Linking Protein Stability to Pathogenicity: Predicting Clinical Significance of Single-Missense Mutations in Ocular Proteins Using Machine Learning.

ABSTRACT: Understanding the effect of single-missense mutations on protein stability is crucial for clinical decision-making and therapeutic development. The impact of these mutations on protein stability and 3D structure remains underexplored. Here, we developed a program to investigate the relationship between pathogenic mutations with protein unfolding and compared seven machine learning (ML) models to predict the clinical significance of single-missense mutations with unknown impacts, based on protein stability parameters. We analyzed seven proteins associated with ocular disease-causing genes. The program revealed an R-squared value of 0.846 using Decision Tree Regression between pathogenic mutations and decreased protein stability, with 96.20% of pathogenic mutations in RPE65 leading to protei

SUBMITTER: Majid I 

PROVIDER: S-EPMC11546782 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature