A molecular simulation and machine learning framework for the rational discovery of allosteric modulators of protein function

This project will focus on developing a computational methodology that combines molecular simulation, information theory and machine learning methods for predicting how the binding of drug-like small molecules to different locations on the surface of a protein will modulate the biological function of the protein. Such so-called allosteric modulators are of particular interest to tackle challenging drug targets. The computational methodology will be applied to compounds binding to pharmaceutically important enzymes or protein-protein interactions. We are particularly interested in characterising the potential of hit molecules, weak binders typically identified at the early stage of a drug discovery campaign by fragment screens, to be developed into potent allosteric modulators. The work will build on molecular dynamics simulation and free energy calculation methodologies the Michel lab has recently used to elucidate allosteric effects in proteins (Chem. Commun. 2019), and to guide rational drug design efforts (Chem. Sci. 2019). The project will be carried out in collaboration with the biopharmaceutical company UCB and involve placements at their R&D site. This is an exciting opportunity to develop, validate and apply next-generation computer-aided drug design software and methodologies. Upon completion of the studentship, the successful applicant will have gained strong technical expertise in molecular modelling and learned to work closely with the pharmaceutical industry sector. This will prepare him or her well for a future career in academia or industry.