Background and aims: Familial hypercholesterolemia (FH) is a an autosomal dominant disorder characterized by very high levels of low-density lipoprotein cholesterol (LDL-C). It is estimated that >85% of all FH-causing mutations involve genetic variants in the LDL receptor (LDLR). To date, 795 single amino acid LDLR missense mutations have been reported in the Leiden Open Variation Database (LOVD). However, the functional impact of these variants on the LDLR pathway has received little attention and remains poorly understood. We aim to establish a systematic functional prediction model for LDLR single missense mutations.
Methods: Using a combined structural modeling and bioinformatics algorithm, we developed an in silico prediction model called "Structure-based Functional Impact Prediction for Mutation Identification" (SFIP-MutID) for FH with LDLR single missense mutations. We compared the pathogenicity and functional impact predictions of our model to those of other conventional tools with experimentally validated variants, as well as in vitro functional test results for patients with LDLR variants.
Results: Our SFIP-MutID model systematically predicted 13,167 potential LDLR single amino acid missense substitutions with biological effects. The functional impact of 52 out of 54 specific mutations with reported in vitro experimental data was predicted correctly. Further functional tests on LDLR variants from patients were also consistent with the prediction of our model.
Conclusions: Our LDLR structure-based computational model predicted the pathogenicity of LDLR missense mutations by linking genotypes with LDLR functional phenotypes. Our model complements other prediction tools for variant interpretation and facilitates the precision diagnosis and treatment of FH and atherosclerotic cardiovascular diseases.
Keywords: Familial hypercholesterolemia; Function prediction; LDLR; Missense mutation.
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