computed and selected using various methods, will be produced in bacteria and tested for properties such as stability and activity. Building on this knowledge, our aim is to develop a general method for modifying amino acid sequences to increase protein yields.

No additional funding sources recorded.