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Medicine

Title Description Supervisor(s)
Protein origami: New computational methods to predict protein folding ensembles

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How does a protein fold into its native state? This is one of the most important and challenging problems in the chemical sciences, and a key question in understanding diseases driven by protein misfolding and aggregation (such as Parkinson’s disease). In this project, we will develop and employ a new computational scheme to access long time-scale protein folding events by mapping onto a discretized connectivity-based description of protein structure. This new approach will then enable us to investigate sequence-specific folding effects and translational folding, as well as providing a new scheme for protein-structure prediction. Scott Habershon, Gabriele Sosso
Exploring the Heterogenous Biogenesis Pathways of the Bacterial Cell Envelope

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The cell envelope serves as the front-line to both defence and pathogenicity in bacteria. Critical protein assemblies are required to mature, localise and assemble proteins, sugars and lipids around the cell to enable protection against antibiotics, phages and toxins, and to modulate cell structure and shape. A major component of this is the essential extracellular cell wall, which forms a mesh-like coat around the cell. Its assembly is the target for many antibiotics. Here we will use molecular simulation to study the protein machinery responsible for the formation of the cell wall and other biogenesis pathways within the cell envelope. Phillip Stansfeld, David Roper
Modelling noisy biochemical switches and networks

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Biological systems often need to switch behaviour due to environmental (e.g. temperature) and other stimuli (e.g. cell-cell interactions). Yet, understanding how cells can make reliable decisions given the inevitable noisiness of the intracellular conditions remains an open problem. Here, we will develop stochastic models of small biochemical networks that undergo stochastic switching in behaviour. The project involves building a robust software framework for exploring the role of spatial and temporal fluctuations in protein number within different boundary constraints. Working closely with experimentalists (Loose lab, IST-Austria), we will generate predictions that can then be directly tested. Timothy Saunders, Phillip Stansfield
Untangling disorder in amorphous pharmaceuticals using artificial intelligence

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The best pharmacologically active compound may have been found, but its function, properties and processability are heavily dependent on the solid form. Solid-state Nuclear Magnetic Resonance and diffraction experiments, coupled with quantum mechanical calculations are powerful tools to elucidate the atomic structure, which is needed to understand and control macroscopic properties. The challenge in amorphous systems is that experiments only provide globally averaged measurements, while large length scales are needed to capture the disorder, which render quantum mechanical calculations unfeasible. In this project, machine learning models will be used to bypass the need for these costly computations, allowing interpretation of experimental data and structure determination with unprecedented accuracy. Albert Bartok-Partay, Steven Brown