Publications by authors named "Lars J Dornfeld"
De novo design of complex protein folds using solely computational means remains a substantial 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 G-protein-coupled receptors, are not found in the soluble proteome, and we demonstrate that their structural features can be recapitulated in solution.
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- Designing complex protein folds using only computation is tough, but researchers have utilized a deep learning pipeline to create soluble versions of integral membrane proteins.
- They focused on unique structures, particularly from GPCRs, showing that these features can actually work outside of a cell membrane in a soluble form.
- The results showed that these soluble proteins are not only stable but also maintain their functions, opening up new avenues for drug discovery and expanding the variety of functional protein designs.
Protein structures are essential to understanding cellular processes in molecular detail. While advances in artificial intelligence revealed the tertiary structure of proteins at scale, their quaternary structure remains mostly unknown. We devise a scalable strategy based on AlphaFold2 to predict homo-oligomeric assemblies across four proteomes spanning the tree of life.
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