# Applying Spatial Covariance to Understand Human Variation in Genetic Disease > **NIH NIH R01** · SCRIPPS RESEARCH INSTITUTE, THE · 2023 · $452,500 ## Abstract 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 are composed of select clusters of SCV defined residue-residue spatial relationships in the protein structure that serve as master regulators for presentation of CFTR to the COPII ER export machinery through a cytosolic exposed 'YKDAD' exit code. We hypothesize that COPII components differentially respond to SCV set-points impacted by genetic variation to generate disease in the individual. We will determine the impact of genetic variation for each of the steps dictating COPII assembly to understand those events responsible for pathophysiology. In Aim 3, we further hypothesize based on GP logistics that variant CFTR polypeptides will be highly responsive to novel correctors that directly interact with the fold to restore function. We will utilize an SCV- based 'triangulation' approach to identify small molecules that directly impact the stability of the YKDAD exit motif defective in F508del and other variants to identify compounds that affect a cure for CF using in silico computational ... ## Key facts - **NIH application ID:** 10734426 - **Project number:** 1R01HL166410-01A1 - **Recipient organization:** SCRIPPS RESEARCH INSTITUTE, THE - **Principal Investigator:** William Edward Balch - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2023 - **Award amount:** $452,500 - **Award type:** 1 - **Project period:** 2023-08-01 → 2027-04-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10734426 ## Citation > US National Institutes of Health, RePORTER application 10734426, Applying Spatial Covariance to Understand Human Variation in Genetic Disease (1R01HL166410-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10734426. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*