Molecular insights into the coding region mutations of low-density lipoprotein receptor adaptor protein 1 (LDLRAP1) linked to familial hypercholesterolemia

Abstract

Background: Familial hypercholesterolemia (FH) is a lipid disorder caused by pathogenic mutations in LDLRAP1 gene. The present study has aimed to deepen our understanding about the pathogenicity predictions of FH causative genetic mutations, as well as their relationship to phenotype changes in LDLRAP1 protein, by utilizing multidirectional computational analysis.

Methods: FH linked LDLRAP1 mutations were mined from databases, and the prediction ability of several pathogenicity classifiers against these clinical variants, was assessed through different statistical measures. Furthermore, these mutations were 3D modelled in protein structures to assess their impact on protein phenotype changes.

Results: Our findings suggest that Polyphen-2, when compared with SIFT, M-CAP and CADD tools, can make better pathogenicity predictions for FH causative LDLRAP1 mutations. Through, 3D simulation and superimposition analysis of LDLRAP1 protein structures, it was found that missense mutations do not create any gross changes in the protein structure, although they could induce subtle structural changes at the level of amino acid residues. Near native molecular dynamic analysis revealed that missense mutations could induce variable degrees of fluctuation differences guiding the protein flexibility. Stability analysis showed that most missense mutations shifts the free energy equilibrium and hence they destabilize the protein. Molecular docking analysis demonstrates the molecular shifts in hydrogen and ionic bonds and Van der waals bonding properties, which further cause differences in the binding energy of LDLR-LDLRAP1 proteins.

Conclusions: The diverse computational approaches used in the present study may provide a new dimension for exploring the structure-function relationship of the novel and deleterious LDLRAP1 mutations linked to FH.

Keywords: LDLRAP1; docking; familial hypercholesterolemia; molecular dynamics; protein modelling.