OptiMo-LDLr: an integrated In silico model with enhanced predictive power for LDL receptor variants, unraveling hot spot pathogenic residues
dc.contributor.author | Larrea Sebal, Asier | |
dc.contributor.author | Sasiain, Iñaki | |
dc.contributor.author | Jebari Benslaiman, Shifa | |
dc.contributor.author | Galicia García, Unai | |
dc.contributor.author | Belloso Uribe, Kepa | |
dc.contributor.author | Benito Vicente, Asier | |
dc.contributor.author | Gracia Rubio, Irene | |
dc.contributor.author | Bediaga Bañeres, Harbil | |
dc.contributor.author | Arrasate Gil, Sonia | |
dc.contributor.author | Cenarro Lagunas, Ana | |
dc.contributor.author | Civeira Murillo, Fernando | |
dc.contributor.author | González Díaz, Humberto | |
dc.contributor.author | Martín Plágaro, César Augusto | |
dc.date.accessioned | 2024-05-20T13:44:05Z | |
dc.date.available | 2024-05-20T13:44:05Z | |
dc.date.issued | 2024-04 | |
dc.identifier.citation | Advanced Science 11(13) : (2024) // Article ID 2305177 | es_ES |
dc.identifier.issn | 2198-3844 | |
dc.identifier.uri | http://hdl.handle.net/10810/68044 | |
dc.description.abstract | 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. | es_ES |
dc.description.sponsorship | This research was funded by the Grupos Consolidados Gobierno Vasco 2021, grant number 449IT1720-22 and Proyectos de Generación de Conocimiento from the Ministerio de Ciencia, Innovación y Universidades, under the grant PID2022-136788OB-I00. A.L.-S. was supported by a grant PIF (2019–2020), Gobierno Vasco, and partially supported by Fundación Biofísica Bizkaia. S.J-B. was supported by a Margarita Salas Grant 2022 from the University of the Basque Country. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Wiley | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/PID2022-136788OB-I00 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | hot spot | es_ES |
dc.subject | in silico | es_ES |
dc.subject | LDLr | es_ES |
dc.subject | predictive software | es_ES |
dc.title | OptiMo-LDLr: an integrated In silico model with enhanced predictive power for LDL receptor variants, unraveling hot spot pathogenic residues | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © 2024 The Authors. Advanced Science published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. | es_ES |
dc.rights.holder | Atribución 3.0 España | * |
dc.relation.publisherversion | https://onlinelibrary.wiley.com/doi/full/10.1002/advs.202305177 | es_ES |
dc.identifier.doi | 10.1002/advs.202305177 | |
dc.departamentoes | Química Orgánica e Inorgánica | es_ES |
dc.departamentoes | Bioquímica y biología molecular | es_ES |
dc.departamentoes | Química física | es_ES |
dc.departamentoeu | Kimika Organikoa eta Ez-Organikoa | es_ES |
dc.departamentoeu | Biokimika eta biologia molekularra | es_ES |
dc.departamentoeu | Kimika fisikoa | es_ES |
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This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.