Back

Computational design of soluble analogues of integral membrane protein structures

Goverde, C. A.; Pacesa, M.; Dornfeld, L. J.; Georgeon, S.; Rosset, S.; Dauparas, J.; Shellhaas, C.; Kozlov, S.; Baker, D.; Ovchinnikov, S.; Correia, B.

2023-05-09 bioinformatics
10.1101/2023.05.09.540044 bioRxiv
Show abstract

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.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.