Project Summary/Abstract: The focus of this proposal is based on our ongoing efforts to link genetic sequence variation leading to changes in the protein fold triggering human genetic disease using an unprecedented variation spatial profiling (VSP) approach we have pioneered. VSP is a Gaussian process (GP) regression machine learning approach that utilizes human variation to assign function for each residue in the protein fold responsible for the genotype to phenotype transformation driving human biology- a new technology that is universal in application to any protein. VSP is built on the general principle of spatial covariance (SCV) which describes fundamental covariant relationships between all residues dictating the protein fold and function. These spatial relationships allow us to define with assigned uncertainty the role of each residue in genetic disease to define the residue-residue interactions that drive function in protein structure using variation capture (VarC). We focus on the cystic fibrosis transmembrane conductance regulator (CFTR), the causative agent of CF, as a model protein to understand SCV/VarC relationships dictating the impact of genetic variation on folding and trafficking through the exocytic pathway. To understand how genetic variation impacting protein fold design is managed by proteostasis folding and COPII based trafficking pathways, and how we can improve function in genetic disease by promoting protein fold fitness through small molecule correctors, we propose 3 goals. In Aim 1, we will utilize SCV relationships to dissect the contribution of the Hsp70 and Hsp90 chaperone/co-chaperone proteostasis systems we hypothesize are misaligned for the proper management of naturally occurring genetic variants triggering disease- and that these components can be retuned by adjusting their activity through molecular and chemical approaches. In Aim 2, we hypothesize that the proteostasis system generates SCV-defined 'set-points'. SCV set-points a