OptiMo-LDLr: an integrated In silico model with enhanced predictive power for LDL receptor variants, unraveling hot spot pathogenic residues

Familial hypercholesterolemia (FH) is an inherited metabolic disease affecting cholesterol metabolism, with 90% of cases caused by mutations in the LDL receptor gene (LDLR), primarily missense mutations. This study aims to integrate six commonly used predictive software to create a new model for pre...

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Bibliographic Details
Authors: Larrea Sebal, Asier, Sasiain, Iñaki, Jebari Benslaiman, Shifa, Galicia García, Unai, Belloso Uribe, Kepa, Benito Vicente, Asier, Gracia Rubio, Irene, Bediaga Bañeres, Harbil, Arrasate Gil, Sonia, Cenarro Lagunas, Ana, Civeira Murillo, Fernando, González Díaz, Humberto, Martín Plágaro, César Augusto
Format: article
Publication Date:2024
Country:España
Institution:Universidad del País Vasco
Repository:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/68044
Online Access:http://hdl.handle.net/10810/68044
Access Level:Open access
Keyword:hot spot
in silico
LDLr
predictive software
Description
Summary:Familial hypercholesterolemia (FH) is an inherited metabolic disease affecting cholesterol metabolism, with 90% of cases caused by mutations in the LDL receptor gene (LDLR), primarily missense mutations. This study aims to integrate six commonly used predictive software to create a new model for predicting LDLR mutation pathogenicity and mapping hot spot residues. Six predictive-software are selected: Polyphen-2, SIFT, MutationTaster, REVEL, VARITY, and MLb-LDLr. Software accuracy is tested with the characterized variants annotated in ClinVar and, by bioinformatic and machine learning techniques all models are integrated into a more accurate one. The resulting optimized model presents a specificity of 96.71% and a sensitivity of 98.36%. Hot spot residues with high potential of pathogenicity appear across all domains except for the signal peptide and the O-linked domain. In addition, translating this information into 3D structure of the LDLr highlights potentially pathogenic clusters within the different domains, which may be related to specific biological function. The results of this work provide a powerful tool to classify LDLR pathogenic variants. Moreover, an open-access guide user interface (OptiMo-LDLr) is provided to the scientific community. This study shows that combination of several predictive software results in a more accurate prediction to help clinicians in FH diagnosis.

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