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Biology subjects

Davies, T. G.

Publications and source records attributed to Davies, T. G..

2 recordsLinked to original sources

Structure-function analysis of PI3K signalling cascade base editing screens in cancer cells

Knowledge of protein structure and function underpins rational drug discovery, yet many targets lack known selectively druggable sites. Furthermore, the identification of secondary druggable sites offers a strategy to overcome drug resistance. Fragment-based drug discovery (FBDD) can identify new ligandable binding pockets, though how to triage those with the ability to exert biologically relevant effects can be unclear. Systematic approaches to identify novel functionally-important protein sites for therapeutic intervention, such as allosteric pockets or protein-protein interaction (PPI) interfaces, has the potential to accelerate drug discovery, particularly when combined with structure-based hit-finding modalities. The phosphoinositide-3 kinase (PI3K) signalling pathway is frequently altered in human cancer and resistance to approved inhibitors is an ongoing challenge. Here, we performed large-scale CRISPR base editing mutagenesis screens across 30 PI3K pathway proteins in three disease-relevant cancer cell models to systematically map functional residues. Integration of base editing data with structural information identified residues corresponding to known catalytic sites, fragment-binding pockets and PPI interfaces, providing validation for the approach. Additionally, we identified putative allosteric pockets near regions of undefined function. Together, these findings establish high-throughput base editing mutagenesis combined with structural analysis as a scalable strategy to delineate structure-function relationships and inform drug development.

genomics↗

Mapping the space of protein binding sites with sequence-based protein language models

Binding sites are the key interfaces that determine a proteins biological activity, and therefore common targets for therapeutic intervention. Techniques that help us detect, compare and contextualise binding sites are hence of immense interest to drug discovery. Here we present an approach that integrates protein language models with a 3D tesselation technique to derive rich and versatile representations of binding sites that combine functional, structural and evolutionary information with unprecedented detail. We demonstrate that the associated similarity metrics induce meaningful pocket clusterings by balancing local structure against global sequence effects. The resulting embeddings are shown to simplify a variety of downstream tasks: they help organise the "pocketome" in a way that efficiently contextualises new binding sites, construct performant druggability models, and define challenging train-test splits for believable benchmarking of pocket-centric machine-learning models.

biochemistry↗