Supervisors: Phillip Stansfeld, Livia Bartok-Partay

Summary:

The bacterial cell envelope is the front-line to killing drug-resistant, pathogenic bacteria. The development of new protein structure prediction methods (e.g. RoseTTA All-Atom AlphaFold and ESMFold) have enabled the accurate computational determination of over 600 million protein structures. This dataset enables the study of entire bacterial membrane proteomes from the perspective of structure-based drug discovery. As part of this PhD studentship, you will develop and apply methods to identify and characterize binding pockets, predict candidate small molecule and antibody binding, and perform free energy calculations of molecules bound to folded protein structures. The overall aim of this PhD proposal is to develop blueprints for new medicines to treat drug-resistant bacterial infections.

Background:

The development of the AI-based protein structure prediction software, AlphaFold2, has enabled the accurate computational determination of over 130,000,000 individual protein structures. This dataset enables the study of entire bacterial membrane proteomes from the perspective of structure-based drug discovery. Molecular simulation methods, used in the Stansfeld group, enable the assembly of lipid membranes around protein structures. This allows for a more appropriate description of the protein’s molecular setting and therefore enables full details of the accessibility of binding sites to the surrounding environment. It also permits molecular motions and interactions of both binding sites and potential drug candidates in the context of the lipid membrane. The nested sampling methods developed in the Bartok-Partay group have the potential to be applied to explore the viability of potential binding sites while giving access to the free energy, and to enable unbiased sampling of flexible loop conformational changes around these sites.

Our aim is to exploit the developments in AI-based protein structure prediction by developing and applying molecular modelling techniques to define molecular binding sites.