OptiMo-LDLr: an integrated In silico model with enhanced predictive power for LDL receptor variants, unraveling hot spot pathogenic residues
View/ Open
Date
2024-04Author
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
Metadata
Show full item record
Advanced Science 11(13) : (2024) // Article ID 2305177
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.
Collections
Except where otherwise noted, this item's license is described as © 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.