학술논문
Structure-based network analysis predicts pathogenic variants in human proteins associated with inherited retinal disease.
Document Type
Academic Journal
Author
Hauser BM; Harvard Medical School, Boston, MA, USA.; Luo Y; Harvard Medical School, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA.; Nathan A; Ragon Institute of Mass General, MIT, and Harvard, Cambridge, MA, USA.; Al-Moujahed A; Harvard Medical School, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA.; Vavvas DG; Harvard Medical School, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA.; Comander J; Harvard Medical School, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA.; Pierce EA; Harvard Medical School, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA.; Place EM; Harvard Medical School, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA.; Bujakowska KM; Harvard Medical School, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA.; Gaiha GD; Ragon Institute of Mass General, MIT, and Harvard, Cambridge, MA, USA.; Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA.; Rossin EJ; Harvard Medical School, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA. elizabeth_rossin@meei.harvard.edu.
Source
Publisher: Springer Nature in partnership with the Center of Excellence in Genomic Medicine Research at King Abdulaziz University Country of Publication: England NLM ID: 101685193 Publication Model: Electronic Cited Medium: Internet ISSN: 2056-7944 (Electronic) Linking ISSN: 20567944 NLM ISO Abbreviation: NPJ Genom Med Subsets: PubMed not MEDLINE
Subject
Language
English
Abstract
Advances in gene sequencing technologies have accelerated the identification of genetic variants, but better tools are needed to understand which are causal of disease. This would be particularly useful in fields where gene therapy is a potential therapeutic modality for a disease-causing variant such as inherited retinal disease (IRD). Here, we apply structure-based network analysis (SBNA), which has been successfully utilized to identify variant-constrained amino acid residues in viral proteins, to identify residues that may cause IRD if subject to missense mutation. SBNA is based entirely on structural first principles and is not fit to specific outcome data, which makes it distinct from other contemporary missense prediction tools. In 4 well-studied human disease-associated proteins (BRCA1, HRAS, PTEN, and ERK2) with high-quality structural data, we find that SBNA scores correlate strongly with deep mutagenesis data. When applied to 47 IRD genes with available high-quality crystal structure data, SBNA scores reliably identified disease-causing variants according to phenotype definitions from the ClinVar database. Finally, we applied this approach to 63 patients at Massachusetts Eye and Ear (MEE) with IRD but for whom no genetic cause had been identified. Untrained models built using SBNA scores and BLOSUM62 scores for IRD-associated genes successfully predicted the pathogenicity of novel variants (AUC = 0.851), allowing us to identify likely causative disease variants in 40 IRD patients. Model performance was further augmented by incorporating orthogonal data from EVE scores (AUC = 0.927), which are based on evolutionary multiple sequence alignments. In conclusion, SBNA can used to successfully identify variants as causal of disease in human proteins and may help predict variants causative of IRD in an unbiased fashion.
(© 2024. The Author(s).)
(© 2024. The Author(s).)