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Proteins mediate their functions through chemical interactions; modeling these interactions, which are typically through sidechains, is an important need in protein design. However, constructing an all-atom generative model requires an appropriate scheme for managing the jointly continuous and discrete nature of proteins encoded in the structure and sequence. We describe an all-atom diffusion model of protein structure, Protpardelle, which instantiates a “superposition” over the possible sidechain states, and collapses it to conduct reverse diffusion for sample generation. When combined with sequence design methods, our model is able to co-design all-atom protein structure and sequence. Generated proteins are of good quality under the typical quality, diversity, and novelty metrics, and sidechains reproduce the chemical features and behavior of natural proteins. Finally, we explore the potential of our model conduct all-atom protein design and scaffold functional motifs in a backbone- and rotamer-free way.

Designingde novobinding proteins against arbitrary epitopes using a single scaffold, as seen with natural antibodies, remains an unsolved challenge in protein design. Current design methods are unable to capture the structural dynamics of flexible loops nor search loop conformational space in a principled way. Here we present Sculptor, a deep generative design algorithm that creates epitope-specific protein binders. The Sculptor algorithm constitutes a joint search over the positions, interactions, and generated conformations of a fold, and crafts a backbone to complement a user-specified epitope. Sequences are designed onto generated backbones using a combination of a residue-wise interaction database, a convolutional sequence design module, and Rosetta. Instead of relying on static structures, we capture the local conformational landscape of a single fold using molecular dynamics, and demonstrate that a model trained on such dense conformational data can generate backbones tailor-fit to an epitope. We use Sculptor to design binders against a conserved epitope on venom toxins implicated in neuromuscular paralysis, and obtain a multi-toxin binder from a small naïve library – a promising step towards creating broadly neutralizing binders. This study constitutes a novel application of deep generative modeling for epitope-targeted design, leveraging conformational dynamics to achieve function.

Precision tools for spatiotemporal control of cytoskeletal motor function are needed to dissect fundamental biological processes ranging from intracellular transport to cell migration and division. Direct optical control of motor speed and direction is one promising approach, but it remains a challenge to engineer controllable motors with desirable properties such as the speed and processivity required for transport applications in living cells. Here, we develop engineered myosin motors that combine large optical modulation depths with high velocities, and create processive myosin motors with optically controllable directionality. We characterize the performance of the motors using in vitro motility assays, single-molecule tracking and live-cell imaging. Bidirectional processive motors move efficiently toward the tips of cellular protrusions in the presence of blue light, and can transport molecular cargo in cells. Robust gearshifting myosins will further enable programmable transport in contexts ranging from in vitro active matter reconstitutions to microfabricated systems that harness molecular propulsion. High-performance engineered myosins robustly change speed or direction in response to an optical signal. In living cells, these motors localize to the tips of protrusions when illuminated and deliver molecular cargos.

The glucagon-like peptide 1 receptor (GLP-1R) is a class B G-protein coupled receptor (GPCR) and diabetes drug target expressed mainly in pancreatic β-cells that, when activated by its agonist glucagon-like peptide 1 (GLP-1) after a meal, stimulates insulin secretion and β-cell survival and proliferation. The N-terminal region of GLP-1 interacts with membrane-proximal residues of GLP-1R, stabilizing its active conformation to trigger intracellular signaling. The best-studied agonist peptides, GLP-1 and exendin-4, share sequence homology at their N-terminal region; however, modifications that can be tolerated here are not fully understood. In this work a functional screen of GLP-1 variants with randomized N-terminal domains reveals new GLP-1R agonists and uncovers a pattern whereby a negative charge is preferred at the third position in various sequence contexts. We further tested this sequence-structure-activity principle by synthesizing peptide analogues where this position was mutated to both canonical and non-canonical amino acids. We discovered a highly active GLP-1 analogue in which the native glutamate residue three positions from the N-terminus was replaced with the sulfo-containing amino acid cysteic acid (GLP-1-CYA). The receptor binding and downstream signaling properties elicited by GLP-1-CYA were similar to the wild type GLP-1 peptide. Computational modeling identified a likely mode of interaction of the negatively charged side chain in GLP-1-CYA with an arginine on GLP-1R. This work highlights a strategy of combinatorial peptide screening coupled with chemical exploration that could be used to generate novel agonists for other receptors with peptide ligands.

The plasticity of naturally occurring protein structures, which can change shape considerably in response to changes in environmental conditions, is critical to biological function. While computational methods have been used for de novo design of proteins that fold to a single state with a deep free-energy minimum [P.-S. Huang, S. E. Boyken, D. Baker, Nature 537, 320–327 (2016)], and to reengineer natural proteins to alter their dynamics [J. A. Davey, A. M. Damry, N. K. Goto, R. A. Chica, Nat. Chem. Biol. 13, 1280–1285 (2017)] or fold [P. A. Alexander, Y. He, Y. Chen, J. Orban, P. N. Bryan, Proc. Natl. Acad. Sci. U.S.A. 106, 21149–21154 (2009)], the de novo design of closely related sequences which adopt well-defined but structurally divergent structures remains an outstanding challenge. We designed closely related sequences (over 94% identity) that can adopt two very different homotrimeric helical bundle conformations—one short (∼66 Å height) and the other long (∼100 Å height)—reminiscent of the conformational transition of viral fusion proteins. Crystallographic and NMR spectroscopic characterization shows that both the short- and long-state sequences fold as designed. We sought to design bistable sequences for which both states are accessible, and obtained a single designed protein sequence that populates either the short state or the long state depending on the measurement conditions. The design of sequences which are poised to adopt two very different conformations sets the stage for creating large-scale conformational switches between structurally divergent forms.

The ability to finely control the structure of protein folds is an important prerequisite to functional protein design. The TIM barrel fold is an important target for these efforts as it is highly enriched for diverse functions in nature. Although a TIM barrel protein has been designed de novo, the ability to finely alter the curvature of the central beta barrel and the overall architecture of the fold remains elusive, limiting its utility for functional design. Here, we report the de novo design of a TIM barrel with ovoid (twofold) symmetry, drawing inspiration from natural beta and TIM barrels with ovoid curvature. We use an autoregressive backbone sampling strategy to implement our hypothesis for elongated barrel curvature, followed by an iterative enrichment sequence design protocol to obtain sequences which yield a high proportion of successfully folding designs. Designed sequences are highly stable and fold to the designed barrel curvature as determined by a 2.1 Å resolution crystal structure. The designs show robustness to drastic mutations, retaining high melting temperatures even when multiple charged residues are buried in the hydrophobic core or when the hydrophobic core is ablated to alanine. As a scaffold with a greater capacity for hosting diverse hydrogen bonding networks and installation of binding pockets or active sites, the ovoid TIM barrel represents a major step towards the de novo design of functional TIM barrels.

Staphylococcus aureus expresses several hemolytic pore-forming toxins (PFTs), which are all commonly composed of three domains: cap, rim and stem. PFTs are expressed as soluble monomers and assemble to form a transmembrane β-barrel pore in the erythrocyte cell membrane. The stem domain undergoes dramatic conformational changes to form a pore. Staphylococcal PFTs are classified into two groups: one-component α-hemolysin (α-HL) and two-component γ-hemolysin (γ-HL). The α-HL forms a homo-heptamer, whereas γ-HL is an octamer composed of F-component (LukF) and S-component (Hlg2). Because PFTs are used as materials for nanopore-based sensors, knowledge of the functional properties of PFTs is used to develop new, engineered PFTs. However, it remains challenging to design PFTs with a β-barrel pore because their formation as transmembrane protein assemblies requires large conformational changes. In the present study, aiming to investigate the design principles of the β-barrel formed as a consequence of the conformational change, chimeric mutants composed of the cap/rim domains of α-HL and the stem of LukF or Hlg2 were prepared. Biochemical characterization and electron microscopy showed that one of them assembles as a heptameric one-component PFT, whereas another participates as both a heptameric one- and heptameric/octameric two-component PFT. All chimeric mutants intrinsically assemble into SDS-resistant oligomers. Based on these observations, the role of the stem domain of these PFTs is discussed. These findings provide clues for the engineering of staphylococcal PFT β-barrels for use in further promising applications.

The task of protein sequence design is central to nearly all rational protein engineering problems, and enormous effort has gone into the development of energy functions to guide design. Here, we investigate the capability of a deep neural network model to automate design of sequences onto protein backbones, having learned directly from crystal structure data and without any human-specified priors. The model generalizes to native topologies not seen during training, producing experimentally stable designs. We evaluate the generalizability of our method to a de novo TIM-barrel scaffold. The model produces novel sequences, and high-resolution crystal structures of two designs show excellent agreement with in silico models. Our findings demonstrate the tractability of an entirely learned method for protein sequence design.

While deep learning models have seen increasing applications in protein science, few have been implemented for protein backbone generation—an important task in structure-based problems such as active site and interface design. We present a new approach to building class-specific backbones, using a variational auto-encoder to directly generate the 3D coordinates of immunoglobulins. Our model is torsion- and distance-aware, learns a high-resolution embedding of the dataset, and generates novel, high-quality structures compatible with existing design tools. We show that the Ig-VAE can be used to create a computational model of a SARS-CoV2-RBD binder via latent space sampling. We further demonstrate that the model’s generative prior is a powerful tool for guiding computational protein design, motivating a new paradigm under which backbone design is solved as constrained optimization problem in the latent space of a generative model.

Interleukin-6 (IL-6) family cytokines signal through multimeric receptor complexes, providing unique opportunities to create novel ligand-based therapeutics. The cardiotrophin-like cytokine factor 1 (CLCF1) ligand has been shown to play a role in cancer, osteoporosis, and atherosclerosis. Once bound to ciliary neurotrophic factor receptor (CNTFR), CLCF1 mediates interactions to coreceptors glycoprotein 130 (gp130) and leukemia inhibitory factor receptor (LIFR). By increasing CNTFR-mediated binding to these coreceptors we generated a receptor superagonist which surpassed the potency of natural CNTFR ligands in neuronal signaling. Through additional mutations, we generated a receptor antagonist with increased binding to CNTFR but lack of binding to the coreceptors that inhibited tumor progression in murine xenograft models of nonsmall cell lung cancer. These studies further validate the CLCF1-CNTFR signaling axis as a therapeutic target and highlight an approach of engineering cytokine activity through a small number of mutations.

RNA hydrolysis presents problems in manufacturing, long-term storage, world-wide delivery and in vivo stability of messenger RNA (mRNA)-based vaccines and therapeutics. A largely unexplored strategy to reduce mRNA hydrolysis is to redesign RNAs to form double-stranded regions, which are protected from in-line cleavage and enzymatic degradation, while coding for the same proteins. The amount of stabilization that this strategy can deliver and the most effective algorithmic approach to achieve stabilization remain poorly understood. Here, we present simple calculations for estimating RNA stability against hydrolysis, and a model that links the average unpaired probability of an mRNA, or AUP, to its overall hydrolysis rate. To characterize the stabilization achievable through structure design, we compare AUP optimization by conventional mRNA design methods to results from more computationally sophisticated algorithms and crowdsourcing through the OpenVaccine challenge on the Eterna platform. We find that rational design on Eterna and the more sophisticated algorithms lead to constructs with low AUP, which we term 'superfolder' mRNAs. These designs exhibit a wide diversity of sequence and structure features that may be desirable for translation, biophysical size, and immunogenicity. Furthermore, their folding is robust to temperature, computer modeling method, choice of flanking untranslated regions, and changes in target protein sequence, as illustrated by rapid redesign of superfolder mRNAs for B.1.351, P.1 and B.1.1.7 variants of the prefusion-stabilized SARS-CoV-2 spike protein. Increases in in vitro mRNA half-life by at least two-fold appear immediately achievable.

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at ., This Perspective reviews tools developed over the past five years in the macromolecular modeling, docking and design software Rosetta.

V-domain Ig Suppressor of T cell Activation (VISTA) is an immune checkpoint protein that inhibits the T - cell response against cancer. Similar to PD-1 and CTLA-4, antibodies that block VISTA signaling can release the brakes of the immune system and promote tumor clearance. VISTA has an Ig-like fold, but little is known about its structure and mechanism of action. Here, we report a 1.85 Å crystal structure of the human VISTA extracellular domain and highlight structural features that make VISTA unique among B7 family members. Through fine-epitope mapping, we also identify solvent-exposed residues that underlie binding to a clinically relevant anti-VISTA antibody. This antibody-binding region is also shown to interact with V-set and Ig domain-containing 3 (VSIG3), the recently proposed functional binding partner of VISTA. The structure and functional epitope determined here will help guide future drug development efforts against this important checkpoint target.

Filling in the protein fold picture Fewer than a third of the 14,849 known protein families have at least one member with an experimentally determined structure. This leaves more than 5000 protein families with no structural information. Protein modeling using residue-residue contacts inferred from evolutionary data has been successful in modeling unknown structures, but it requires large numbers of aligned sequences. Ovchinnikov et al. augmented such sequence alignments with metagenome sequence data (see the Perspective by Söding). They determined the number of sequences required to allow modeling, developed criteria for model quality, and, where possible, improved modeling by matching predicted contacts to known structures. Their method predicted quality structural models for 614 protein families, of which about 140 represent newly discovered protein folds. Science , this issue p. 294 ; see also p. 248

Naturally occurring, pharmacologically active peptides constrained with covalent crosslinks generally have shapes that have evolved to fit precisely into binding pockets on their targets. Such peptides can have excellent pharmaceutical properties, combining the stability and tissue penetration of small-molecule drugs with the specificity of much larger protein therapeutics. The ability to design constrained peptides with precisely specified tertiary structures would enable the design of shape-complementary inhibitors of arbitrary targets. Here we describe the development of computational methods for accurate de novo design of conformationally restricted peptides, and the use of these methods to design 18-47 residue, disulfide-crosslinked peptides, a subset of which are heterochiral and/or N-C backbone-cyclized. Both genetically encodable and non-canonical peptides are exceptionally stable to thermal and chemical denaturation, and 12 experimentally determined X-ray and NMR structures are nearly identical to the computational design models. The computational design methods and stable scaffolds presented here provide the basis for development of a new generation of peptide-based drugs.

There are 20(200) possible amino-acid sequences for a 200-residue protein, of which the natural evolutionary process has sampled only an infinitesimal subset. De novo protein design explores the full sequence space, guided by the physical principles that underlie protein folding. Computational methodology has advanced to the point that a wide range of structures can be designed from scratch with atomic-level accuracy. Almost all protein engineering so far has involved the modification of naturally occurring proteins; it should now be possible to design new functional proteins from the ground up to tackle current challenges in biomedicine and nanotechnology.

Summary Activating precursor B cell receptors of HIV-1 broadly neutralizing antibodies requires specifically designed immunogens. Here, we compared the abilities of three such germline-targeting immunogens against the VRC01-class receptors to activate the targeted B cells in transgenic mice expressing the germline VH of the VRC01 antibody but diverse mouse light chains. Immunogen-specific VRC01-like B cells were isolated at different time points after immunization, their VH and VL genes were sequenced, and the corresponding antibodies characterized. VRC01 B cell sub-populations with distinct cross-reactivity properties were activated by each immunogen, and these differences correlated with distinct biophysical and biochemical features of the germline-targeting immunogens. Our study indicates that the design of effective immunogens to activate B cell receptors leading to protective HIV-1 antibodies will require a better understanding of how the biophysical properties of the epitope and its surrounding surface on the germline-targeting immunogen influence its interaction with the available receptor variants in vivo.

The regular arrangements of β-strands around a central axis in β-barrels and of α-helices in coiled coils contrast with the irregular tertiary structures of most globular proteins, and have fascinated structural biologists since they were first discovered. Simple parametric models have been used to design a wide range of α-helical coiled-coil structures, but to date there has been no success with β-barrels. Here we show that accurate de novo design of β-barrels requires considerable symmetry-breaking to achieve continuous hydrogen-bond connectivity and eliminate backbone strain. We then build ensembles of β-barrel backbone models with cavity shapes that match the fluorogenic compound DFHBI,and use a hierarchical grid-based search method to simultaneously optimize the rigid-body placement of DFHBI in these cavities and the identities of the surrounding amino acids to achieve high shape and chemical complementarity. The designs have high structural accuracy and bind and fluorescently activate DFHBI in vitro and in Escherichia coli, yeast and mammalian cells. This de novo design of small-molecule binding activity, using backbones custom-built to bind the ligand, should enable the design of increasingly sophisticated ligand-binding proteins, sensors and catalysts that are not limited by the backbone geometries available in known protein structures.

A central question in protein evolution is the extent to which naturally occurring proteins sample the space of folded structures accessible to the polypeptide chain. Repeat proteins composed of multiple tandem copies of a modular structure unit are widespread in nature and have critical roles in molecular recognition, signalling, and other essential biological processes. Naturally occurring repeat proteins have been re-engineered for molecular recognition and modular scaffolding applications. Here we use computational protein design to investigate the space of folded structures that can be generated by tandem repeating a simple helix-loop-helix-loop structural motif. Eighty-three designs with sequences unrelated to known repeat proteins were experimentally characterized. Of these, 53 are monomeric and stable at 95 °C, and 43 have solution X-ray scattering spectra consistent with the design models. Crystal structures of 15 designs spanning a broad range of curvatures are in close agreement with the design models with root mean square deviations ranging from 0.7 to 2.5 Å. Our results show that existing repeat proteins occupy only a small fraction of the possible repeat protein sequence and structure space and that it is possible to design novel repeat proteins with precisely specified geometries, opening up a wide array of new possibilities for biomolecular engineering.

Summary: V-domain immunoglobulin (Ig) suppressor of T cell activation (VISTA) is an immune checkpoint protein that inhibits the T cell response against cancer. Similar to PD-1 and CTLA-4, a blockade of VISTA promotes tumor clearance by the immune system. Here, we report a 1.85 Å crystal structure of the elusive human VISTA extracellular domain, whose lack of homology necessitated a combinatorial MR-Rosetta approach for structure determination. We highlight features that make the VISTA immunoglobulin variable (IgV)-like fold unique among B7 family members, including two additional disulfide bonds and an extended loop region with an attached helix that we show forms a contiguous binding epitope for a clinically relevant anti-VISTA antibody. We propose an overlap of this antibody-binding region with the binding epitope for V-set and Ig domain containing 3 (VSIG3), a purported functional binding partner of VISTA. The structure and functional epitope presented here will help guide future drug development efforts against this important checkpoint target. : Using a combinatorial MR-Rosetta approach, Mehta et al. solve the crystal structure of human V-domain immunoglobulin (Ig) suppressor of T cell activation (VISTA), an important checkpoint protein in cancer immunotherapy. The authors use yeast display to map the epitope of a clinical anti-VISTA antibody and demonstrate its overlap to the VISTA/V-set and Ig domain containing 3 (VSIG3) binding interface. Keywords: VISTA, PD-1H, B7-H5, cancer immunotherapy, checkpoint inhibitor, high resolution crystal structure, VSIG3, IGSF11, yeast display, epitope mapping

Vaccine development to induce broadly neutralizing antibodies (bNAbs) against HIV-1 is a global health priority. Potent VRC01-class bNAbs against the CD4 binding site of HIV gp120 have been isolated from HIV-1-infected individuals; however, such bNAbs have not been induced by vaccination. Wild-type gp120 proteins lack detectable affinity for predicted germline precursors of VRC01-class bNAbs, making them poor immunogens to prime a VRC01-class response. We employed computation-guided, in vitro screening to engineer a germline-targeting gp120 outer domain immunogen that binds to multiple VRC01-class bNAbs and germline precursors, and elucidated germline binding crystallographically. When multimerized on nanoparticles, this immunogen (eOD-GT6) activates germline and mature VRC01-class B cells. Thus, eOD-GT6 nanoparticles have promise as a vaccine prime. In principle, germline-targeting strategies could be applied to other epitopes and pathogens.

Mucosal addressin cell adhesion molecule (MAdCAM) binds integrin α4β7. Their interaction directs lymphocyte homing to mucosa-associated lymphoid tissues. The interaction between the two immunoglobulin superfamily (IgSF) domains of MAdCAM and integrin α4β7 is unusual in its ability to mediate either rolling adhesion or firm adhesion of lymphocytes on vascular surfaces. We determined four crystal structures of the IgSF domains of MAdCAM to test for unusual structural features that might correlate with this functional diversity. Higher resolution 1.7- and 1.4-Å structures of the IgSF domains of MAdCAM in a previously described crystal lattice revealed two alternative conformations of the integrin-binding loop, which were deformed by large lattice contacts. New crystal forms in the presence of two different Fabs to MAdCAM demonstrate a shift in IgSF domain topology from the I2- to I1-set, with a switch of integrin-binding loop from CC' to CD. The I1-set fold and CD loop appear biologically relevant. The different conformations seen in crystal structures suggest that the integrin-binding loop of MAdCAM is inherently flexible. This contrasts with rigidity of the corresponding loops in vascular cell adhesion molecule, intercellular adhesion molecule (ICAM)-1, ICAM-2, ICAM-3, and ICAM-5 and may reflect a specialization of MAdCAM to mediate both rolling and firm adhesion by binding to different α4β7 integrin conformations.

Synthetic protein switches controlled with user-defined inputs are powerful tools for studying and controlling dynamic cellular processes. To date, these approaches have relied primarily on intermolecular regulation. Here we report a computationally guided framework for engineering intramolecular regulation of protein function. We utilize this framework to develop chemically inducible activator of RAS (CIAR), a single-component RAS rheostat that directly activates endogenous RAS in response to a small molecule. Using CIAR, we show that direct RAS activation elicits markedly different RAS-ERK signaling dynamics from growth factor stimulation, and that these dynamics differ among cell types. We also found that the clinically approved RAF inhibitor vemurafenib potently primes cells to respond to direct wild-type RAS activation. These results demonstrate the utility of CIAR for quantitatively interrogating RAS signaling. Finally, we demonstrate the general utility of our approach in design of intramolecularly regulated protein tools by applying it to the Rho family of guanine nucleotide exchange factors.

The manipulation of protein backbone structure to control interaction and function is a challenge for protein engineering. We integrated computational design with experimental selection for grafting the backbone and side chains of a two-segment HIV gp120 epitope, targeted by the cross-neutralizing antibody b12, onto an unrelated scaffold protein. The final scaffolds bound b12 with high specificity and with affinity similar to that of gp120, and crystallographic analysis of a scaffold bound to b12 revealed high structural mimicry of the gp120-b12 complex structure. The method can be generalized to design other functional proteins through backbone grafting.

The dodecahedron [corrected] is the largest of the Platonic solids, and icosahedral protein structures are widely used in biological systems for packaging and transport. There has been considerable interest in repurposing such structures for applications ranging from targeted delivery to multivalent immunogen presentation. The ability to design proteins that self-assemble into precisely specified, highly ordered icosahedral structures would open the door to a new generation of protein containers with properties custom-tailored to specific applications. Here we describe the computational design of a 25-nanometre icosahedral nanocage that self-assembles from trimeric protein building blocks. The designed protein was produced in Escherichia coli, and found by electron microscopy to assemble into a homogenous population of icosahedral particles nearly identical to the design model. The particles are stable in 6.7 molar guanidine hydrochloride at up to 80 degrees Celsius, and undergo extremely abrupt, but reversible, disassembly between 2 molar and 2.25 molar guanidinium thiocyanate. The dodecahedron [corrected] is robust to genetic fusions: one or two copies of green fluorescent protein (GFP) can be fused to each of the 60 subunits to create highly fluorescent ‘standard candles’ for use in light microscopy, and a designed protein pentamer can be placed in the centre of each of the 20 pentameric faces to modulate the size of the entrance/exit channels of the cage. Such robust and customizable nanocages should have considerable utility in targeted drug delivery, vaccine design and synthetic biology.

As an approach to both explore the physical/chemical parameters that drive molecular self-assembly and to generate novel protein oligomers, we have developed a procedure to generate protein dimers from monomeric proteins using computational protein docking and amino acid sequence design. A fast Fourier transform-based docking algorithm was used to generate a model for a dimeric version of the 56-amino-acid beta1 domain of streptococcal protein G. Computational amino acid sequence design of 24 residues at the dimer interface resulted in a heterodimer comprised of 12-fold and eightfold variants of the wild-type protein. The designed proteins were expressed, purified, and characterized using analytical ultracentrifugation and heteronuclear NMR techniques. Although the measured dissociation constant was modest ( approximately 300 microM), 2D-[(1)H,(15)N]-HSQC NMR spectra of one of the designed proteins in the absence and presence of its binding partner showed clear evidence of specific dimer formation.

Homodimers are the most common type of protein assembly in nature and have distinct features compared with heterodimers and higher order oligomers. Understanding homodimer interactions at the atomic level is critical both for elucidating their biological mechanisms of action and for accurate modeling of complexes of unknown structure. Computation-based design of novel protein–protein interfaces can serve as a bottom-up method to further our understanding of protein interactions. Previous studies have demonstrated that the de novo design of homodimers can be achieved to atomic-level accuracy by β-strand assembly or through metal-mediated interactions. Here, we report the design and experimental characterization of a α-helix–mediated homodimer with C2 symmetry based on a monomeric Drosophila engrailed homeodomain scaffold. A solution NMR structure shows that the homodimer exhibits parallel helical packing similar to the design model. Because the mutations leading to dimer formation resulted in poor thermostability of the system, design success was facilitated by the introduction of independent thermostabilizing mutations into the scaffold. This two-step design approach, function and stabilization, is likely to be generally applicable, especially if the desired scaffold is of low thermostability.

Designing proteins with novel protein/protein binding properties can be achieved by combining the tools that have been developed independently for protein docking and protein design. We describe here the sequence-independent generation of protein dimer orientations by protein docking for use as scaffolds in protein sequence design algorithms. To dock monomers into sequence-independent dimer conformations, we use a reduced representation in which the side chains are approximated by spheres with atomic radii derived from known C2 symmetry-related homodimers. The interfaces of C2-related homodimers are usually more hydrophobic and protein core-like than the interfaces of heterodimers; we parameterize the radii for docking against this feature to capture and recreate the spatial characteristics of a hydrophobic interface. A fast Fourier transform-based geometric recognition algorithm is used for docking the reduced representation protein models. The resulting docking algorithm successfully predicted the wild-type homodimer orientations in 65 out of 121 dimer test cases. The success rate increases to approximately 70% for the subset of molecules with large surface area burial in the interface relative to their chain length. Forty-five of the predictions exhibited less than 1 A C(alpha) RMSD compared to the native X-ray structures. The reduced protein representation therefore appears to be a reasonable approximation and can be used to position protein backbones in plausible orientations for homodimer design.

Building with alphahelical coiled coils Understanding how proteins fold into well-defined three-dimensional structures has been a longstanding challenge. Increased understanding has led to increased success at designing proteins that mimic existing protein folds. This raises the possibility of custom design of proteins with structures not seen in nature. Thomson et al. describe the design of channelcontaining α-helical barrels, and Huang et al. designed hyperstable helical bundles. Both groups used rational and computational design to make new protein structures based on α-helical coiled coils but took different routes to reach different target structures. Science , this issue p. 485 , p. 481

Protein and peptide self-assembly is a powerful design principle for engineering of new biomolecules. More sophisticated biomaterials could be built if both the structure of the overall assembly and that of the self-assembling building block could be controlled. To approach this problem, we developed a computational design protocol to enable de novo design of self-assembling peptides with predefined structure. The protocol was used to design a peptide building block with a βαβ fold that self-assembles into fibrillar structures. The peptide associates into a double β-sheet structure with tightly packed α-helices decorating the exterior of the fibrils. Using circular dichroism, Fourier transform infrared spectroscopy, electron microscopy and X-ray fiber diffraction, we demonstrate that the peptide adopts the designed conformation. The results demonstrate that computational protein design can be used to engineer protein and peptide assemblies with predefined three-dimensional structures, which can serve as scaffolds for the development of functional biomaterials. Rationally designed proteins and peptides could also be used to investigate the subtle energetic and entropic tradeoffs in natural self-assembly processes and the relation between assembly structure and assembly mechanism. We demonstrate that the de novo designed peptide self-assembles with a mechanism that is more complicated than expected, in a process where small changes in solution conditions can lead to significant differences in assembly properties and conformation. These results highlight that formation of structured protein/peptide assemblies is often dependent on the formation of weak but highly precise intermolecular interactions.

Shape complementarity is an important component of molecular recognition, and the ability to precisely adjust the shape of a binding scaffold to match a target of interest would greatly facilitate the creation of high affinity protein reagents and therapeutics. Here we describe a general approach to control the shape of the binding surface on repeat protein scaffolds, and apply it to leucine rich repeat proteins. First, a set of self-compatible building block modules are designed that when polymerized each generate surfaces with unique but constant curvatures. Second, a set of junction modules that connect the different building blocks are designed. Finally, new proteins with custom designed shapes are generated by appropriately combining building block and junction modules. Crystal structures of the designs illustrate the power of the approach in controlling repeat protein curvature.

Nature 535, 136–139 (2016); doi:10.1038/nature18010 In this Letter, the 60-subunit protein assembly presented more strongly resembles a wireframe dodecahedron than an icosahedron. We thank the reader who drew this to our attention and we agree that all instances of the word “icosahedron” should be changed to “dodecahedron”.

Methods for rapid and reliable design and structure prediction of linker loops would facilitate a variety of protein engineering applications. Circular permutation, in which the existing termini of a protein are linked by the polypeptide chain and new termini are created, is one such application that has been employed for decreasing proteolytic susceptibility and other functional purposes. The length and sequence of the linker can impact the expression level, solubility, structure and function of the permuted variants. Hence it is desirable to achieve atomic-level accuracy in linker design. Here, we describe the use of RosettaRemodel for design and structure prediction of circular permutation linkers on a model protein. A crystal structure of one of the permuted variants confirmed the accuracy of the computational prediction, where the all-atom rmsd of the linker region was 0.89 A between the model and the crystal structure. This result suggests that RosettaRemodel may be generally useful for the design and structure prediction of protein loop regions for circular permutations or other structure-function manipulations.

Protein fibril formation is implicated in many diseases, and therefore much effort has been focused toward the development of inhibitors of this process. In a previous project, a monomeric protein was computationally engineered to bind itself and form a heterodimer complex following interfacial redesign. One of the protein monomers, termed monomer-B, was unintentionally destabilized and shown to form macroscopic fibrils. Interestingly, in the presence of the designed binding partner, fibril formation was blocked. Here we describe the complete characterization of the amyloid properties of monomer-B and the inhibition of fiber formation by the designed binding partner, monomer-A. Both proteins are mutants of the betal domain of streptococcal protein-G. The free monomer-B protein forms amyloid-type fibrils, as determined by transmission electron microscopy and the change in fluorescence of Thioflavin T, an amyloid-specific dye. Fibril formation kinetics are influenced by pH, protein concentration, and seeding with preformed fibrils. Under all conditions tested, monomer-A was able to inhibit the formation of monomer-B fibrils. This inhibition is specific to the engineered interaction, as incubation of monomer-B with wild-type protein-G (a structural homologue) did not result in inhibition under the same conditions. Thus, this de novo-designed heterodimeric complex is an excellent model system for the study of protein-based fibril formation and inhibition. This system provides additional insight into the development of pharmaceuticals for amyloid disorders, as well as the potential use of amyloid fibrils for self-assembling nanostructures.

We describe RosettaRemodel, a generalized framework for flexible protein design that provides a versatile and convenient interface to the Rosetta modeling suite. RosettaRemodel employs a unified interface, called a blueprint, which allows detailed control over many aspects of flexible backbone protein design calculations. RosettaRemodel allows the construction and elaboration of customized protocols for a wide range of design problems ranging from loop insertion and deletion, disulfide engineering, domain assembly, loop remodeling, motif grafting, symmetrical units, to de novo structure modeling.