De Novo Design of Bioactive Protein Switches

By Robert A. Langan, Scott E. Boyken, Andrew H. Ng, Jennifer A. Samson, Galen Dods, Alexandra M. Westbrook, Taylor H. Nguyen, Taylor H. Nguyen, M. Lajoie, Zibo Chen and 6 more. 2019

242 citations recorded by the source corpus.

Allosteric regulation of protein function is widespread in biology, but is challenging for de novo protein design as it requires the explicit design of multiple states with comparable free energies. Here we explore the possibility of designing switchable protein systems de novo, through the modulation of competing inter- and intramolecular interactions. We design a static, five-helix ‘cage’ with a single interface that can interact either intramolecularly with a terminal ‘latch’ helix or intermolecularly with a peptide ‘key’. Encoded on the latch are functional motifs for binding, degradation or nuclear export that function only when the key displaces the latch from the cage. We describe orthogonal cage–key systems that function in vitro, in yeast and in mammalian cells with up to 40-fold activation of function by key. The ability to design switchable protein functions that are controlled by induced conformational change is a milestone for de novo protein design, and opens up new avenues for synthetic biology and cell engineering. A technique for the de novo design of switchable protein systems controlled by induced conformational change is demonstrated for three functional motifs, in vitro and in yeast and mammalian cells.

Subfields: Ecology, Molecular Biology, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Learning inverse folding from millions of predicted structures (2022, background): “Multiple conformations Multi-state design is of interest for engineering enzymes and biosensors (Langan et al., 2019; Quijano-Rubio et al., 2021).”
De novo protein design by inversion of the AlphaFold structure prediction network (2022, background): “, 2022), protein switches (Giordano-Attianese et al., 2020; Langan et al., 2019), and vaccines (Correia et al.”
Designing Peptides on a Quantum Computer (2019, methodology): “First, as a scalable test case, we used Rosetta’s parametric design tools ( 18 , 35 , 36 ) to construct helical bundles of various sizes, then set up design tasks involving core positions, and using different sets of rotamers.”
Recent advances in de novo protein design: Principles, methods, and applications (2021, methodology): “Helical bundle proteins have indeed proven to be very “ designable ” (22) and have consequently been adapted to many functions (13 – 15 , 23 – 27).”