Loss-of-function, gain-of-function and dominant-negative mutations have profoundly different effects on protein structure

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 investigate 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, even those based solely on sequence conservation, underperform on non-LOF mutations. However, we do show that non-LOF mutations could potentially be identified by their tendency to cluster in three-dimensional space. Overall, our work suggests that many pathogenic mutations that act via DN and GOF mechanisms 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.

Buffering of genetic dominance by allele-specific protein complex assembly

“…Moreover, pathogenic variants in predicted non-LOF genes show strong 3D clustering in their respective protein structures, consistent with previous observations (Gerasimavicius, Livesey, and Marsh 2022;Lelieveld et al 2017). In Figure S4F/G, we provide further support that, between thresholds T1 and T2, both ΔΔG and EDC exhibit the expected trend with an increasing effect size.…”

“…As an unbiased approach to assess the model, we analysed the ΔΔG of pathogenic mutations and their extent of disease clustering (EDC) after removing genes used for training, and AR genes, whose biallelic LOF mutations would bias the trend. In Figure 5C we show the result of this analysis at threshold T2, demonstrating that missense mutations in predicted non-LOF genes exhibit a milder impact on protein structure (Gerasimavicius, Livesey, and Marsh 2022; McEntagart et al 2016). Moreover, pathogenic variants in predicted non-LOF genes show strong 3D clustering in their respective protein structures, consistent with previous observations (Gerasimavicius, Livesey, and Marsh 2022; Lelieveld et al 2017).…”

“…In Figure 5C we show the result of this analysis at threshold T2, demonstrating that missense mutations in predicted non-LOF genes exhibit a milder impact on protein structure (Gerasimavicius, Livesey, and Marsh 2022; McEntagart et al 2016). Moreover, pathogenic variants in predicted non-LOF genes show strong 3D clustering in their respective protein structures, consistent with previous observations (Gerasimavicius, Livesey, and Marsh 2022; Lelieveld et al 2017). In Figure S4F/G , we provide further support that, between thresholds T1 and T2, both ΔΔG and EDC exhibit the expected trend with an increasing effect size.…”

“…There have been important studies aimed at understanding the properties of haploinsufficient genes and their prediction (Dang et al 2008; Huang et al 2010; Karczewski et al 2020; MacArthur et al 2012; Petrovski et al 2015; Shihab et al 2017; Steinberg et al 2015), but comparatively little effort has been channelled into exploring the characteristics of genes that give rise to dominant disorders in a manner not explained by simple LOF. Recently, we have shown that variants in genes associated with non-LOF mechanisms are predicted substantially poorer by state-of-the-art variant effect predictors (Gerasimavicius, Livesey, and Marsh 2022). This observation emphasises the importance of considering alternative molecular mechanisms in our collective attempt to annotate the human pathogenic variation.…”

Evolutionary dynamics of genome size and content during the adaptive radiation of Heliconiini butterflies

“…These analyses suggest that the duplicated genes might have gained the capacity to bind and process different substrates by changing their flexibility throughout the radiation of Heliconiini. This is consistent with the hypothesis that, in order to obtain a gain-of-function and to give rise to new interactions, a protein needs to change few sites in intrinsically disordered regions 40 . Our combined results present a more complex story than previously described, and both the high copy number variation and patterns of selection within Heliconiinae appear inconsistent with these genes playing a critical role in the evolution of pollen feeding.…”