Jonathan Jenkins


My research is an exciting mix of wet lab work and computational modelling looking at completely artificial enzymes (de novo enzymes). Enzymes are proteins used by all living things to perform their chemical reactions. The overall aim of my research area is to understand the minimum structural requirements of an enzyme to give a particular chemical activity. We use a bottom-up approach, starting with a simple synthetic protein scaffold that has no activity. Through an iterative process, guided by computational modelling, we design in activity in a way that the effect of each individual change made to the scaffold can be explored. Understanding how enzymes work and what structural rules are needed to build them could revolutionise the use of enzymes in both industrial and medical applications. It would open new possibilities to design bespoke enzymes that solve specific problems like; degrading environmental pollutants, producing more complex and specific drug molecules in synthetic chemistry or as a targeted treatment for diseases like cancer.

I use a synthetic four helix bundle as a protein scaffold that is free from evolutionary complexity. Heme C is covalently bound as a cofactor with the aim of performing catalysis analogous to natural P450 enzymes. My lab work is guided by computational modelling of both the de novo enzymes and natural enzymes that perform similar chemistry. I use molecular dynamics simulations and structural prediction software like Amber and Rosetta to model structural changes in the synthetic enzymes. I also use quantum mechanical (QM) and hybrid QM/MM modelling of natural heme containing peroxidases to better understand the heme species found in the reactive cycle.


The de novo enzyme lab work is carried out with Dr Ross Anderson in the School of Biochemistry and the computational component with Professor Adrian Mulholland in the Centre for Computational Chemistry. My PhD is funded by the BBSRC as part of the SWBio Doctoral Training Partnership, I have also benefited from equipment and computation resources associated with BrisSynBio.

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