Cargando…
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...
Autores principales: | , , , , |
---|---|
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 |
_version_ | 1783468859623211008 |
---|---|
author | Caswell, Richard C Owens, Martina M Gunning, Adam C Ellard, Sian Wright, Caroline F |
author_facet | Caswell, Richard C Owens, Martina M Gunning, Adam C Ellard, Sian Wright, Caroline F |
author_sort | Caswell, Richard C |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6846327 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Endocrine Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-68463272019-11-15 Using Structural Analysis In Silico to Assess the Impact of Missense Variants in MEN1 Caswell, Richard C Owens, Martina M Gunning, Adam C Ellard, Sian Wright, Caroline F J Endocr Soc Research Articles 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. Endocrine Society 2019-09-27 /pmc/articles/PMC6846327/ /pubmed/31737856 http://dx.doi.org/10.1210/js.2019-00260 Text en Copyright © 2019 Endocrine Society https://creativecommons.org/licenses/by/4.0/ This article has been published under the terms of the Creative Commons Attribution License (CC BY; https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Copyright for this article is retained by the author(s). |
spellingShingle | Research Articles Caswell, Richard C Owens, Martina M Gunning, Adam C Ellard, Sian Wright, Caroline F Using Structural Analysis In Silico to Assess the Impact of Missense Variants in MEN1 |
title | Using Structural Analysis In Silico to Assess the Impact of Missense Variants in MEN1 |
title_full | Using Structural Analysis In Silico to Assess the Impact of Missense Variants in MEN1 |
title_fullStr | Using Structural Analysis In Silico to Assess the Impact of Missense Variants in MEN1 |
title_full_unstemmed | Using Structural Analysis In Silico to Assess the Impact of Missense Variants in MEN1 |
title_short | Using Structural Analysis In Silico to Assess the Impact of Missense Variants in MEN1 |
title_sort | using structural analysis in silico to assess the impact of missense variants in men1 |
topic | Research Articles |
url | 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 |
work_keys_str_mv | AT caswellrichardc usingstructuralanalysisinsilicotoassesstheimpactofmissensevariantsinmen1 AT owensmartinam usingstructuralanalysisinsilicotoassesstheimpactofmissensevariantsinmen1 AT gunningadamc usingstructuralanalysisinsilicotoassesstheimpactofmissensevariantsinmen1 AT ellardsian usingstructuralanalysisinsilicotoassesstheimpactofmissensevariantsinmen1 AT wrightcarolinef usingstructuralanalysisinsilicotoassesstheimpactofmissensevariantsinmen1 |