Antigen spacing on protein nanoparticles influences antibody responses to vaccination.

Immunogen design approaches aim to control the specificity and quality of antibody responses elicited by next-generation vaccines. Here, we use computational protein design to generate a nanoparticle vaccine platform based on the receptor-binding domain (RBD) of influenza hemagglutinin (HA) that enables precise control of antigen conformation and spacing. HA RBDs are presented as either monomers or native-like closed trimers that are connected to the underlying nanoparticle by a rigid linker that is modularly extended to precisely control antigen spacing. Nanoparticle immunogens with decreased spacing between trimeric RBDs elicit antibodies with improved hemagglutination inhibition and neutralization potency as well as binding breadth across diverse H1 HAs. Our "trihead" nanoparticle immunogen platform provides insights into anti-HA immunity, establishes antigen spacing as an important parameter in structure-based vaccine design, and embodies several design features that could be used in next-generation vaccines against influenza and other viruses.

In vivo selection of synthetic nucleocapsids for tissue targeting.

Controlling the biodistribution of protein- and nanoparticle-based therapeutic formulations remains challenging. In vivo library selection is an effective method for identifying constructs that exhibit desired distribution behavior; library variants can be selected based on their ability to localize to the tissue or compartment of interest despite complex physiological challenges. Here, we describe further development of an in vivo library selection platform based on self-assembling protein nanoparticles encapsulating their own mRNA genomes (synthetic nucleocapsids or synNCs). We tested two distinct libraries: a low-diversity library composed of synNC surface mutations (45 variants) and a high-diversity library composed of synNCs displaying miniproteins with binder-like properties (6.2 million variants). While we did not identify any variants from the low-diversity surface library that yielded therapeutically relevant changes in biodistribution, the high-diversity miniprotein display library yielded variants that shifted accumulation toward lungs or muscles in just two rounds of in vivo selection. Our approach should contribute to achieving specific tissue homing patterns and identifying targeting ligands for diseases of interest.

Designed Endocytosis-Triggering Proteins mediate Targeted Degradation.

Endocytosis and lysosomal trafficking of cell surface receptors can be triggered by interaction with endogenous ligands. Therapeutic approaches such as LYTAC1,2 and KineTAC3, have taken advantage of this to target specific proteins for degradation by fusing modified native ligands to target binding proteins. While powerful, these approaches can be limited by possible competition with the endogenous ligand(s), the requirement in some cases for chemical modification that limits genetic encodability and can complicate manufacturing, and more generally, there may not be natural ligands which stimulate endocytosis through a given receptor. Here we describe general protein design approaches for designing endocytosis triggering binding proteins (EndoTags) that overcome these challenges. We present EndoTags for the IGF-2R, ASGPR, Sortillin, and Transferrin receptors, and show that fusing these tags to proteins which bind to soluble or transmembrane protein leads to lysosomal trafficking and target degradation; as these receptors have different tissue distributions, the different EndoTags could enable targeting of degradation to different tissues. The modularity and genetic encodability of EndoTags enables AND gate control for higher specificity targeted degradation, and the localized secretion of degraders from engineered cells. The tunability and modularity of our genetically encodable EndoTags should contribute to deciphering the relationship between receptor engagement and cellular trafficking, and they have considerable therapeutic potential as targeted degradation inducers, signaling activators for endocytosis-dependent pathways, and cellular uptake inducers for targeted antibody drug and RNA conjugates.

Precisely patterned nanofibres made from extendable protein multiplexes.

Molecular systems with coincident cyclic and superhelical symmetry axes have considerable advantages for materials design as they can be readily lengthened or shortened by changing the length of the constituent monomers. Among proteins, alpha-helical coiled coils have such symmetric, extendable architectures, but are limited by the relatively fixed geometry and flexibility of the helical protomers. Here we describe a systematic approach to generating modular and rigid repeat protein oligomers with coincident C2 to C8 and superhelical symmetry axes that can be readily extended by repeat propagation. From these building blocks, we demonstrate that a wide range of unbounded fibres can be systematically designed by introducing hydrophilic surface patches that force staggering of the monomers; the geometry of such fibres can be precisely tuned by varying the number of repeat units in the monomer and the placement of the hydrophilic patches.

De novo design of buttressed loops for sculpting protein functions.

In natural proteins, structured loops play central roles in molecular recognition, signal transduction and enzyme catalysis. However, because of the intrinsic flexibility and irregularity of loop regions, organizing multiple structured loops at protein functional sites has been very difficult to achieve by de novo protein design. Here we describe a solution to this problem that generates structured loops buttressed by extensive hydrogen bonding interactions with two neighboring loops and with secondary structure elements. We use this approach to design tandem repeat proteins with buttressed loops ranging from 9 to 14 residues in length. Experimental characterization shows the designs are folded and monodisperse, highly soluble, and thermally stable. Crystal structures are in close agreement with the computational design models, with the loops structured and buttressed by their neighbors as designed. We demonstrate the functionality afforded by loop buttressing by designing and characterizing binders for extended peptides in which the loops form one side of an extended binding pocket. The ability to design multiple structured loops should contribute quite generally to efforts to design new protein functions.

Immunogenicity and safety of SARS-CoV-2 recombinant protein nanoparticle vaccine GBP510 adjuvanted with AS03: interim results of a randomised, active-controlled, observer-blinded, phase 3 trial.

Background: GBP510 vaccine contains self-assembling, recombinant nanoparticles displaying SARS-CoV-2 spike receptor-binding domains. We report interim phase 3 immunogenicity results for GBP510 adjuvanted with AS03 (GBP510/AS03) compared with ChAdOx1-S (Vaxzevria, AstraZeneca) in healthy adults aged ≥18 years, up to 6 months after the second dose. Methods: This was a randomised, active-controlled, observer-blinded, parallel group, phase 3 study, conducted at 38 sites across six countries (South Korea, Philippines, Thailand, Vietnam, Ukraine and New Zealand). Cohort 1 (no history of SARS-CoV-2 infection/COVID-19 vaccination) was randomised 2:1 to receive two doses of GBP510/AS03 or ChAdOx1-S (immunogenicity and safety), while Cohort 2 (regardless of baseline serostatus) was randomised 5:1 (safety). Primary objectives were to demonstrate superiority in geometric mean titre (GMT) and non-inferiority in seroconversion rate (SCR; ≥4-fold rise from baseline) of GBP510/AS03 vs. ChAdOx1-S for neutralising antibodies against the ancestral strain by live-virus neutralisation assay. Secondary objectives included assessment of safety and reactogenicity (long-term 6 months cut-off date: 09 August 2022). This study was registered on ClinicalTrials.gov (NCT05007951). Findings: Between 30 August 2021 and 11 January 2022, a total of 4913 participants were screened and 4036 participants (1956 in Cohort 1 and 2080 in Cohort 2) who met eligibility criteria were enrolled and randomised to receive 2 doses of GBP510/AS03 (n = 3039) or ChAdOx1-S (n = 997). Most participants were Southeast Asian (81.5%) and aged 18-64 years (94.7%). The primary objectives assessed in per-protocol set included 877 participants in GBP510/AS03 and 441 in ChAdOx1-S group: at 2 weeks after the second vaccination, the GMT ratio (GBP510/AS03/ChAdOx1-S) in per-protocol set was 2.93 (95% CI 2.63-3.27), demonstrating superiority (95% CI lower limit >1) of GBP510/AS03; the between-group SCR difference of 10.8% (95% CI 7.68-14.32) also satisfied the non-inferiority criterion (95% CI lower limit > -5%). Neutralizing antibody titres sustained higher for the GBP510/AS03 group compared to the ChAdOx1-S group through 6 months after the second vaccination. In Safety analysis (Cohort 1 & 2), the proportion of participants with adverse events (AEs) after any vaccination was higher with GBP510/AS03 vs. ChAdOx1-S for solicited local AEs (56.7% vs. 49.2%), but was similar for solicited systemic AEs (51.2% vs. 53.5%) and unsolicited AEs (13.3% vs. 14.6%) up to 28 days after the second vaccination. No safety concerns were identified during follow-up for 6 months after the second vaccination. Interpretation: Our interim findings suggested that GBP510/AS03 met the superiority criterion for neutralising antibodies and non-inferiority criterion for SCR compared with ChAdOx1-S, and showed a clinically acceptable safety profile. Funding: This work was supported, in whole or in part, by funding from CEPI and the Bill & Melinda Gates Foundation Investments INV-010680 and INV-006462. The Bill & Melinda Gates Foundation supported this project for the generation of IND-enabling data and CEPI supported this clinical study.

De novo design of highly selective miniprotein inhibitors of integrins αvß6 and αvß8.

The RGD (Arg-Gly-Asp)-binding integrins αvß6 and αvß8 are clinically validated cancer and fibrosis targets of considerable therapeutic importance. Compounds that can discriminate between homologous αvß6 and αvß8 and other RGD integrins, stabilize specific conformational states, and have high thermal stability could have considerable therapeutic utility. Existing small molecule and antibody inhibitors do not have all these properties, and hence new approaches are needed. Here we describe a generalized method for computationally designing RGD-containing miniproteins selective for a single RGD integrin heterodimer and conformational state. We design hyperstable, selective αvß6 and αvß8 inhibitors that bind with picomolar affinity. CryoEM structures of the designed inhibitor-integrin complexes are very close to the computational design models, and show that the inhibitors stabilize specific conformational states of the αvß6 and the αvß8 integrins. In a lung fibrosis mouse model, the αvß6 inhibitor potently reduced fibrotic burden and improved overall lung mechanics, demonstrating the therapeutic potential of de novo designed integrin binding proteins with high selectivity.

Hallucination of closed repeat proteins containing central pockets.

In pseudocyclic proteins, such as TIM barrels, ß barrels, and some helical transmembrane channels, a single subunit is repeated in a cyclic pattern, giving rise to a central cavity that can serve as a pocket for ligand binding or enzymatic activity. Inspired by these proteins, we devised a deep-learning-based approach to broadly exploring the space of closed repeat proteins starting from only a specification of the repeat number and length. Biophysical data for 38 structurally diverse pseudocyclic designs produced in Escherichia coli are consistent with the design models, and the three crystal structures we were able to obtain are very close to the designed structures. Docking studies suggest the diversity of folds and central pockets provide effective starting points for designing small-molecule binders and enzymes.

De novo design of monomeric helical bundles for pH-controlled membrane lysis.

Targeted intracellular delivery via receptor-mediated endocytosis requires the delivered cargo to escape the endosome to prevent lysosomal degradation. This can in principle be achieved by membrane lysis tightly restricted to endosomal membranes upon internalization to avoid general membrane insertion and lysis. Here, we describe the design of small monomeric proteins with buried histidine containing pH-responsive hydrogen bond networks and membrane permeating amphipathic helices. Of the 30 designs that were experimentally tested, all expressed in Escherichia coli, 13 were monomeric with the expected secondary structure, and 4 designs disrupted artificial liposomes in a pH-dependent manner. Mutational analysis showed that the buried histidine hydrogen bond networks mediate pH-responsiveness and control lysis of model membranes within a very narrow range of pH (6.0-5.5) with almost no lysis occurring at neutral pH. These tightly controlled lytic monomers could help mediate endosomal escape in designed targeted delivery platforms.

Saponin Nanoparticle Adjuvants Incorporating Toll-Like Receptor Agonists Improve Vaccine Immunomodulation.

Over the past few decades, the development of potent and safe immune-activating adjuvant technologies has become the heart of intensive research in the constant fight against highly mutative and immune evasive viruses such as influenza, SARS-CoV-2, and HIV. Herein, we developed a highly modular saponin-based nanoparticle platform incorporating toll-like receptor agonists (TLRas) including TLR1/2a, TLR4a, TLR7/8a adjuvants and their mixtures. These various TLRa-SNP adjuvant constructs induce unique acute cytokine and immune-signaling profiles, leading to specific Th-responses that could be of interest depending on the target disease for prevention. In a murine vaccine study, the adjuvants greatly improved the potency, durability, breadth, and neutralization of both COVID-19 and HIV vaccine candidates, suggesting the potential broad application of these adjuvant constructs to a range of different antigens. Overall, this work demonstrates a modular TLRa-SNP adjuvant platform which could improve the design of vaccines for and dramatically impact modern vaccine development.

Elucidating the clinical and molecular spectrum of SMARCC2-associated NDD in a cohort of 65 affected individuals.

PURPOSE: Coffin-Siris and Nicolaides-Baraitser syndromes are recognizable neurodevelopmental disorders caused by germline variants in BAF complex subunits. The SMARCC2 BAFopathy was recently reported. Herein, we present clinical and molecular data on a large cohort. METHODS: Clinical symptoms for 41 novel and 24 previously published affected individuals were analyzed using the Human Phenotype Ontology. For genotype-phenotype correlations, molecular data were standardized and grouped into non-truncating and likely gene-disrupting (LGD) variants. Missense variant protein expression and BAF-subunit interactions were examined using 3D protein modeling, co-immunoprecipitation, and proximity-ligation assays. RESULTS: Neurodevelopmental delay with intellectual disability, muscular hypotonia, and behavioral disorders were the major manifestations. Clinical hallmarks of BAFopathies were rare. Clinical presentation differed significantly, with LGD variants being predominantly inherited and associated with mildly reduced or normal cognitive development, whereas non-truncating variants were mostly de novo and presented with severe developmental delay. These distinct manifestations and non-truncating variant clustering in functional domains suggest different pathomechanisms. In vitro testing showed decreased protein expression for N-terminal missense variants similar to LGD. CONCLUSION: This study improved SMARCC2 variant classification and identified discernible SMARCC2-associated phenotypes for LGD and non-truncating variants, which were distinct from other BAFopathies. The pathomechanism of most non-truncating variants has yet to be investigated.

De novo design of highly selective miniprotein inhibitors of integrins αvß6 and αvß8.

The RGD (Arg-Gly-Asp)-binding integrins αvß6 and αvß8 are clinically validated cancer and fibrosis targets of considerable therapeutic importance. Compounds that can discriminate between the two closely related integrin proteins and other RGD integrins, stabilize specific conformational states, and have sufficient stability enabling tissue restricted administration could have considerable therapeutic utility. Existing small molecules and antibody inhibitors do not have all of these properties, and hence there is a need for new approaches. Here we describe a method for computationally designing hyperstable RGD-containing miniproteins that are highly selective for a single RGD integrin heterodimer and conformational state, and use this strategy to design inhibitors of αvß6 and αvß8 with high selectivity. The αvß6 and αvß8 inhibitors have picomolar affinities for their targets, and >1000-fold selectivity over other RGD integrins. CryoEM structures are within 0.6-0.7Å root-mean-square deviation (RMSD) to the computational design models; the designed αvß6 inhibitor and native ligand stabilize the open conformation in contrast to the therapeutic anti-αvß6 antibody BG00011 that stabilizes the bent-closed conformation and caused on-target toxicity in patients with lung fibrosis, and the αvß8 inhibitor maintains the constitutively fixed extended-closed αvß8 conformation. In a mouse model of bleomycin-induced lung fibrosis, the αvß6 inhibitor potently reduced fibrotic burden and improved overall lung mechanics when delivered via oropharyngeal administration mimicking inhalation, demonstrating the therapeutic potential of de novo designed integrin binding proteins with high selectivity.

Antigen spacing on protein nanoparticles influences antibody responses to vaccination.

Immunogen design approaches aim to control the specificity and quality of antibody responses to enable the creation of next-generation vaccines with improved potency and breadth. However, our understanding of the relationship between immunogen structure and immunogenicity is limited. Here we use computational protein design to generate a self-assembling nanoparticle vaccine platform based on the head domain of influenza hemagglutinin (HA) that enables precise control of antigen conformation, flexibility, and spacing on the nanoparticle exterior. Domain-based HA head antigens were presented either as monomers or in a native-like closed trimeric conformation that prevents exposure of trimer interface epitopes. These antigens were connected to the underlying nanoparticle by a rigid linker that was modularly extended to precisely control antigen spacing. We found that nanoparticle immunogens with decreased spacing between closed trimeric head antigens elicited antibodies with improved hemagglutination inhibition (HAI) and neutralization potency as well as binding breadth across diverse HAs within a subtype. Our "trihead" nanoparticle immunogen platform thus enables new insights into anti-HA immunity, establishes antigen spacing as an important parameter in structure-based vaccine design, and embodies several design features that could be used to generate next-generation vaccines against influenza and other viruses.

Process development of a SARS-CoV-2 nanoparticle vaccine.

One of the outcomes from the global COVID-19 pandemic caused by SARS-CoV-2 has been an acceleration of development timelines to provide treatments in a timely manner. For example, it has recently been demonstrated that the development of monoclonal antibody therapeutics from vector construction to IND submission can be achieved in five to six months rather than the traditional ten-to-twelve-month timeline using CHO cells [1], [2]. This timeline is predicated on leveraging existing, robust platforms for upstream and downstream processes, analytical methods, and formulation. These platforms also reduce; the requirement for ancillary studies such as cell line stability, or long-term product stability studies. Timeline duration was further reduced by employing a transient cell line for early material supply and using a stable cell pool to manufacture toxicology study materials. The development of non-antibody biologics utilizing traditional biomanufacturing processes in CHO cells within a similar timeline presents additional challenges, such as the lack of platform processes and additional analytical assay development. In this manuscript, we describe the rapid development of a robust and reproducible process for a two-component self-assembling protein nanoparticle vaccine for SARS-CoV-2. Our work has demonstrated a successful academia-industry partnership model that responded to the COVID-19 global pandemic quickly and efficiently and could improve our preparedness for future pandemic threats.

Stepwise design of pseudosymmetric protein hetero-oligomers.

Pseudosymmetric hetero-oligomers with three or more unique subunits with overall structural (but not sequence) symmetry play key roles in biology, and systematic approaches for generating such proteins de novo would provide new routes to controlling cell signaling and designing complex protein materials. However, the de novo design of protein hetero-oligomers with three or more distinct chains with nearly identical structures is a challenging problem because it requires the accurate design of multiple protein-protein interfaces simultaneously. Here, we describe a divide-and-conquer approach that breaks the multiple-interface design challenge into a set of more tractable symmetric single-interface redesign problems, followed by structural recombination of the validated homo-oligomers into pseudosymmetric hetero-oligomers. Starting from de novo designed circular homo-oligomers composed of 9 or 24 tandemly repeated units, we redesigned the inter-subunit interfaces to generate 15 new homo-oligomers and recombined them to make 17 new hetero-oligomers, including ABC heterotrimers, A2B2 heterotetramers, and A3B3 and A2B2C2 heterohexamers which assemble with high structural specificity. The symmetric homo-oligomers and pseudosymmetric hetero-oligomers generated for each system share a common backbone, and hence are ideal building blocks for generating and functionalizing larger symmetric assemblies.

Improving the secretion of designed protein assemblies through negative design of cryptic transmembrane domains.

Computationally designed protein nanoparticles have recently emerged as a promising platform for the development of new vaccines and biologics. For many applications, secretion of designed nanoparticles from eukaryotic cells would be advantageous, but in practice, they often secrete poorly. Here we show that designed hydrophobic interfaces that drive nanoparticle assembly are often predicted to form cryptic transmembrane domains, suggesting that interaction with the membrane insertion machinery could limit efficient secretion. We develop a general computational protocol, the Degreaser, to design away cryptic transmembrane domains without sacrificing protein stability. The retroactive application of the Degreaser to previously designed nanoparticle components and nanoparticles considerably improves secretion, and modular integration of the Degreaser into design pipelines results in new nanoparticles that secrete as robustly as naturally occurring protein assemblies. Both the Degreaser protocol and the nanoparticles we describe may be broadly useful in biotechnological applications.

Modulation of FGF pathway signaling and vascular differentiation using designed oligomeric assemblies.

Growth factors and cytokines signal by binding to the extracellular domains of their receptors and drive association and transphosphorylation of the receptor intracellular tyrosine kinase domains, initiating downstream signaling cascades. To enable systematic exploration of how receptor valency and geometry affects signaling outcomes, we designed cyclic homo-oligomers with up to 8 subunits using repeat protein building blocks that can be modularly extended. By incorporating a de novo designed fibroblast growth-factor receptor (FGFR) binding module into these scaffolds, we generated a series of synthetic signaling ligands that exhibit potent valency- and geometry-dependent Ca2+ release and MAPK pathway activation. The high specificity of the designed agonists reveal distinct roles for two FGFR splice variants in driving endothelial and mesenchymal cell fates during early vascular development. The ability to incorporate receptor binding domains and repeat extensions in a modular fashion makes our designed scaffolds broadly useful for probing and manipulating cellular signaling pathways.

De novo design of small beta barrel proteins.

Small beta barrel proteins are attractive targets for computational design because of their considerable functional diversity despite their very small size (<70 amino acids). However, there are considerable challenges to designing such structures, and there has been little success thus far. Because of the small size, the hydrophobic core stabilizing the fold is necessarily very small, and the conformational strain of barrel closure can oppose folding; also intermolecular aggregation through free beta strand edges can compete with proper monomer folding. Here, we explore the de novo design of small beta barrel topologies using both Rosetta energy-based methods and deep learning approaches to design four small beta barrel folds: Src homology 3 (SH3) and oligonucleotide/oligosaccharide-binding (OB) topologies found in nature and five and six up-and-down-stranded barrels rarely if ever seen in nature. Both approaches yielded successful designs with high thermal stability and experimentally determined structures with less than 2.4 Å rmsd from the designed models. Using deep learning for backbone generation and Rosetta for sequence design yielded higher design success rates and increased structural diversity than Rosetta alone. The ability to design a large and structurally diverse set of small beta barrel proteins greatly increases the protein shape space available for designing binders to protein targets of interest.

De novo design of luciferases using deep learning.

De novo enzyme design has sought to introduce active sites and substrate-binding pockets that are predicted to catalyse a reaction of interest into geometrically compatible native scaffolds1,2, but has been limited by a lack of suitable protein structures and the complexity of native protein sequence-structure relationships. Here we describe a deep-learning-based 'family-wide hallucination' approach that generates large numbers of idealized protein structures containing diverse pocket shapes and designed sequences that encode them. We use these scaffolds to design artificial luciferases that selectively catalyse the oxidative chemiluminescence of the synthetic luciferin substrates diphenylterazine3 and 2-deoxycoelenterazine. The designed active sites position an arginine guanidinium group adjacent to an anion that develops during the reaction in a binding pocket with high shape complementarity. For both luciferin substrates, we obtain designed luciferases with high selectivity; the most active of these is a small (13.9 kDa) and thermostable (with a melting temperature higher than 95 °C) enzyme that has a catalytic efficiency on diphenylterazine (kcat/Km = 106 M-1 s-1) comparable to that of native luciferases, but a much higher substrate specificity. The creation of highly active and specific biocatalysts from scratch with broad applications in biomedicine is a key milestone for computational enzyme design, and our approach should enable generation of a wide range of luciferases and other enzymes.

Sampling of structure and sequence space of small protein folds.

Nature only samples a small fraction of the sequence space that can fold into stable proteins. Furthermore, small structural variations in a single fold, sometimes only a few amino acids, can define a protein's molecular function. Hence, to design proteins with novel functionalities, such as molecular recognition, methods to control and sample shape diversity are necessary. To explore this space, we developed and experimentally validated a computational platform that can design a wide variety of small protein folds while sampling shape diversity. We designed and evaluated stability of about 30,000 de novo protein designs of eight different folds. Among these designs, about 6,200 stable proteins were identified, including some predicted to have a first-of-its-kind minimalized thioredoxin fold. Obtained data revealed protein folding rules for structural features such as helix-connecting loops. Beyond serving as a resource for protein engineering, this massive and diverse dataset also provides training data for machine learning. We developed an accurate classifier to predict the stability of our designed proteins. The methods and the wide range of protein shapes provide a basis for designing new protein functions without compromising stability.