An integrative analysis of functional consequences of PKD2 missense variants on RNA and protein structures: a computational approach - Chandra Devi; Prashant Ranjan; Parimal Das (Egyptian Journal of Medical Human Genetics)
Abstract Background The PKD2, encoding polycystin-2 (PC2) protein, is second major genetic determinant of autosomal dominant polycystic kidney disease (ADPKD) after PKD1. However, the structural and functional consequences of genetic variants in PKD2 remain poorly understood. Given the complexity and heterogeneous nature of ADPKD, understanding its pathogenesis at cellular and molecular levels is vital for deciphering genotype–phenotype correlations and disease severity, thus informing patient-centered treatments. We analyzed missense variants of PKD2 to assess their impact on RNA structure using computational tools and explored associated protein structure dynamics through MD simulation. Results Our findings reveal distinct structural alterations and dynamic behaviors associated with specific missense variants. The variants such as c.1789C > A (p.L597M), c.1109G > A (p.S370N), c.1849C > A (p.L617I), and c.646 T > C (p.Y216H) induced major changes not only in RNA structure and accessibility profile but also in protein structure dynamics. In contrast, variants such as c.915C > A (p.N305K), c.1354A > G (p.I452V), and c.568G > A (p.A190T) resulted in minor alterations in RNA structure but exhibited noticeable effects on certain parameters of protein structure dynamics. Conclusion This study suggests the multifaceted impact of these missense variants on both RNA and protein levels. It lays the groundwork in identifying high-impact variants in terms of pathogenicity and prioritizing these for further implications in understanding disease heterogeneity and eventually contributing to the development of targeted therapeutic interventions for ADPKD. Graphical Abstract Study Highlights PKD2 missense variants analyzed for impact on RNA and protein Structures Alterations in RNA Structures and protein dynamics observed Computational integration of analyses aids in prioritizing variants for further study Provide insights into disease heterogeneity and potential therapeutic targets