Machine Learning for Prioritization of Thermostabilizing Mutations for G-Protein Coupled Receptors.

ABSTRACT: Although the three-dimensional structures of G-protein coupled receptors (GPCRs), the largest superfamily of drug targets, have enabled structure-based drug design, there are no structures available for 87% of GPCRs. This is due to the stiff challenge in purifying the inherently flexible GPCRs. Identifying thermostabilized mutant GPCRs via systematic alanine scanning mutations has been a successful strategy in stabilizing GPCRs, but it remains a daunting task for each GPCR. We developed a computational method that combines sequence-, structure-, and dynamics-based molecular properties of GPCRs that recapitulate GPCR stability, with four different machine learning methods to predict thermostable mutations ahead of experiments. This method has been trained on thermostability data for 1231 mu

SUBMITTER: Muk S 

PROVIDER: S-EPMC6895739 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature