Abstract
Despite the rapid expansion in recent years of databases reporting either benign or pathogenic genetic variations, the interpretation of novel missense variants remains challenging, particularly for clinical or genetic testing laboratories where functional analysis is often unfeasible. Previous studies have shown that thermodynamic analysis of protein structure in silico can discriminate between groups of benign and pathogenic missense variants. However, although structures exist for many human disease‒associated proteins, such analysis remains largely unexploited in clinical laboratories. Here, we analyzed the predicted effect of 338 known missense variants on the structure of menin, the MEN1 gene product. Results provided strong discrimination between pathogenic and benign variants, with a threshold of >4 kcal/mol for the predicted change in stability, providing a strong indicator of pathogenicity. Subsequent analysis of seven novel missense variants identified during clinical testing of patients with MEN1 showed that all seven were predicted to destabilize menin by >4 kcal/mol. We conclude that structural analysis provides a useful tool in understanding the effect of missense variants in MEN1 and that integration of proteomic with genomic data could potentially contribute to the classification of novel variants in this disease.
Highlights
The potential utility of protein stability data in the analysis of missense variants was recently demonstrated in studies of the Lynch syndrome protein MSH2 [7] and in phenylalanine hydroxylase (PAH) [8], in which pathogenic variants result in phenylketonuria
Both of these studies combined in silico analysis with extensive functional analysis of a number of MSH2 and PAH variants; resources for the latter are unlikely to be routinely available in clinical genetics laboratories
We report here that thermodynamic analysis of multiple endocrine neoplasia type 1 (MEN1) variants in silico provided a very strong positive predictive value (PPV) for pathogenicity, thereby helping to assess the effect of novel missense variants on protein function and potentially allowing the use of such analysis in variant classification
Summary
As has been comprehensively reviewed elsewhere [1], these methods rely on analysis of DNA and protein conservation, protein structure-based analysis, or a combination of the two In the latter, widely used tools such as PolyPhen may incorporate information on the nature of the amino acid change itself Loss of menin activity could lead to neoplasia and tumor formation via a number of potential pathways
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