Machine learning augmented molecular simulation pipelines for modelling allosteric modulation of protein function

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This project will focus on developing computational methodologies grounded in molecular simulation and machine learning for predicting how the binding of a ligand to the surface of a protein will modulate its biological function. Such capability may facilitate the rational design of allosteric modulators of protein function which of enormous importance in drug discovery. This work builds on Markov State Modelling methodologies and alchemical free energy calculation methodologies that the Michel research group has recently developed to simulate large scale conformational changes in protein structures (Chem. Sci. , 11, 2670-2680, 2020) and to estimate protein-ligand binding affinities (Chem. Sci. , 13, 5220-5229, 2022). Throughout the project there will be opportunities to interact with pharmaceutical companies interested in such methodologies.