Development of improved methods for predictive modelling of G protein-coupled receptors

Abstract

G protein-coupled receptors (GPCRs) are a superfamily of transmembrane proteins that play crucial roles in cell physiology. Being key drug targets in many diseases, drug discovery efforts harness structural information about these receptors to rationally design drugs in the field of structure-based drug design (SBDD). This thesis presents the development of computational tools to manage and assess large GPCR structural model datasets, applies these tools to define key features linked with the best performing models in SBDD and integrates this knowledge into a novel computational workflow that predicts new GPCR models, which may offer new opportunities in SBDD programs.