Programmable design of orthogonal protein heterodimers

By Zibo Chen, Scott E. Boyken, Mengxuan Jia, F. Busch, D. Flores-Solis, M. J. Bick, P. Lu, Zachary L. VanAernum, Aniruddha Sahasrabuddhe, Robert A. Langan and 8 more. 2018

167 citations recorded by the source corpus.

Specificity of interactions between two DNA strands, or between protein and DNA, is often achieved by varying bases or side chains coming off the DNA or protein backbone—for example, the bases participating in Watson–Crick pairing in the double helix, or the side chains contacting DNA in TALEN–DNA complexes. By contrast, specificity of protein–protein interactions usually involves backbone shape complementarity1, which is less modular and hence harder to generalize. Coiled-coil heterodimers are an exception, but the restricted geometry of interactions across the heterodimer interface (primarily at the heptad a and d positions2) limits the number of orthogonal pairs that can be created simply by varying side-chain interactions3,4. Here we show that protein–protein interaction specificity can be achieved using extensive and modular side-chain hydrogen-bond networks. We used the Crick generating equations5 to produce millions of four-helix backbones with varying degrees of supercoiling around a central axis, identified those accommodating extensive hydrogen-bond networks, and used Rosetta to connect pairs of helices with short loops and to optimize the remainder of the sequence. Of 97 such designs expressed in Escherichia coli, 65 formed constitutive heterodimers, and the crystal structures of four designs were in close agreement with the computational models and confirmed the designed hydrogen-bond networks. In cells, six heterodimers were fully orthogonal, and in vitro—following mixing of 32 chains from 16 heterodimer designs, denaturation in 5 M guanidine hydrochloride and reannealing—almost all of the interactions observed by native mass spectrometry were between the designed cognate pairs. The ability to design orthogonal protein heterodimers should enable sophisticated protein-based control logic for synthetic biology, and illustrates that nature has not fully explored the possibilities for programmable biomolecular interaction modalities. Computational design incorporating modular buried hydrogen networks produces highly orthogonal protein heterodimers.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Deep learning techniques have significantly impacted protein structure prediction and protein design. (2021, methodology): “The backbone structures of each coiled coil were designed in a previous study using parametric modeling to generate the helices and loop fragments to connect them into a single chain [48].”
Reconfigurable asymmetric protein assemblies through implicit negative design (2021, unknown): “The components of designed helical-hairpin heterodimers ( 10 , 11 ) on their own form homo-dimers or other higher-order homomeric aggregates that disassemble on very long time scales ( 10 , 12 ), making them unsuitable for use in constructing reconfigurable higher-order assemblies.”
De novo protein design, a retrospective (2020, background): “Rosetta’s H-bond network algorithm can also now facilitate the process of building hydrogen bond networks (Boyken et al., 2016; Chen et al., 2019).”
AlphaDesign: A de novo protein design framework based on AlphaFold (2021, background): “These applications range from the ﬁrst de novo designed proteins [48] to synthetic vaccines [49, 50], complex assemblies [51, 52] and enzymes [53, 54].”
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): “A set of hetero-dimers that have orthogonal binding speci ﬁ cities were designed using parametric backbone generation and HBNet (120).”