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Genetic variant classification by predicted protein structure: A case study on IRF6.

Hemma Murali1,2, Peng Wang2,3, Eric C Liao4,5

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Summary

Computational tools often misclassify genetic variants. Integrating predicted protein structures with mutation data can improve the accuracy of pathogenicity predictions for rare disease genes like IRF6.

Keywords:
Clinical geneticsStructural biologyVariant interpretation

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Area of Science:

  • Genomics
  • Bioinformatics
  • Structural Biology

Background:

  • Next-generation sequencing identifies many gene variants, but determining their pathogenicity is challenging.
  • Computational tools for variant pathogenicity prediction are often insufficient, necessitating functional validation.
  • AlphaFold2 has advanced protein structure prediction, offering new avenues for variant interpretation.

Purpose of the Study:

  • To develop and evaluate a strategy for enhancing genetic variant classification using predicted protein structures.
  • To assess the utility of structural information in predicting the pathogenicity of IRF6 gene variants.

Main Methods:

  • Compared over 30 computational pathogenicity prediction tools on 37 IRF6 missense variants.
  • Utilized predicted protein structures to analyze mutational clustering and its association with pathogenicity.
  • Validated computational predictions against experimental phenotype rescue data in zebrafish.

Main Results:

  • 19 of 37 IRF6 variants were unanimously predicted as deleterious by computational tools.
  • 12 variants predicted as pathogenic were experimentally determined as benign, highlighting prediction limitations.
  • Structural analysis revealed deleterious mutation clusters near the protein binding domain, contrasting with benign N-terminal variants.

Conclusions:

  • Predicted protein structures can improve gene-specific variant pathogenicity predictions.
  • Incorporating structural features of known mutations offers valuable insights for variant interpretation.
  • This approach aids in classifying the pathogenicity of genetic variants for rare diseases.