Regulators of
G protein signaling (
RGS) proteins modulate
G protein-coupled receptor (GPCR) signaling by acting as negative regulators of
G proteins. Genetic variants in
RGS proteins are associated with many diseases, including
cancers, although the impact of these mutations on
protein function is uncertain. Here we analyze the RGS domains of 15 RGS
protein family members using a novel bioinformatic tool that measures the missense tolerance ratio (MTR) using a three-dimensional (3D) structure (3DMTR). Subsequent permutation analysis can define the
protein regions that are most significantly intolerant (P < 0.05) in each dataset. We further focused on RGS14, RGS10, and RGS4. RGS14 exhibited seven significantly tolerant and seven significantly intolerant residues, RGS10 had six intolerant residues, and RGS4 had eight tolerant and six intolerant residues. Intolerant and tolerant-control residues that overlap with pathogenic
cancer mutations reported in the COSMIC
cancer database were selected to define the functional phenotype. Using complimentary cellular and biochemical approaches,
proteins were tested for effects on GPCR-Gα activation, Gα binding properties, and downstream cAMP levels. Identified intolerant residues with reported
cancer-linked mutations RGS14-R173C/H and RGS4-K125Q/E126K, and tolerant RGS14-S127P and RGS10-S64T resulted in a loss-of-function phenotype in GPCR-
G protein signaling activity. In downstream cAMP measurement, tolerant RGS14-D137Y and RGS10-S64T and intolerant RGS10-K89M resulted in change of function phenotypes. These findings show that 3DMTR identified intolerant residues that overlap with
cancer-linked mutations cause phenotypic changes that negatively impact GPCR-
G protein signaling and suggests that 3DMTR is a potentially useful bioinformatics tool for predicting functionally important
protein residues. SIGNIFICANCE STATEMENT: Human genetic variant/mutation information has expanded rapidly in recent years, including
cancer-linked mutations in regulator of
G protein signaling (
RGS) proteins. However, experimental testing of the impact of this vast catalogue of mutations on
protein function is not feasible. We used the novel bioinformatics tool three-dimensional missense tolerance ratio (3DMTR) to define regions of genetic intolerance in
RGS proteins and prioritize which
cancer-linked mutants to test. We found that 3DMTR more accurately classifies loss-of-function mutations in
RGS proteins than other databases thereby offering a valuable new research tool.