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Using Structural Analysis In Silico to Assess the Impact of Missense Variants in MEN1

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 stud...

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Detalles Bibliográficos
Autores principales: Caswell, Richard C, Owens, Martina M, Gunning, Adam C, Ellard, Sian, Wright, Caroline F
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Endocrine Society 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6846327/
https://www.ncbi.nlm.nih.gov/pubmed/31737856
http://dx.doi.org/10.1210/js.2019-00260
Descripción
Sumario: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.