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

Lead Research Organisation: Imperial College London
Department Name: Dept of Chemistry

Abstract

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.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S023518/1 01/10/2019 31/03/2028
2277910 Studentship EP/S023518/1 01/10/2019 30/09/2023 Sara Cioccolo