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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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Figures

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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Cites

Research works printed but not linked: 77

  1. Stenson, P. D. et al. The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies. Hum. Genet. 136, 665–677 (2017). · DOI
  2. Katsonis, P. et al. Single nucleotide variations: Biological impact and theoretical interpretation. Protein Sci. 23, 1650–1666 (2014). · DOI
  3. Landrum, M. J. et al. ClinVar: Public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 42, 980–985 (2014). · DOI · Paper page
  4. Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020). · DOI · Paper page
  5. Gudmundsson, S. et al. Variant interpretation using population databases: Lessons from gnomAD. Hum. Mutat. humu.24309 https://doi.org/10.1002/humu.24309 (2021). · DOI
  6. Miosge, L. A. et al. Comparison of predicted and actual consequences of missense mutations. Proc. Natl. Acad. Sci. USA 112, E5189–E5198 (2015). · DOI
  7. Livesey, B. J. & Marsh, J. A. Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations. Mol. Syst. Biol. 16, 1–12 (2020). · DOI
  8. Gray, V. E., Hause, R. J., Luebeck, J., Shendure, J. & Fowler, D. M. Quantitative missense variant effect prediction using large-scale mutagenesis data. Cell Syst. 6, 116–124.e3 (2018). · DOI
  9. Sanavia, T. et al. Limitations and challenges in protein stability prediction upon genome variations: towards future applications in precision medicine. Comput. Struct. Biotechnol. J. 18, 1968–1979 (2020). · DOI
  10. Heyn, P. et al. Gain-of-function DNMT3A mutations cause microcephalic dwarfism and hypermethylation of Polycomb-regulated regions. Nat. Genet. 51, 96–105 (2019). · DOI
  11. Holt, R. J. et al. De novo missense variants in FBXW11 cause diverse developmental phenotypes including brain, eye, and digit anomalies. Am. Am. J. Hum. Genet. 105, 640–657 (2019). · DOI
  12. Williamson, K. A. et al. Recurrent heterozygous PAX6 missense variants cause severe bilateral microphthalmia via predictable effects on DNA–protein interaction. Genet. Med. 22, 598–609 (2020). · DOI
  13. McEntagart, M. et al. A Restricted Repertoire of de Novo Mutations in ITPR1 Cause Gillespie Syndrome with Evidence for Dominant-Negative Effect. Am. J. Hum. Genet. 98, 981–992 (2016). · DOI
  14. Yue, P., Li, Z. & Moult, J. Loss of protein structure stability as a major causative factor in monogenic disease. J. Mol. Biol. 353, 459–473 (2005). · DOI
  15. Glusman, G. et al. Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: A proposed framework. Genome Med. 9, 1–10 (2017). · DOI
  16. Bhattacharya, R., Rose, P. W., Burley, S. K. & Prlić, A. Impact of genetic variation on three dimensional structure and function of proteins. PLoS ONE 12, 1–22 (2017). · DOI
  17. Gerasimavicius, L., Liu, X. & Marsh, J. A. Identification of pathogenic missense mutations using protein stability predictors. Sci. Rep. 10, 1–10 (2020). · DOI
  18. Casadio, R., Vassura, M., Tiwari, S., Fariselli, P. & Luigi Martelli, P. Correlating disease-related mutations to their effect on protein stability: A large-scale analysis of the human proteome. Hum. Mutat. 32, 1161–1170 (2011). · DOI
  19. Birolo, G. et al. Protein Stability Perturbation Contributes to the Loss of Function in Haploinsufficient Genes. Front. Mol. Biosci. 8, 620793 (2021). · DOI
  20. Herskowitz, I. dominant negative mutations CS-G-CS. Nature 329, 1–4 (1987). · DOI
  21. Bergendahl, L. T. et al. The role of protein complexes in human genetic disease. Protein Sci. 28, 1400–1411 (2019). · DOI
  22. Veitia, R. A., Caburet, S. & Birchler, J. A. Mechanisms of Mendelian dominance. Clin. Genet. 93, 419–428 (2018). · DOI
  23. Delgado, J., Radusky, L. G., Cianferoni, D. & Serrano, L. FoldX 5.0: Working with RNA, small molecules and a new graphical interface. Bioinformatics 35, 4168–4169 (2019). · DOI
  24. Hanley, J. A. & McNeil, B. J. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143, 29–36 (1982). · DOI · Paper page
  25. Jubb, H. C. et al. Mutations at protein-protein interfaces: Small changes over big surfaces have large impacts on human health. Prog. Biophys. Mol. Biol. 128, 3–13 (2017). · DOI
  26. Amberger, J. S., Bocchini, C. A., Scott, A. F. & Hamosh, A. OMIM.org: Leveraging knowledge across phenotype-gene relationships. Nucleic Acids Res. 47, D1038–D1043 (2019). · DOI
  27. Tokuriki, N., Stricher, F., Schymkowitz, J., Serrano, L. & Tawfik, D. S. The stability effects of protein mutations appear to be universally distributed. J. Mol. Biol. 369, 1318–1332 (2007). · DOI
  28. Ferrer-Costa, C., Orozco, M. & de la Cruz, X. Characterization of disease-associated single amino acid polymorphisms in terms of sequence and structure properties. J. Mol. Biol. 315, 771–786 (2002). · DOI
  29. Livesey, B. J. & Marsh, J. A. The properties of human disease mutations at protein interfaces. PLOS Comput. Biol. 18, e1009858 (2022). · DOI
  30. Seifi, M. & Walter, M. A. Accurate prediction of functional, structural, and stability changes in PITX2 mutations using in silico bioinformatics algorithms. PLoS ONE 13, 1–23 (2018). · DOI
  31. Potapov, V., Cohen, M. & Schreiber, G. Assessing computational methods for predicting protein stability upon mutation: Good on average but not in the details. Protein Eng. Des. Sel. 22, 553–560 (2009). · DOI · Paper page
  32. Bromberg, Y. & Rost, B. Correlating protein function and stability through the analysis of single amino acid substitutions. BMC Bioinforma. 10, 1–9 (2009). · DOI
  33. Iqbal, S. et al. Comprehensive characterization of amino acid positions in protein structures reveals molecular effect of missense variants. Proc. Natl. Acad. Sci. 117, 28201–28211 (2020). · DOI
  34. Mi, H. et al. PANTHER version 16: A revised family classification, tree-based classification tool, enhancer regions and extensive API. Nucleic Acids Res. 49, D394–D403 (2021). · DOI · Paper page
  35. Backwell, L. & Marsh, J. A. Diverse molecular mechanisms underlying pathogenic protein mutations: Beyond the loss-of-function paradigm. Annu. Rev. Genomics Hum. Genet. 23, annurev-genom-111221-103208 (2022). · DOI
  36. Sevim Bayrak, C. et al. Identification of discriminative gene-level and protein-level features associated with pathogenic gain-of-function and loss-of-function variants. Am. J. Hum. Genet. 108, 2301–2318 (2021). · DOI
  37. Stenson, P. D. et al. Human Gene Mutation Database (HGMD®): 2003 update. Hum. Mutat. 21, 577–581 (2003). · DOI
  38. Li, X. H. & Babu, M. M. Human Diseases from Gain-of-Function Mutations in Disordered Protein Regions. Cell 175, 40–42 (2018). · DOI
  39. Witham, S., Takano, K., Schwartz, C. & Alexov, E. A missense mutation in CLIC2 associated with intellectual disability is predicted by in silico modeling to affect protein stability and dynamics. Proteins Struct. Funct. Bioinforma. 79, 2444–2454 (2011). · DOI
  40. Stefl, S., Nishi, H., Petukh, M., Panchenko, A. R. & Alexov, E. Molecular mechanisms of disease-causing missense mutations. J. Mol. Biol. 425, 3919–3936 (2013). · DOI
  41. Nishi, H. et al. Cancer Missense Mutations Alter Binding Properties of Proteins and Their Interaction Networks. PLoS One 8, e66273 (2013). · DOI
  42. Stein, A., Fowler, D. M., Hartmann-Petersen, R. & Lindorff-Larsen, K. Biophysical and Mechanistic Models for Disease-Causing Protein Variants. Trends Biochem. Sci. 44, 575–588 (2019). · DOI
  43. Cleary, M. A., Pendergrast, P. S. & Herr, W. Structural flexibility in transcription complex formation revealed by protein-DNA photocrosslinking. Proc. Natl Acad. Sci. 94, 8450–8455 (1997). · DOI
  44. Pan, Y., Tsai, C.-J., Ma, B. & Nussinov, R. Mechanisms of transcription factor selectivity. Trends Genet. 26, 75–83 (2010). · DOI
  45. Grimm, D. G. et al. The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity. Hum. Mutat. 36, 513–523 (2015). · DOI · Paper page
  46. Ng, P. C. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res. 31, 3812–3814 (2003). · DOI
  47. Riesselman, A. J., Ingraham, J. B. & Marks, D. S. Deep generative models of genetic variation capture the effects of mutations. Nat. Methods 15, 816–822 (2018). · DOI · Paper page
  48. Chen, J. W., Romero, P., Uversky, V. N. & Dunker, A. K. Conservation of intrinsic disorder in protein domains and families: I. A Database of Conserved Predicted Disordered Regions. J. Proteome Res. 5, 879–887 (2006). · DOI
  49. Lelieveld, S. H. et al. Spatial clustering of de novo missense mutations identifies candidate neurodevelopmental disorder-associated genes. Am. J. Hum. Genet. 101, 478–484 (2017). · DOI
  50. Sivley, R. M., Dou, X., Meiler, J., Bush, W. S. & Capra, J. A. Comprehensive analysis of constraint on the spatial distribution of missense variants in human protein structures. Am. J. Hum. Genet. 102, 415–426 (2018). · DOI
  51. Turner, T. N. et al. Proteins linked to autosomal dominant and autosomal recessive disorders harbor characteristic rare missense mutation distribution patterns. Hum. Mol. Genet. 24, 5995–6002 (2015). · DOI
  52. Stehr, H. et al. The structural impact of cancer-associated missense mutations in oncogenes and tumor suppressors. Mol. Cancer 10, 54 (2011). · DOI
  53. Mastrogiorgio, G. et al. Clinical and molecular characterization of patients with adenylosuccinate lyase deficiency. Orphanet J. Rare Dis. 16, 112 (2021). · DOI
  54. Hijikata, A., Tsuji, T., Shionyu, M. & Shirai, T. Decoding disease-causing mechanisms of missense mutations from supramolecular structures. Sci. Rep. 7, 1–8 (2017). · DOI
  55. Veitia, R. A. Exploring the molecular etiology of dominant-negative mutations. Plant Cell Online 19, 3843–3851 (2007). · DOI
  56. Chen, C.-H. et al. Novel and prevalent non-East Asian ALDH2 variants; Implications for global susceptibility to aldehydes’ toxicity. EBioMedicine 55, 102753 (2020). · DOI
  57. Prelich, G. Gene overexpression: Uses, mechanisms, and interpretation. Genetics 190, 841–854 (2012). · DOI
  58. De Baets, G., Van Doorn, L., Rousseau, F. & Schymkowitz, J. Increased Aggregation Is More Frequently Associated to Human Disease-Associated Mutations Than to Neutral Polymorphisms. PLOS Comput. Biol. 11, e1004374 (2015). · DOI
  59. Flanagan, S. E., Patch, A.-M. & Ellard, S. Using SIFT and Polyphen to predict loss-of-function and gain-of-function mutations. Genet. Test. Mol. Biomark. 14, 533–537 (2010). · DOI
  60. Rodrigues, C. H. M., Pires, D. E. V. & Ascher, D. B. DynaMut2: Assessing changes in stability and flexibility upon single and multiple point missense mutations. Protein Sci. 30, 60–69 (2021). · DOI
  61. Findlay, G. M. et al. Accurate classification of BRCA1 variants with saturation genome editing. Nature 562, 217–222 (2018). · DOI · Paper page
  62. Majithia, A. R. et al. Prospective functional classification of all possible missense variants in PPARG. Nat. Genet. 48, 1570–1575 (2016). · DOI · Paper page
  63. Nielsen, S. V., Hartmann-Petersen, R., Stein, A. & Lindorff-Larsen, K. Multiplexed assays reveal effects of missense variants in MSH2 and cancer predisposition. PLOS Genet 17, e1009496 (2021). · DOI
  64. Cagiada, M. et al. Understanding the origins of loss of protein function by analyzing the effects of thousands of variants on activity and abundance. Mol. Biol. Evol. 38, 3235–3246 (2021). · DOI
  65. Marneros, A. G. et al. Mutations in KCTD1 Cause Scalp-Ear-Nipple Syndrome. Am. J. Hum. Genet. 92, 621–626 (2013). · DOI
  66. Kumar, S. et al. Standardized, systemic phenotypic analysis reveals kidney dysfunction as main alteration of Kctd1 I27N mutant mice. J. Biomed. Sci. 24, 57 (2017). · DOI
  67. Hu, L. et al. KCTD1 mutants in scalp‑ear‑nipple syndrome and AP‑2α P59A in Char syndrome reciprocally abrogate their interactions, but can regulate Wnt/β‑catenin signaling. Mol. Med. Rep. https://doi.org/10.3892/mmr.2020.11457. (2020). · DOI
  68. Levy, E. D. A simple definition of structural regions in proteins and its use in analyzing interface evolution. J. Mol. Biol. 403, 660–670 (2010). · DOI · Paper page
  69. The UniProt Consortium. et al. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 49, D480–D489 (2021). · DOI · Paper page
  70. Nightingale, A. et al. The Proteins API: Accessing key integrated protein and genome information. Nucleic Acids Res. 45, W539–W544 (2017). · DOI
  71. Liu, X., Li, C., Mou, C., Dong, Y. & Tu, Y. dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs. Genome Med. 12, 103 (2020). · DOI
  72. Patil, I. Visualizations with statistical details: The ‘ggstatsplot’ approach. J. Open Source Softw. 6, 3167 (2021). · DOI
  73. Dunn, O. J. Multiple comparisons using rank sums. Technometrics 6, 241–252 (1964). · DOI
  74. Dinno, A. Nonparametric pairwise multiple comparisons in independent groups using Dunn’s Test. Stata J. Promot. Commun. Stat. Stata. 15, 292–300 (2015). · DOI
  75. Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979).
  76. Giacalone, M., Agata, Z., Cozzucoli, P. C. & Alibrandi, A. Bonferroni-Holm and permutation tests to compare health data: methodological and applicative issues. BMC Med. Res. Methodol. 18, 81 (2018). · DOI
  77. Greiner, M., Pfeiffer, D. & Smith, R. D. Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Prev. Vet. Med. 45, 23–41 (2000). · DOI · Paper page

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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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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.