From Mutations to Disease: Computational Analysis and Interpretation of GPCR-Associated Pathogenicity

G protein-coupled receptors (GPCRs) perform critical roles in numerous physiological processes and their mutations are, therefore, associated with various human diseases. Hence, understanding the molecular consequences of pathogenic mutations in GPCRs is essential for elucidating disease mechanisms and developing effective therapeutic strategies. In this study, we employed computational approaches to explore the impact of mutations on GPCRs using two distinct datasets: ClinVar and MutHTP. We first evaluated the performance of available pathogenicity predictors. Beyond that, we used statistical analysis to identify key characteristics of mutations in GPCRs leading to diseases. We first evaluated available computational predictors, such as SIFT, PolyPhen-2, PROVEAN, ESM1b, and AlphaMissense in classifying GPCR mutations. During this task, we observed that all predictors performed with reliability when assessing GPCR mutations leading to diseases in the ClinVar dataset. On the other hand, when dealing with the MutHTP dataset, all predictors demonstrated poor performance, emphasising the importance of dataset characteristics and the need for comprehensive evaluation when selecting mutation predictive tools for GPCR analysis. The statistical analysis of mutations on GPCRs and disease development suggests that mutations occurring in conserved regions or regions with stronger intermolecular interactions are more likely to disrupt protein function and contribute to disease pathogenesis. Additionally, regarding our analysis, we also obtained insights into the importance of hydrophobic interactions and hydrogen bonding patterns in mutations in GPCRs and pathogenicity. Overall, our study enhances our understanding of the molecular mechanisms underlying GPCR-associated diseases and provides valuable insights for future research and clinical diagnostics in this field.