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

Gerasimavicius, Lukas, Livesey, Benjamin J., Marsh, Joseph A.·2022·Nature Communications
DOI 10.1038/s41467-022-31686-6PMC PMC9259657
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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.

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Fig. 1 Using full protein complex structures improves discrimination between pathogenic and putatively benign missense variants. a Comparison of predicted | ΔΔG | values for putatively benign gnomAD and pathogenic ClinVar mutations when calculated using monomer structures only, or by also employing full biological assemblies and therefore also considering intermolecular interactions. Boxes denote data within 25th and 75th percentiles, and contain median (middle line) and mean (red dot) value notations. Whiskers extend from the box to furthest values within 1.5x the inter-quartile range. All pairwise group comparisons (both between and within prediction types) are significant (p < 2.5 × 10−28, two-sided Dunn’s test for multiple pairwise comparisons, corrected for multiple testing by Holm’s method; see ‘Methods’). b Receiver operating characteristic (ROC) curves and area under the curve (AUC) for discrimination between ClinVar and gnomAD mutations using monomer and full | ΔΔG | values. Source data are provided as a Source Data file.
Fig. 1 Using full protein complex structures improves discrimination between pathogenic and putatively benign missense variants. a Comparison of predicted | ΔΔG | values for putatively benign gnomAD and pathogenic ClinVar mutations when calculated using monomer structures only, or by also employing full biological assemblies and therefore also considering intermolecular interactions. Boxes denote data within 25th and 75th percentiles, and contain median (middle line) and mean (red dot) value notations. Whiskers extend from the box to furthest values within 1.5x the inter-quartile range. All pairwise group comparisons (both between and within prediction types) are significant (p < 2.5 × 10−28, two-sided Dunn’s test for multiple pairwise comparisons, corrected for multiple testing by Holm’s method; see ‘Methods’). b Receiver operating characteristic (ROC) curves and area under the curve (AUC) for discrimination between ClinVar and gnomAD mutations using monomer and full | ΔΔG | values. Source data are provided as a Source Data file.CC BY · PubMed Central
Fig. 2 Autosomal dominant mutations are milder than autosomal recessive mutations. a Comparison of predicted | ΔΔG | values for gnomAD variants and pathogenic ClinVar mutations from autosomal dominant (AD) and autosomal recessive (AR) genes. Boxes denote data within 25th and 75th percentiles, and contain median (middle line) and mean (red dot) value notations. Whiskers extend from the box to furthest values within 1.5x the inter-quartile range. All pairwise comparisons between groups are significant (p < 6.4 × 10−72, two-sided Holm-corrected Dunn’s test). b Proportions of gnomAD, AD and AR mutations occurring in protein interiors, surfaces and interfaces. All pairwise comparisons are significant (Chi-square test). c AUC values calculated from ROC curves for discriminating between pathogenic ClinVar mutations from AD or AR genes, and putatively benign gnomAD variants. ‘AR, All’ considers all gnomAD variants from the AR genes, while ‘AR, Hom’ only includes those variants that have been observed in a homozygous state in gnomAD at least once. Error bars denote 95% confidence intervals. Source data are provided as a Source Data file.
Fig. 2 Autosomal dominant mutations are milder than autosomal recessive mutations. a Comparison of predicted | ΔΔG | values for gnomAD variants and pathogenic ClinVar mutations from autosomal dominant (AD) and autosomal recessive (AR) genes. Boxes denote data within 25th and 75th percentiles, and contain median (middle line) and mean (red dot) value notations. Whiskers extend from the box to furthest values within 1.5x the inter-quartile range. All pairwise comparisons between groups are significant (p < 6.4 × 10−72, two-sided Holm-corrected Dunn’s test). b Proportions of gnomAD, AD and AR mutations occurring in protein interiors, surfaces and interfaces. All pairwise comparisons are significant (Chi-square test). c AUC values calculated from ROC curves for discriminating between pathogenic ClinVar mutations from AD or AR genes, and putatively benign gnomAD variants. ‘AR, All’ considers all gnomAD variants from the AR genes, while ‘AR, Hom’ only includes those variants that have been observed in a homozygous state in gnomAD at least once. Error bars denote 95% confidence intervals. Source data are provided as a Source Data file.CC BY · PubMed Central
Fig. 3 Gain-of-function and dominant-negative mutations are milder than loss-of-function mutations. a Comparison of predicted | ΔΔG | values for gnomAD variants and pathogenic ClinVar mutations from AD genes classified as dominant negative (DN), gain of function (GOF) and haploinsufficient (HI), and pathogenic ClinVar mutations from AR genes. Boxes denote data within 25th and 75th percentiles, and contain median (middle line) and mean (red dot) value notations. Whiskers extend from the box to furthest values within 1.5x the inter-quartile range. Pairwise group comparisons are significant (p < 0.001, two-sided Holm-corrected Dunn’s test) unless specified (n.s., p-values in order from left to right: 0.786, 0.321, 0.075). b Proportions of different types of mutations occurring in protein interiors, surfaces and interfaces. All pairwise proportion comparisons via Chi-square test are significant, with the lowest effect size being noted for HI vs AR (Cramer’s V = 0.04, compared to 0.20 and 0.26 for DN vs. HI, and DN vs. AR, respectively). c AUC values calculated from ROC curves for discriminating between different types of pathogenic ClinVar mutations, and putatively benign gnomAD variants. Only homozygous gnomAD variants were included for the AR analysis. Error bars denote 95% confidence intervals. Source data are provided as a Source Data file.
Fig. 3 Gain-of-function and dominant-negative mutations are milder than loss-of-function mutations. a Comparison of predicted | ΔΔG | values for gnomAD variants and pathogenic ClinVar mutations from AD genes classified as dominant negative (DN), gain of function (GOF) and haploinsufficient (HI), and pathogenic ClinVar mutations from AR genes. Boxes denote data within 25th and 75th percentiles, and contain median (middle line) and mean (red dot) value notations. Whiskers extend from the box to furthest values within 1.5x the inter-quartile range. Pairwise group comparisons are significant (p < 0.001, two-sided Holm-corrected Dunn’s test) unless specified (n.s., p-values in order from left to right: 0.786, 0.321, 0.075). b Proportions of different types of mutations occurring in protein interiors, surfaces and interfaces. All pairwise proportion comparisons via Chi-square test are significant, with the lowest effect size being noted for HI vs AR (Cramer’s V = 0.04, compared to 0.20 and 0.26 for DN vs. HI, and DN vs. AR, respectively). c AUC values calculated from ROC curves for discriminating between different types of pathogenic ClinVar mutations, and putatively benign gnomAD variants. Only homozygous gnomAD variants were included for the AR analysis. Error bars denote 95% confidence intervals. Source data are provided as a Source Data file.CC BY · PubMed Central
Fig. 4 Stabilising mutations may be important for disease in gain-of-function and particular functional class LOF contexts. a ΔΔG distributions (not absolute values) for pathogenic mutations, associated with different molecular mechanisms. Violin middle line shows median, red dot represents the mean. Pairwise group comparisons are significant (p < 3.29 × 10−4, two-sided Holm-corrected Dunn’s test) unless specified (n.s., p = 0.107). b Fraction of pathogenic mutations associated with different molecular mechanisms with negative (stabilising) ΔΔG values. Error bars denote 95% confidence intervals. c Fisher log odds enrichments of stabilising mutations within each class from among all disease mutations. Error bars represent 95% confidence intervals. d Destabilising and stabilising variant prevalence differences in HI genes associated with particular functional classes. Sample sizes denote variant number, found in a gene associated with the particular function. Chi-square test p-value and effect size are shown. Source data are provided as a Source Data file.
Fig. 4 Stabilising mutations may be important for disease in gain-of-function and particular functional class LOF contexts. a ΔΔG distributions (not absolute values) for pathogenic mutations, associated with different molecular mechanisms. Violin middle line shows median, red dot represents the mean. Pairwise group comparisons are significant (p < 3.29 × 10−4, two-sided Holm-corrected Dunn’s test) unless specified (n.s., p = 0.107). b Fraction of pathogenic mutations associated with different molecular mechanisms with negative (stabilising) ΔΔG values. Error bars denote 95% confidence intervals. c Fisher log odds enrichments of stabilising mutations within each class from among all disease mutations. Error bars represent 95% confidence intervals. d Destabilising and stabilising variant prevalence differences in HI genes associated with particular functional classes. Sample sizes denote variant number, found in a gene associated with the particular function. Chi-square test p-value and effect size are shown. Source data are provided as a Source Data file.CC BY · PubMed Central
Fig. 5 Dominant-negative and gain-of-function mutations are less well identified by nearly all computational variant effect predictors than loss-of-function mutations. AUC values calculated from ROC curves for discriminating between different types of pathogenic ClinVar mutations and putatively benign gnomAD variants, using the outputs of different computational variant effect predictors. Only homozygous gnomAD variants were included for the AR analysis. Error bars denote 95% confidence intervals. Source data are provided as a Source Data file.
Fig. 5 Dominant-negative and gain-of-function mutations are less well identified by nearly all computational variant effect predictors than loss-of-function mutations. AUC values calculated from ROC curves for discriminating between different types of pathogenic ClinVar mutations and putatively benign gnomAD variants, using the outputs of different computational variant effect predictors. Only homozygous gnomAD variants were included for the AR analysis. Error bars denote 95% confidence intervals. Source data are provided as a Source Data file.CC BY · PubMed Central
Fig. 6 Pathogenic mutations in genes associated with dominant-negative and gain-of-function mechanisms are significantly more clustered in space than those associated with loss-of-function mechanisms. Sample sizes denote protein number. Boxes denote data within 25th and 75th percentiles, and contain median (middle line) and mean (red dot) value notations. Whiskers extend from the box to furthest values within 1.5x the inter-quartile range. a Distribution of Extent of Disease Clustering (EDC) values for autosomal recessive (AR) compared to autosomal dominant (AD) disease genes. EDC values calculated with gnomAD variants from the same set of genes are shown for comparison. p-values were calculated with the two-sided Holm-corrected Dunn’s test. EDC values were only calculated for protein structures with pathogenic/gnomAD variants at least 10 different residues, which means that the number of proteins in the gnomAD set is slightly smaller than the sum of the AD and AR groups. b Distribution of EDC values for AD disease genes associated with gain-of-function (GOF), dominant-negative (DN) and haploinsufficient (HI) mechanisms. c Demonstration of the variant distribution differences between non-LOF (KCTD1) and LOF (ADSL) disease proteins, and their strikingly different EDC values. Source data are provided as a Source Data file.
Fig. 6 Pathogenic mutations in genes associated with dominant-negative and gain-of-function mechanisms are significantly more clustered in space than those associated with loss-of-function mechanisms. Sample sizes denote protein number. Boxes denote data within 25th and 75th percentiles, and contain median (middle line) and mean (red dot) value notations. Whiskers extend from the box to furthest values within 1.5x the inter-quartile range. a Distribution of Extent of Disease Clustering (EDC) values for autosomal recessive (AR) compared to autosomal dominant (AD) disease genes. EDC values calculated with gnomAD variants from the same set of genes are shown for comparison. p-values were calculated with the two-sided Holm-corrected Dunn’s test. EDC values were only calculated for protein structures with pathogenic/gnomAD variants at least 10 different residues, which means that the number of proteins in the gnomAD set is slightly smaller than the sum of the AD and AR groups. b Distribution of EDC values for AD disease genes associated with gain-of-function (GOF), dominant-negative (DN) and haploinsufficient (HI) mechanisms. c Demonstration of the variant distribution differences between non-LOF (KCTD1) and LOF (ADSL) disease proteins, and their strikingly different EDC values. Source data are provided as a Source Data file.CC BY · PubMed Central

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Metadata

Authors
Gerasimavicius, L., Livesey, B. J., & Marsh, J. A.
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2022-07-06
Volume
13
Issue
1
ISSN
2041-1723
DOI
10.1038/s41467-022-31686-6
PMC
PMC9259657
Cited by
338
References
8
Language
en
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Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Loss-of-function, gain-of-function and dominant-negative mutations have profoundly different effects on protein structure. https://app.cochat.ai/papers/10.1038/s41467-022-31686-6
The paper
Gerasimavicius, L., Livesey, B. J., & Marsh, J. A. (2022). Loss-of-function, gain-of-function and dominant-negative mutations have profoundly different effects on protein structure. Nature Communications. https://doi.org/10.1038/s41467-022-31686-6

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.