# Applying Spatial Covariance to Understand Human Variation in Genetic Disease

> **NIH HL R01** · SCRIPPS RESEARCH INSTITUTE, THE · 2026 · $460,000

## 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 a

## Key facts

- **NIH application ID:** 11318888
- **Project number:** 5R01HL166410-04
- **Recipient organization:** SCRIPPS RESEARCH INSTITUTE, THE
- **Principal Investigator:** William Edward Balch
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** HL
- **Fiscal year:** 2026
- **Award amount:** $460,000
- **Award type:** 5
- **Project period:** 2023-08-01T00:00:00 → 2027-04-30T00:00:00

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11318888

## Citation

> US National Institutes of Health, RePORTER application 11318888, Applying Spatial Covariance to Understand Human Variation in Genetic Disease (5R01HL166410-04). Retrieved via AI Analytics 2026-07-13 from https://api.ai-analytics.org/grant/nih/11318888. Licensed CC0.

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