Development of a novel prediction model based on protein structure for identifying RPE65-associated inherited retinal disease (IRDs) of missense variants.

Purpose: This study aimed to develop a prediction model to classify RPE65 -mediated inherited retinal disease (IRDs) based on protein secondary structure and to analyze phenotype-protein structure correlations of RPE65 missense variants in a Chinese cohort.
Methods: Pathogenic or likely pathogenic missense variants of RPE65 were obtained from UniProt, ClinVar, and HGMD databases. The three-dimensional structure of RPE65 was retrieved from the Protein Data Bank (PDB) and modified with Pymol software. A novel prediction model was developed using LASSO regression and multivariate logistic regression to identify RPE65 -associated IRDs. A total of 21 Chinese probands with RPE65 variants were collected to analyze phenotype-protein structure correlations of RPE65 missense variants.
Results: The study found that both pathogenic and population missense variants were associated with structural features of RPE65. Pathogenic variants were linked to sheet, β -sheet, strands, β -hairpins, Fe ²⁺ (iron center), and active site cavity, while population variants were related to helix, loop, helices, and helix-helix interactions. The novel prediction model showed accuracy and confidence in predicting the disease type of RPE65 variants (AUC = 0.7531). The study identified 25 missense variants in Chinese patients, accounting for 72.4% of total mutations. A significant correlation was observed between clinical characteristics of RPE65-associated IRDs and changes in amino acid type, specifically for missense variants of F8 (H68Y, P419S).
Conclusion: The study developed a novel prediction model based on the protein structure of RPE65 and investigated phenotype-protein structure correlations of RPE65 missense variants in a Chinese cohort. The findings provide insights into the precise diagnosis of RPE65 -mutated IRDs.
Competing Interests: The authors declare there are no competing interests.
(©2023 Wu et al.)