Accurate energy evaluation at atomic level and establishing NMR chemical shifts as reliable reporters of atomic charge distributions

The project has two objectives. The first is to develop a next generation force field that will allow accurate energy evaluation at atomic level. This objective will be explored with the industrial partner AstraZeneca (See next section). The second is to use related approaches to establish how and to what extent NMR measurements in proteins can be used as a reliable surrogate reporter of the charge distributions at atomic level.
For the second objective, we will focus on molecular interactions that deliver catalysis within enzymes. While knowledge of enzyme catalysis is ever-expanding, fundamental questions relating to the quantification of intra- and intermolecular interactions still limit the ability to predict their catalytic behaviour. The distribution of charge in enzyme active sites, how it relates to atomic positions, and how it changes during the catalytic cycle, are of critical importance to develop a level of understanding where new catalysts can be reliably designed. Currently, access to such information at the accuracy required to understand and predict catalytic rate enhancements dependably, requires a combination of very high resolution structural biology and very high level quantum calculations, along with accurate methods to partition the calculated electron densities to individual atoms. The required resolution is only seldomly available in experimentally determined protein structures, and current computational capabilities dictate that relatively few atoms (<200) can be accommodated in the appropriate quantum calculations, meaning that the generated picture is necessarily limited.
Our preliminary work has established that charge partitioning using the Quantum Theory of Atoms in Molecules (QTAIM) method, derived from the Quantum Chemical Topology (QCT) approach can deliver proof of a quantitative predictable framework linking NMR chemical shifts to atomic charges. The focus is now to take these exciting preliminary data and extend QTAIM derived predictions to enzymes where we have the necessary experimental NMR and X-ray data available for various complexes that represent the catalytic cycle, and appropriate QM models for these. The derived understanding of atomic charge distributions, and how they relate to molecular structure and bond formation / cleavage, at different stages of the catalytic cycle has far reaching consequences for the future exploitation of catalysis.

the approach that will be taken to answer these questions (what the student will actually be doing);
The REG method has been developed in the Popelier group and comes with an in-house code called REG.py. The student will be programming in Python, modifying the in-house code REG.py to interface them with the problem of crystal structure prediction. He will investigate the streamlining and speeding up of the REG procedure leading to a severe reduction of computation time. The student will be involved with careful and systematic testing and thus gather unprecedented quantum mechanical insight.


the novel engineering and/or physical sciences content of the research (the science that places it within EPSRC's remit).
This project resorts under the Chemical Sciences Grand Challenge of "Directed Assembly of Extended Structures with Targeted Properties (DAESTP)". There is a strong Machine Learning component to the first objective of the project, and thus overlap with Artificial Intelligence, a popular funding topic. The associated scientific product is called FFLUX, which is a completely new force field, designed by novel principles and encoded as a software package.