Targeting protein-ligand neosurfaces with a generalizable deep learning tool.

Molecular recognition events between proteins drive biological processes in living systems. However, higher levels of mechanistic regulation have emerged, in which protein-protein interactions are conditioned to small molecules. Despite recent advances, computational tools for the design of new chemically induced protein interactions have remained a challenging task for the field.

BindCraft: one-shot design of functional protein binders.

Protein-protein interactions (PPIs) are at the core of all key biological processes. However, the complexity of the structural features that determine PPIs makes their design challenging. We present BindCraft, an open-source and automated pipeline for protein binder design with experimental success rates of 10-100%.

Dual ON/OFF-switch chimeric antigen receptor controlled by two clinically approved drugs.

The ability to remotely control the activity of chimeric antigen receptors (CARs) with small molecules can improve the safety and efficacy of gene-modified T cells. Split ON- or OFF-switch CARs involve the dissociation of tumor-antigen binding from T cell activation (i.e.

Computational design of soluble and functional membrane protein analogues.

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.