Designing and synthesising new classes of phosphatase inhibitor using novel computational methods

Phosphatases are responsible for removing the phosphate groups and play a role in conjunction with kinases (that do the opposite) in controlling many signalling networks in cells. Malfunctioning of this signalling and the biochemical processes they control is associated with a range of diseases including diabetes, some cancers and infections. Despite this important activity, with tantalising prospects for new treatments, phosphatases are often viewed as undruggable - that is that it is thought impossible to obtain molecules that can block the phosphatase but also have the sort of physical properties that are required in a drug. In this project, you will apply novel computational techniques to probe whether they can provide a route map towards new molecule types that are able to achieve this balance. The project will allow the most exciting computational designs to be synthesised and then tested thanks to the breadth of experience of the supervisory team.
The project will first explore the known structure-activity relationships for a number of phosphatases of therapeutic interest. This will include data in the public domain but also those measured in Manchester. The same features that make phosphatases challenging for drug discoverers (tuned to bind multiply-charged anionic groups with long bonds to phosphorous) make them challenging to study computationally and the student will therefore spend some time exploring the levels of theory available (using quantum mechanical calculations) and considering protein flexibility (using molecular dynamics calculations) in order to derive a satisfactory model. This model and the insights provided during its derivation will be used to design new potential inhibitors that employ non-covalent and covalent modes of action. These will then be subjected to calculations with the best modelling approaches. Candidates would not need experience in using any of these calculation types although some understanding of their background would be beneficial.
Subsequently, the student will be able to choose to either synthesise and test some of the best molecules or else to explore a wider range of phosphatases computationally in order to explore whether this class of enzymes might be amenable to some of the latest in machine learning approaches in order to generalise the quantum mechanical/dynamical calculations.