Loss-of-function, gain-of-function and dominant-negative mutations have profoundly different effects on protein structure: implications for variant effect prediction

Abstract

Most known pathogenic mutations occur in protein-coding regions of DNA and change the way proteins are made. Taking protein structure into account has therefore provided great insight into the molecular mechanisms underlying human genetic disease. While there has been much focus on how mutations can disrupt protein structure and thus cause a loss of function (LOF), alternative mechanisms, specifically dominant-negative (DN) and gain-of-function (GOF) effects, are less understood. Here, we have investigated the protein-level effects of pathogenic missense mutations associated with different molecular mechanisms. We observe striking differences between recessive vs dominant, and LOF vs non-LOF mutations, with dominant, non-LOF disease mutations having much milder effects on protein structure, and DN mutations being highly enriched at protein interfaces. We also find that nearly all computational variant effect predictors underperform on non-LOF mutations, even those based solely on sequence conservation. However, we do find that non-LOF mutations could potentially be identified by their tendency to cluster in space. Overall, our work suggests that many pathogenic mutations that act via DN and GOF mutations are likely being missed by current variant prioritisation strategies, but that there is considerable scope to improve computational predictions through consideration of molecular disease mechanisms.

Understanding molecular mechanisms and predicting phenotypic effects of pathogenic tubulin mutations

“…Notably, this aligns with recent work showing that the predicted effects on protein stability tend to be much milder in gain-of-function and dominant-negative mutations than for pathogenic mutations associated with a loss of function (37), supporting the idea that most pathogenic tubulin mutations are due to non-loss-of-function effects.…”

“…Each mutation was mapped onto the highest resolution structure from the dataset above. Here, the ΔΔG values were calculated considering the monomer only, as well as the whole biological assembly, in the same manner as previously described (37).…”

Saturation genome editing of DDX3X clarifies pathogenicity of germline and somatic variation

“…A recent study suggested that clinically-relevant loss-of-function variants in haploinsufficient genes are more likely to be observed at residues buried within protein structures, and be more disruptive to protein structure (as predicted by the change in Gibbs free energy of folding (ΔΔG)) 28 . We therefore calculated the ΔΔG of missense variants for the different SGE functional classes, and the proportion of these variants that are predicted to be buried within the core of the protein (using relative solvent accessible surface area from structural models) 29 .…”

Large protein complex interfaces have evolved to promote cotranslational assembly

“…How much does transcript-specific assembly buffer the dominant-negative effect, and what does it mean in the context of human genetic disease ( Bergendahl et al, 2019 )? This effect requires mutant subunits to be stable enough to assemble into complexes, and thus the impact of the mutations tends to be milder at the structural level than of other pathogenic mutations ( McEntagart et al, 2016 ), making them exceptionally difficult to detect using the existing variant effect predictors ( Gerasimavicius et al, 2021 ). Interestingly, cotranslational assembly should actually make the dominant-negative effect less common in homomers, because it can limit the mixing that occurs between wild-type and mutant subunits.…”