Predicting the impact of Lynch syndrome-causing missense mutations from structural calculations

Research output: Contribution to journalJournal articlepeer-review

Standard

Predicting the impact of Lynch syndrome-causing missense mutations from structural calculations. / Nielsen, Sofie V,; Stein, Amelie; Dinitzen, Alexander B.; Papaleo, Elena; Tatham, Michael H.; Poulsen, Esben Guldahl; Kassem, Maher Mahmoud; Rasmussen, Lene Juel; Lindorff-Larsen, Kresten; Hartmann-Petersen, Rasmus.

In: PLoS Genetics, Vol. 13, No. 4, e1006739, 19.04.2017.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Nielsen, SV, Stein, A, Dinitzen, AB, Papaleo, E, Tatham, MH, Poulsen, EG, Kassem, MM, Rasmussen, LJ, Lindorff-Larsen, K & Hartmann-Petersen, R 2017, 'Predicting the impact of Lynch syndrome-causing missense mutations from structural calculations', PLoS Genetics, vol. 13, no. 4, e1006739. https://doi.org/10.1371/journal.pgen.1006739

APA

Nielsen, S. V., Stein, A., Dinitzen, A. B., Papaleo, E., Tatham, M. H., Poulsen, E. G., Kassem, M. M., Rasmussen, L. J., Lindorff-Larsen, K., & Hartmann-Petersen, R. (2017). Predicting the impact of Lynch syndrome-causing missense mutations from structural calculations. PLoS Genetics, 13(4), [e1006739]. https://doi.org/10.1371/journal.pgen.1006739

Vancouver

Nielsen SV, Stein A, Dinitzen AB, Papaleo E, Tatham MH, Poulsen EG et al. Predicting the impact of Lynch syndrome-causing missense mutations from structural calculations. PLoS Genetics. 2017 Apr 19;13(4). e1006739. https://doi.org/10.1371/journal.pgen.1006739

Author

Nielsen, Sofie V, ; Stein, Amelie ; Dinitzen, Alexander B. ; Papaleo, Elena ; Tatham, Michael H. ; Poulsen, Esben Guldahl ; Kassem, Maher Mahmoud ; Rasmussen, Lene Juel ; Lindorff-Larsen, Kresten ; Hartmann-Petersen, Rasmus. / Predicting the impact of Lynch syndrome-causing missense mutations from structural calculations. In: PLoS Genetics. 2017 ; Vol. 13, No. 4.

Bibtex

@article{ed42aa8560154eaa84d43665c71a4a1a,
title = "Predicting the impact of Lynch syndrome-causing missense mutations from structural calculations",
abstract = "Accurate methods to assess the pathogenicity of mutations are needed to fully leverage the possibilities of genome sequencing in diagnosis. Current data-driven and bioinformatics approaches are, however, limited by the large number of new variations found in each newly sequenced genome, and often do not provide direct mechanistic insight. Here we demonstrate, for the first time, that saturation mutagenesis, biophysical modeling and co-variation analysis, performed in silico, can predict the abundance, metabolic stability, and function of proteins inside living cells. As a model system, we selected the human mismatch repair protein, MSH2, where missense variants are known to cause the hereditary cancer predisposition disease, known as Lynch syndrome. We show that the majority of disease-causing MSH2 mutations give rise to folding defects and proteasome-dependent degradation rather than inherent loss of function, and accordingly our in silico modeling data accurately identifies disease-causing mutations and outperforms the traditionally used genetic disease predictors. Thus, in conclusion, in silico biophysical modeling should be considered for making genotype-phenotype predictions and for diagnosis of Lynch syndrome, and perhaps other hereditary diseases.",
keywords = "Journal Article",
author = "Nielsen, {Sofie V,} and Amelie Stein and Dinitzen, {Alexander B.} and Elena Papaleo and Tatham, {Michael H.} and Poulsen, {Esben Guldahl} and Kassem, {Maher Mahmoud} and Rasmussen, {Lene Juel} and Kresten Lindorff-Larsen and Rasmus Hartmann-Petersen",
year = "2017",
month = apr,
day = "19",
doi = "10.1371/journal.pgen.1006739",
language = "English",
volume = "13",
journal = "P L o S Genetics",
issn = "1553-7390",
publisher = "Public Library of Science",
number = "4",

}

RIS

TY - JOUR

T1 - Predicting the impact of Lynch syndrome-causing missense mutations from structural calculations

AU - Nielsen, Sofie V,

AU - Stein, Amelie

AU - Dinitzen, Alexander B.

AU - Papaleo, Elena

AU - Tatham, Michael H.

AU - Poulsen, Esben Guldahl

AU - Kassem, Maher Mahmoud

AU - Rasmussen, Lene Juel

AU - Lindorff-Larsen, Kresten

AU - Hartmann-Petersen, Rasmus

PY - 2017/4/19

Y1 - 2017/4/19

N2 - Accurate methods to assess the pathogenicity of mutations are needed to fully leverage the possibilities of genome sequencing in diagnosis. Current data-driven and bioinformatics approaches are, however, limited by the large number of new variations found in each newly sequenced genome, and often do not provide direct mechanistic insight. Here we demonstrate, for the first time, that saturation mutagenesis, biophysical modeling and co-variation analysis, performed in silico, can predict the abundance, metabolic stability, and function of proteins inside living cells. As a model system, we selected the human mismatch repair protein, MSH2, where missense variants are known to cause the hereditary cancer predisposition disease, known as Lynch syndrome. We show that the majority of disease-causing MSH2 mutations give rise to folding defects and proteasome-dependent degradation rather than inherent loss of function, and accordingly our in silico modeling data accurately identifies disease-causing mutations and outperforms the traditionally used genetic disease predictors. Thus, in conclusion, in silico biophysical modeling should be considered for making genotype-phenotype predictions and for diagnosis of Lynch syndrome, and perhaps other hereditary diseases.

AB - Accurate methods to assess the pathogenicity of mutations are needed to fully leverage the possibilities of genome sequencing in diagnosis. Current data-driven and bioinformatics approaches are, however, limited by the large number of new variations found in each newly sequenced genome, and often do not provide direct mechanistic insight. Here we demonstrate, for the first time, that saturation mutagenesis, biophysical modeling and co-variation analysis, performed in silico, can predict the abundance, metabolic stability, and function of proteins inside living cells. As a model system, we selected the human mismatch repair protein, MSH2, where missense variants are known to cause the hereditary cancer predisposition disease, known as Lynch syndrome. We show that the majority of disease-causing MSH2 mutations give rise to folding defects and proteasome-dependent degradation rather than inherent loss of function, and accordingly our in silico modeling data accurately identifies disease-causing mutations and outperforms the traditionally used genetic disease predictors. Thus, in conclusion, in silico biophysical modeling should be considered for making genotype-phenotype predictions and for diagnosis of Lynch syndrome, and perhaps other hereditary diseases.

KW - Journal Article

U2 - 10.1371/journal.pgen.1006739

DO - 10.1371/journal.pgen.1006739

M3 - Journal article

C2 - 28422960

VL - 13

JO - P L o S Genetics

JF - P L o S Genetics

SN - 1553-7390

IS - 4

M1 - e1006739

ER -

ID: 178481727