Abstract

The solute carrier (SLC) family encompasses a diverse group of membrane transporters essential for maintaining cellular homeostasis by facilitating the transport of substrates such as nutrients, neurotransmitters, and ions. Despite their biological importance, understanding the structural and functional impacts of disease-relevant mutations remains a significant challenge due to the complexity and diversity of these transporters. Among all, the SLC6 transporters are of particular interest due to their critical roles in neurotransmitter reuptake and their clinical relevance in neurological and developmental disorders. This dissertation integrates computational and experimental approaches to investigate the pathogenic mechanisms of SLC mutations. The work combines family-wide analyses, such as ProteoMutaMetrics and hotspot studies, with case-specific investigations of individual mutations to provide a comprehensive view of mutation-induced dysfunction. Family-wide studies systematically assessed mutation impacts on structural and functional levels, revealing conserved helices critical for substrate transport and extracellular loop regions as mutation hotspots, where mutations often disrupt protein-protein interactions. Machine learning models further enhanced the ability to predict mutation pathogenicity by integrating structural and sequence features. Case-specific studies, such as those examining the E1065K mutation in SLC12A4 and the Q332E mutation in SLC6A4, provided detailed insights into unique dysfunction mechanisms, including structural destabilization and impaired gating dynamics. These investigations demonstrated how mutations lead to transporter misfunction through localized and systemic disruptions. In summary, this dissertation bridges the gap between structural modeling and functional analysis, offering tools and insights to prioritize mutations for experimental validation and therapeutic exploration. It contributes to a deeper understanding of SLC transporter biology and lays the groundwork for addressing mutation-driven diseases through pharmacological and computational innovations.