Membrane protein solubilization and structure determination using de novo–designed proteins
Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.3 RoseTTAFold-based
Abstract
Developing therapies and vaccines against integral membrane proteins is hindered by their extensive hydrophobic surfaces, which complicate production and structural analysis. Here, we describe a general deep learning–based design approach for solubilizing native membrane proteins while preserving their sequence, fold, active-site, and ligand-binding properties. Genetically encoded de novo protein WRAPs [water-soluble RFdiffused amphipathic proteins] surround the lipid-interacting hydrophobic surfaces, rendering them thermostable and water-soluble without the need for detergents. We design WRAPs for both monomeric and oligomeric beta-barrel outer membrane proteins and helical multipass transmembrane proteins. A 2.95-angstrom-resolution cryo–electron microscopy structure of WRAPed mycobacterial porin demonstrates that WRAPs can be used for the structural determination of membrane proteins in solution. As a step toward syphilis vaccine development, we generated soluble versions of Treponema pallidum antigens.