A deep understanding of enzymatic processes requires an atomic-level description, which is often beyond experimental techniques but achievable through synergy with computational chemistry. This purely computational project proposes a step change in the modelling of an enzyme’s active site by building on the foundations laid by a novel force field method called FFLUX. This method is based on the best modern definition of an atom inside a molecular system. FFLUX uses machine learning to train these atoms to “know what to do” in a previously unseen atomic configuration. In other words, quantum mechanically accurate energies and forces between atoms in an enzyme’s active site are readily available. Hence, the potential energy surface of the active site can be quickly and accurately explored, both statically and dynamically. A suitable prototype case for using FFLUX in enzyme engineering are terpenoids. They are the most abundant and largest class (>75,000) of natural products. Most are commonly found in plants, with biological roles ranging from interspecies communication to intracellular signalling and defence against predatory species. Their commercial use is wide ranging as pharmaceuticals, herbicides, flavourings, fragrances and biofuels. Molecular dynamics simulations suggest that the monoterpene synthase class of enzymes do not undergo large-scale conformational changes during the reaction cycle (after initial substrate binding), making them attractive targets for structured-based protein engineering to expand their catalytic scope toward alternative monoterpene scaffolds. This very important class of compounds has been thoroughly studied in the MIB, enriching interaction with experimentalists. The aim is creating a step change in the realism of modelling an enzyme’s active site. A rigorous yet accessible quantum mechanical description of a reaction is vital for ultimate progress. Only with detailed atomic insight into the mechanism of an enzymatic reaction, using FFLUX-for-enzymes, can one correctly rationalise the design of future enzymes.
Contact for further Information Prof Popelier, [Email Address Removed] http://www.qct.manchester.ac.uk
Applications are invited from self-funded students. For UK tuition fees are £6,000 and International are £27,000 for 2022/23 academic year.
Academic background of candidates Candidates are expected to hold (or be about to obtain) a first class honours degree (or the overseas equivalent) in chemistry or physics. Candidates with experience in machine learning or with an interest in quantum chemistry are encouraged to apply.
 P. L. A. Popelier, Int.J.Quant.Chem., 2015, 115, 1005–1011.  P. L. A. Popelier, in The Chemical Bond - 100 years old and getting stronger, ed. M. Mingos, Springer, Switzerland, 2016, pp. 71-117.  V. Karuppiah et al., ACS Catal. , 2017, 7, 6268−6282.
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