OptiMo-LDLr: An Integrated In Silico Model with Enhanced Predictive Power for LDL Receptor Variants, Unraveling Hot Spot Pathogenic Residues

Larrea-Sebal, Asier ; Sasiain, Iñaki ; Jebari-Benslaiman, Shifa ; Galicia-Garcia, Unai ; Uribe, Kepa B. ; Benito-Vicente, Asier ; Gracia-Rubio, Irene (Universidad de Zaragoza) ; Bediaga-Bañeres, Harbil ; Arrasate, Sonia ; Cenarro, Ana ; Civeira, Fernando (Universidad de Zaragoza) ; González-Díaz, Humberto ; Martín, Cesar

Resumen: 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.

Idioma: Inglés

DOI: 10.1002/advs.202305177

Año: 2024

Publicado en: Advanced Science 11, 13 (2024), 2305177 [15 pp.]
ISSN: 2198-3844

Factor impacto JCR: 14.1 (2024)

Categ. JCR: MATERIALS SCIENCE, MULTIDISCIPLINARY rank: 33 / 461 = 0.072 (2024) - Q1 - T1
Categ. JCR: NANOSCIENCE & NANOTECHNOLOGY rank: 15 / 147 = 0.102 (2024) - Q1 - T1
Categ. JCR: CHEMISTRY, MULTIDISCIPLINARY rank: 19 / 239 = 0.079 (2024) - Q1 - T1

Factor impacto CITESCORE: 18.2 - Physics and Astronomy (all) (Q1) - Materials Science (all) (Q1) - Biochemistry, Genetics and Molecular Biology (miscellaneous) (Q1) - Medicine (miscellaneous) (Q1) - Engineering (all) (Q1) - Chemical Engineering (all) (Q1)

Factor impacto SCIMAGO: 3.775 - Biochemistry, Genetics and Molecular Biology (miscellaneous) (Q1) - Chemical Engineering (miscellaneous) (Q1) - Physics and Astronomy (miscellaneous) (Q1) - Materials Science (miscellaneous) (Q1) - Medicine (miscellaneous) (Q1) - Engineering (miscellaneous) (Q1)

Tipo y forma: Article (Published version)

Área (Departamento): Area Anatom.Embriol.Humana (Dpto. Anatom.Histolog.Humanas)
Área (Departamento): Area Medicina (Dpto. Medicina, Psiqu. y Derm.)