Design of High Affinity Binders to Convex Protein Target Sites.

While there has been progress in the de novo design of small globular miniproteins (50-65 residues) to bind to primarily concave regions of a target protein surface, computational design of minibinders to convex binding sites remains an outstanding challenge due to low level of overall shape complementarity. Here, we describe a general approach to generate computationally designed proteins which bind to convex target sites that employ geometrically matching concave scaffolds. We used this approach to design proteins binding to TGFßRII, CTLA-4 and PD-L1 which following experimental optimization have low nanomolar to picomolar affinities and potent biological activity. Co-crystal structures of the TGFßRII and CTLA-4 binders in complex with the receptors are in close agreement with the design models. Our approach provides a general route to generating very high affinity binders to convex protein target sites.

De novo design of high-affinity binders of bioactive helical peptides.

Many peptide hormones form an α-helix on binding their receptors1-4, and sensitive methods for their detection could contribute to better clinical management of disease5. De novo protein design can now generate binders with high affinity and specificity to structured proteins6,7. However, the design of interactions between proteins and short peptides with helical propensity is an unmet challenge. Here we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that by extending RFdiffusion8 to enable binder design to flexible targets, and to refining input structure models by successive noising and denoising (partial diffusion), picomolar-affinity binders can be generated to helical peptide targets by either refining designs generated with other methods, or completely de novo starting from random noise distributions without any subsequent experimental optimization. The RFdiffusion designs enable the enrichment and subsequent detection of parathyroid hormone and glucagon by mass spectrometry, and the construction of bioluminescence-based protein biosensors. The ability to design binders to conformationally variable targets, and to optimize by partial diffusion both natural and designed proteins, should be broadly useful.

Design of stimulus-responsive two-state hinge proteins.

In nature, proteins that switch between two conformations in response to environmental stimuli structurally transduce biochemical information in a manner analogous to how transistors control information flow in computing devices. Designing proteins with two distinct but fully structured conformations is a challenge for protein design as it requires sculpting an energy landscape with two distinct minima. Here we describe the design of "hinge" proteins that populate one designed state in the absence of ligand and a second designed state in the presence of ligand. X-ray crystallography, electron microscopy, double electron-electron resonance spectroscopy, and binding measurements demonstrate that despite the significant structural differences the two states are designed with atomic level accuracy and that the conformational and binding equilibria are closely coupled.

Design of Diverse Asymmetric Pockets in De Novo Homo-oligomeric Proteins.

A challenge for design of protein-small-molecule recognition is that incorporation of cavities with size, shape, and composition suitable for specific recognition can considerably destabilize protein monomers. This challenge can be overcome through binding pockets formed at homo-oligomeric interfaces between folded monomers. Interfaces surrounding the central homo-oligomer symmetry axes necessarily have the same symmetry and so may not be well suited to binding asymmetric molecules. To enable general recognition of arbitrary asymmetric substrates and small molecules, we developed an approach to designing asymmetric interfaces at off-axis sites on homo-oligomers, analogous to those found in native homo-oligomeric proteins such as glutamine synthetase. We symmetrically dock curved helical repeat proteins such that they form pockets at the asymmetric interface of the oligomer with sizes ranging from several angstroms, appropriate for binding a single ion, to up to more than 20 Å across. Of the 133 proteins tested, 84 had soluble expression in E. coli, 47 had correct oligomeric states in solution, 35 had small-angle X-ray scattering (SAXS) data largely consistent with design models, and 8 had negative-stain electron microscopy (nsEM) 2D class averages showing the structures coming together as designed. Both an X-ray crystal structure and a cryogenic electron microscopy (cryoEM) structure are close to the computational design models. The nature of these proteins as homo-oligomers allows them to be readily built into higher-order structures such as nanocages, and the asymmetric pockets of these structures open rich possibilities for small-molecule binder design free from the constraints associated with monomer destabilization.

Insights into the Evolution and Function of Auxin Signaling F-Box Proteins in Arabidopsis thaliana Through Synthetic Analysis of Natural Variants.

The evolution of complex body plans in land plants has been paralleled by gene duplication and divergence within nuclear auxin-signaling networks. A deep mechanistic understanding of auxin signaling proteins therefore may allow rational engineering of novel plant architectures. Toward that end, we analyzed natural variation in the auxin receptor F-box family of wild accessions of the reference plant Arabidopsis thaliana and used this information to populate a structure/function map. We employed a synthetic assay to identify natural hypermorphic F-box variants and then assayed auxin-associated phenotypes in accessions expressing these variants. To more directly measure the impact of the strongest variant in our synthetic assay on auxin sensitivity, we generated transgenic plants expressing this allele. Together, our findings link evolved sequence variation to altered molecular performance and auxin sensitivity. This approach demonstrates the potential for combining synthetic biology approaches with quantitative phenotypes to harness the wealth of available sequence information and guide future engineering efforts of diverse signaling pathways.

Comparing atomistic molecular mechanics force fields for a difficult target: a case study on the Alzheimer's amyloid ß-peptide.

Macromolecular function arises from structure, and many diseases are associated with misfolding of proteins. Molecular simulation methods can augment experimental techniques to understand misfolding and aggregation pathways with atomistic resolution, but the reliability of these predictions is a function of the parameters used for the simulation. There are many biomolecular force fields available, but most are validated using stably folded structures. Here, we present the results of molecular dynamics simulations on the intrinsically disordered amyloid ß-peptide (Aß), whose misfolding and aggregation give rise to the symptoms of Alzheimer's disease. Because of the link between secondary structure changes and pathology, being able to accurately model the structure of Aß would greatly improve our understanding of this disease, and it may facilitate application of modeling approaches to other protein misfolding disorders. To this end, we compared five popular atomistic force fields (AMBER03, CHARMM22 + CMAP, GROMOS96 53A6, GROMOS96 54A7, and OPLS-AA) to determine which could best model the structure of Aß. By comparing secondary structure content, NMR shifts, and radius of gyration to available experimental data, we conclude that AMBER03 and CHARMM22 + CMAP over-stabilize helical structure within Aß, with CHARMM22 + CMAP also producing elongated Aß structures, in conflict with experimental findings. OPLS-AA, GROMOS96 53A6, and GROMOS96 54A7 produce very similar results in terms of helical and ß-strand content, calculated NMR shifts, and radii of gyration that agree well with experimental data.