RESUMEN
De novo design of complex protein folds using solely computational means remains a substantial challenge1. Here we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topologies, such as those from G-protein-coupled receptors2, are not found in the soluble proteome, and we demonstrate that their structural features can be recapitulated in solution. Biophysical analyses demonstrate the high thermal stability of the designs, and experimental structures show remarkable design accuracy. The soluble analogues were functionalized with native structural motifs, as a proof of concept for bringing membrane protein functions to the soluble proteome, potentially enabling new approaches in drug discovery. In summary, we have designed complex protein topologies and enriched them with functionalities from membrane proteins, with high experimental success rates, leading to a de facto expansion of the functional soluble fold space.
RESUMEN
Modern artificial intelligence-based protein structure prediction methods, such as Alphafold2, can predict structures of folded proteins with reasonable accuracy. However, Alphafold2 provides a static view of a protein, which does not show the conformational variability of the protein, domain movement in a multi-domain protein, or ligand-induced conformational changes it might undergo in solution. Small-angle X-ay scattering (SAXS) and wide-angle X-ray scattering (WAXS) are solution techniques that can aid in integrative modeling of conformationally flexible proteins, or in validating their predicted ensemble structures. While SAXS is sensitive to global structural features, WAXS can expand the scope of structural modeling by including information about local structural changes. We present SAXS and WAXS datasets obtained from conformationally flexible d-ribose binding protein (RBP) from Escherichia coli in the ribose bound and unbound forms. SAXS/WAXS datasets of RBP provided here may aid in method development efforts for more accurate prediction of structural ensembles of conformationally flexible proteins, and their conformational changes.
RESUMEN
De novo design of complex protein folds using solely computational means remains a significant challenge. Here, we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topologies, such as those from GPCRs, are not found in the soluble proteome and we demonstrate that their structural features can be recapitulated in solution. Biophysical analyses reveal high thermal stability of the designs and experimental structures show remarkable design accuracy. The soluble analogues were functionalized with native structural motifs, standing as a proof-of-concept for bringing membrane protein functions to the soluble proteome, potentially enabling new approaches in drug discovery. In summary, we designed complex protein topologies and enriched them with functionalities from membrane proteins, with high experimental success rates, leading to a de facto expansion of the functional soluble fold space.