Core Concepts
Mutations that disrupt cotranslational membrane integration (V276T) or tertiary structure (W107A) of the gonadotropin-releasing hormone receptor (GnRHR) form distinct epistatic interactions that depend on the mechanism and severity of destabilization.
Abstract
The authors investigated how epistatic interactions between mutations are shaped by the folding defects in an unstable membrane protein, the gonadotropin-releasing hormone receptor (GnRHR). They compared the epistatic interactions formed by two GnRHR variants - V276T, which disrupts cotranslational membrane integration, and W107A, which disrupts the native tertiary structure.
Key insights:
Mutations that form negative epistatic interactions with V276T are predominantly located in soluble loops, while mutations that form positive epistatic interactions with W107A are found across both loops and transmembrane domains.
The distinct epistatic patterns arise from differences in how the V276T and W107A mutations destabilize the protein. V276T, which disrupts cotranslational folding, exhibits more pronounced negative epistasis, while W107A, which disrupts the native fold, exhibits positive epistasis as additional destabilizing mutations have diminishing impacts.
An unsupervised clustering analysis revealed that mutations in transmembrane domains tend to form positive epistatic interactions with W107A, likely because the protein is already predominantly misfolded in this background.
The findings suggest that the distinct biosynthetic mechanisms of membrane proteins can differentially shape their fitness landscapes through context-dependent epistatic interactions.
Stats
Mutations that disrupt cotranslational membrane integration (V276T) reduce the plasma membrane expression of GnRHR by 65 ± 4% relative to wild-type.
Mutations that disrupt the native tertiary structure (W107A) reduce the plasma membrane expression of GnRHR by 88 ± 4% relative to wild-type.
Quotes
"Mutations that generate Stage I and Stage II folding defects form distinct epistatic interactions throughout this receptor."
"An unsupervised learning analysis reveals how these interactions depend on both changes in stability and the topological context of the mutation."