Combining Machine Learning, Molecular Dynamics and Membrane Biophysics to identify new therapeutics for the treatment of Tuberculosis

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Tuberculosis (TB) is currently one of the world's leading causes of mortality with 10 million new cases reported in 2017 alone and 1.3 million deaths (Global Tuberculosis Report 2018 WHO), a further complicating factor is the evolution of multi-drug and totally drug resistant strains. There is an urgent need to develop effective new therapeutic agents to target TB and critical in this process is the identification of a suitable protein to target. MmpL3 is a transmembrane protein which is essential for the replication and viability of bacterial cells and therefore represents a suitable target. The recent determination of the structure of MmpL3 from M. smegmatis (Cell 2019; 176: 636-648) provides the starting point for developing new therapeutic strategies. Molecular Dynamics (MD) simulations will be utilised to construct a model of MmpL3 for M. tuberculosis (Mtb) facilitating investigation of drug-protein interactions, known inhibitors will be modelled at physiological conditions with the protein embedded in a realistic representation of the cell membrane. Validation of the computational model will be achieved through the investigation of the structure and mechanics of model membranes, in which Mtb MmpL3 is embedded, via X-ray diffraction and light microscopy. Identification of the binding modes of know inhibitors to Mtb MmpL3 and known drug resistant mutants will be used as input into Machine Learning (ML) to generate rules to search large compound libraries, in particular the Zinc database, to identify suitable compounds to screen. This project will provide the student with a broad range of skills, computational modelling, machine learning, protein expression and purification and experimental membrane biophysics.