Uncovering the Impact of Conformational Dynamics and Allostery on Genetic Diseases, Epistasis, and Evolution

Proteins, the machinery of life, perform a vast array of essential biochemical functions, evolving over time to acquire diverse roles within biological systems. This evolution, primarily driven by mutations within protein sequences, can profoundly impact protein function, potentially leading to various diseases. This thesis aims to dissect the intricate mechanisms through which genetic mutations influence protein functionality, focusing on the dynamic alterations induced by single and combined mutations. Employing a suite of computational tools, including molecular dynamics (MD) simulations and proven analysis metrics like the Dynamic Flexibility Index (DFI) and Dynamic Coupling Index (DCI), I analyze protein dynamics to uncover the common dynamic effects associated with disease causation and compensatory mechanisms. This analysis extends to exploring the concept of epistasis through the lens of protein dynamics, showing how combinations of mutations interact within the protein's 3D structure to either exacerbate or mitigate the functional impacts of individual mutations. The use of EpiScore, a computational tool designed to quantify the epistatic effects of mutations, provides insight on the combined dynamic effects two mutations might have. This is particularly evident in the analysis of rare alleles within human populations, where certain allele combinations, despite their individual rarity, frequently co-occur, suggesting a mechanism of dynamic compensation. This phenomenon is further investigated in the context of the SARS-CoV-2 spike protein, providing insights into viral evolution and the adaptive significance of specific mutations. Additionally, I delve into the role of Intrinsically Disordered Regions (IDRs) in protein function and mutation compensation, highlighting the need for sophisticated dynamics analysis tools to capture the full spectrum of mutation effects. By integrating these analyses, this thesis unveils a complex picture of how proteins' dynamic properties, shaped by mutations, underpin their functional evolution and disease outcomes.