Applying Spatial Covariance to Understand Human Variation in Genetic Disease

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 screening and experimental validation. The combined efforts outlined in Aims 1-3 will allow us to
define a genome based mechanistic foundation for how the fold can be reprogrammed for optimal fitness in the
individual by reducing the impact of variation triggering human genetic disease.

Grant Number: 5R01HL166410-04
NIH Institute/Center: NIH
Principal Investigator: William Balch