Protein modeling and in silico analysis to assess pathogenicity of ABCA4 variants in patients with inherited retinal disease

Abstract

Purpose: The retina-specific ABCA transporter, ABCA4, plays an essential role in translocating retinoids required by the visual cycle. ABCA4 genetic variants are known to cause a wide range of inherited retinal disorders, including Stargardt disease and cone-rod dystrophy. More than 1,400 ABCA4 missense variants have been identified; however, more than half of these remain variants of uncertain significance (VUS). The purpose of this study was to employ a predictive strategy to assess the pathogenicity of ABCA4 variants in inherited retinal diseases using protein modeling and computational approaches.

Methods: We studied 13 clinically well-defined patients with ABCA4 retinopathies and identified the presence of 10 missense variants, including one novel variant in the ABCA4 gene, by next-generation sequencing (NGS). All variants were structurally analyzed using AlphaFold2 models and existing experimental structures of human ABCA4 protein. The results of these analyses were compared with patient clinical presentations to test the effectiveness of the methods employed in predicting variant pathogenicity.

Results: We conducted a phenotype-genotype comparison of 13 genetically and phenotypically well-defined retinal disease patients. The in silico protein structure analyses we employed successfully detected the deleterious effect of missense variants found in this affected patient cohort. Our study provides American College of Medical Genetics and Genomics (ACMG)-defined supporting evidence of the pathogenicity of nine missense ABCA4 variants, aligning with the observed clinical phenotypes in this cohort.

Conclusions: In this report, we describe a systematic approach to predicting the pathogenicity of ABCA4 variants by means of three-dimensional (3D) protein modeling and in silico structure analysis. Our results demonstrate concordance between disease severity and structural changes in protein models induced by genetic variations. Furthermore, the present study suggests that in silico protein structure analysis can be used as a predictor of pathogenicity and may facilitate the assessment of genetic VUS.