Biasing AlphaFold2 to predict GPCRs and kinases with user-defined functional or structural properties.

ABSTRACT: Determining the three-dimensional structure of proteins in their native functional states has been a longstanding challenge in structural biology. While integrative structural biology has been the most effective way to get a high-accuracy structure of different conformations and mechanistic insights for larger proteins, advances in deep machine-learning algorithms have paved the way to fully computational predictions. In this field, AlphaFold2 (AF2) pioneered ab initio high-accuracy single-chain modeling. Since then, different customizations have expanded the number of conformational states accessible through AF2. Here, we further expanded AF2 with the aim of enriching an ensemble of models with user-defined functional or structural features. We tackled two common protein families f

SUBMITTER: Sala D 

PROVIDER: S-EPMC9978208 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature