Towards generalizable predictions for G protein-coupled receptor variant expression.

ABSTRACT: Missense mutations that compromise the plasma membrane expression (PME) of integral membrane proteins are the root cause of numerous genetic diseases. Differentiation of this class of mutations from those that specifically modify the activity of the folded protein has proven useful for the development and targeting of precision therapeutics. Nevertheless, it remains challenging to predict the effects of mutations on the stability and/ or expression of membrane proteins. In this work, we utilize deep mutational scanning data to train a series of artificial neural networks to predict the PME of transmembrane domain variants of G protein-coupled receptors from structural and/ or evolutionary features. We show that our best-performing network, which we term the PME predictor, can recapitulate

SUBMITTER: Kuntz CP 

PROVIDER: S-EPMC9382327 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature