131333 20260217205452.0 doi 10.1002/advs.202305177 sideral 136787 ART-2024-136787 eng Larrea-Sebal, Asier OptiMo-LDLr: An Integrated In Silico Model with Enhanced Predictive Power for LDL Receptor Variants, Unraveling Hot Spot Pathogenic Residues 2024 Access copy available to the general public Unrestricted 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. info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es 14.1 2024 MATERIALS SCIENCE, MULTIDISCIPLINARY 33 / 461 = 0.072 2024 Q1 T1 NANOSCIENCE & NANOTECHNOLOGY 15 / 147 = 0.102 2024 Q1 T1 CHEMISTRY, MULTIDISCIPLINARY 19 / 239 = 0.079 2024 Q1 T1 3.775 2024 Biochemistry, Genetics and Molecular Biology (miscellaneous) 2024 Q1 Chemical Engineering (miscellaneous) 2024 Q1 Physics and Astronomy (miscellaneous) 2024 Q1 Materials Science (miscellaneous) 2024 Q1 Medicine (miscellaneous) 2024 Q1 Engineering (miscellaneous) 2024 Q1 18.2 2024 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Sasiain, Iñaki Jebari-Benslaiman, Shifa Galicia-Garcia, Unai Uribe, Kepa B. Benito-Vicente, Asier Gracia-Rubio, Irene Universidad de Zaragoza (orcid)0000-0003-1028-8581 Bediaga-Bañeres, Harbil Arrasate, Sonia Cenarro, Ana Civeira, Fernando Universidad de Zaragoza (orcid)0000-0001-7043-0952 González-Díaz, Humberto Martín, Cesar 1003 027 Universidad de Zaragoza Dpto. Anatom.Histolog.Humanas Area Anatom.Embriol.Humana 1007 610 Universidad de Zaragoza Dpto. Medicina, Psiqu. y Derm. Area Medicina 11, 13 (2024), 2305177 [15 pp.] Adv. sci. Advanced Science 2198-3844 8211033 http://zaguan.unizar.es/record/131333/files/texto_completo.pdf Versión publicada 2548920 http://zaguan.unizar.es/record/131333/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:131333 articulos driver 2026-02-17-20:19:24 ARTICLE