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. K E Y W O R D Sdocking, familial hypercholesterolemia, LDLRAP1, molecular dynamics, protein modelling Noor A. Shaik and Faten Al-Qahtani contributed equally to this work.

Structural characterization and conformational dynamics of alpha-1 antitrypsin pathogenic variants causing alpha-1-antitrypsin deficiency

“…Classical in-vivo and in-vitro approaches to study the molecular characterization of pathogenic variants are time and cost-consuming. Alternative “ in silico ” approaches, owing to their sensitivity, specificity, and accuracy, act as pre-screens for laboratory studies ( Shaik et al, 2020a ; Shaik et al, 2020b ; Awan et al, 2021 ). In this regard, a growing number of computational methods can effectively predict variant pathogenicity and stability, visualize their structures, map the conserved domains, compare their secondary structures with the wildtype protein, and simulate their ability to bind with a substrate.…”

Beyond the Usual Suspects: Expanding on Mutations and Detection for Familial Hypercholesterolemia

“…In addition, in a few families in which the severe FH phenotype segregates as a recessive trait, studies have identified causative homozygous or compound heterozygous mutations in LDLRAP1, encoding the LDLR adaptor protein 1 [37,59]. Loss of function of this protein causes Autosomal Recessive Hypercholesterolemia (ARH) [60].…”

“…The LDLRAP1 protein is a chaperone protein that binds to the LDLR and allows the internalization of the LDL-LDLR complex. Molecular defects in LDLRAP1 protein as a result of missense mutations might lead to a severe reduction in LDL-C uptake as seen in FH patients [59].…”

Genotype-protein phenotype characterization of NOD2 and IL23R missense variants associated with inflammatory bowel disease: A paradigm from molecular modelling, dynamics, and docking simulations

“…So many computations programs like SIFT (17), Polyphen (18), M-CAP (19), FATHMM (20), CADD (21) etc., each specializing on different prediction principles, are now available for exploring the relationship between genetic mutations and human diseases. Numerous studies have utilized these programs to screen clinically significant genetic variants in different human diseases (22)(23)(24)(25)(26). Therefore, in the present study, we have performed a comprehensive computational analysis of NOD2 (Arg675Trp and Gly908Arg) and IL23R (Gly149Arg and Arg381Gln) variants using diverse range of machine learning approaches.…”