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
SN - 1553-7390
VL - 13
JO - PLoS Genetics
JF - PLoS Genetics
IS - 4
M1 - e1006739
ER -
