Exploring the Complete Mutational Space of the LDL receptor LA5 Domain Using Molecular Dynamics: Linking SNPs with Disease Phenotypes in Familial Hypercholesterolemia
Title | Exploring the Complete Mutational Space of the LDL receptor LA5 Domain Using Molecular Dynamics: Linking SNPs with Disease Phenotypes in Familial Hypercholesterolemia |
Publication Type | Journal Article |
Year of Publication | 2016 |
Authors | Angarica, Vladimir Espinosa, Orozco Modesto, and Sancho Javier |
Journal | Human Molecular Genetics |
Abstract | Familial Hypercholesterolemia (FH), a genetic disorder with a prevalence of 0.2 %, represents a high risk factor to develop cardiovascular and cerebrovascular diseases. The majority and most severe FH cases are associated to mutations in the receptor for Low Density Lipoproteins (LDL-r), but the molecular basis explaining the connection between mutation and phenotype is often unknown, which hinders early diagnosis and treatment of the disease. We have used atomistic simulations to explore the complete SNP mutational space (227 mutants) of the LA5 repeat, the key domain for interacting with LDL that is coded in the exon concentrating the highest number of mutations. Four clusters of mutants of different stability have been identified. The majority of the 50 FH known mutations (33) appear distributed in the unstable clusters, i.e. loss of conformational stability explains 2/3 of FH phenotypes. However, 1/3 of FH phenotypes (17 mutations) do not destabilize the LR5 repeat. Combining our simulations with available structural data from different laboratories, we have defined a consensus binding site for the interaction of the LA5 repeat with LDL-r partner proteins and have found that most (16) of the 17 stable FH mutations occur at binding site residues. Thus, LA5-associated FH arises from mutations that cause either loss of stability or a decrease in domain’s binding affinity. Based on this finding we propose the likely phenotype of each possible SNP in the LA5 repeat and outline a procedure to make a full computational diagnosis for FH. |
URL | http://hmg.oxfordjournals.org/content/early/2016/01/10/hmg.ddw004.abstract |
DOI | 10.1093/hmg/ddw004 |