Tracing nature's template: using statistical machine learning to evolve biocatalysts. In this project new computational methods will be developed to design nature-inspired, biological catalysts for industrial purposes. Such methods will enable catalysts to be designed that can improve the effectiveness and environmental footprint of drug development, agricultural and specialist chemical production and environmental remediation.
Reconstructing proteins to explain and engineer biological diversity. The aim of this project is to develop computational methods to construct entirely new proteins. Computational reconstruction of enzymes that have been extinct for over 400 million years has revealed remarkable opportunities for biotechnological innovation. The intended outcomes are to develop bioinformatics methods to broaden the scope of ancestral protein reconstruction to include protein super-families, to establish what spe ....Reconstructing proteins to explain and engineer biological diversity. The aim of this project is to develop computational methods to construct entirely new proteins. Computational reconstruction of enzymes that have been extinct for over 400 million years has revealed remarkable opportunities for biotechnological innovation. The intended outcomes are to develop bioinformatics methods to broaden the scope of ancestral protein reconstruction to include protein super-families, to establish what specific changes led to the evolutionary success of a protein, and to re-run evolution to generate proteins that perform in conditions suitable for industrial and agricultural applications, in particular the production of hydroxylated fatty acids for bioplastics. By examining proteins from many life forms, the project plans to develop a novel bioinformatics strategy to understand their evolution and engineer new proteins for use in production of chemical commodities.Read moreRead less