Predicting the pathogenicity of missense variants using features derived from AlphaFold2.

ABSTRACT:

Motivation

Missense variants are a frequent class of variation within the coding genome, and some of them cause Mendelian diseases. Despite advances in computational prediction, classifying missense variants into pathogenic or benign remains a major challenge in the context of personalized medicine. Recently, the structure of the human proteome was derived with unprecedented accuracy using the artificial intelligence system AlphaFold2. This raises the question of whether AlphaFold2 wild-type structures can improve the accuracy of computational pathogenicity prediction for missense variants.

Results

To address this, we first engineered a set of features for each amino acid from these structures. We then trained a random forest to distinguish between relatively common (proxy-b

SUBMITTER: Schmidt A 

PROVIDER: S-EPMC10203375 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature