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

A modular mRNA vaccine platform encoding antigen-presenting capsid virus-like particles enhances the immunogenicity of the malaria antigen Pfs25

The COVID-19 pandemic has emphasized the potential of mRNA vaccines in fighting pandemics, owing to their rapid development, strong immunogenicity and adaptability. However, a drawback is their dose-limiting reactogenicity and inability to generate durable humoral immunity. Here we introduce a modular nucleotide vaccine platform combining the advantages of genetic and capsid virus-like-particle-based vaccines. This platform allows for the display of various antigens on different capsid virus-like particles, improving the magnitude, quality and longevity of the vaccine-induced immune responses. We applied this technology to enhance the immunogenicity of the Pfs25 antigen. Immunization with lipid-nanoparticle-formulated mRNA encoding Pfs25 capsid virus-like particles resulted in higher and potentially more durable anti-Pfs25 antibody responses, along with enhanced functional activity, compared with an mRNA vaccine encoding soluble Pfs25. By improving both humoral and cellular immune responses, this approach may reduce the dose and number of administrations required for effective protection. As a result, it can improve the feasibility of both DNA- and mRNA-based vaccines targeting pandemic and endemic infectious diseases.

Local structural flexibility drives oligomorphism in computationally designed protein assemblies

Many naturally occurring protein assemblies have dynamic structures that allow them to perform specialized functions. Although computational methods for designing novel self-assembling proteins have advanced substantially over the past decade, they primarily focus on designing static structures. Here we characterize three distinct computationally designed protein assemblies that exhibit unanticipated structural diversity arising from flexibility in their subunits. Cryo-EM single-particle reconstructions and native mass spectrometry reveal two distinct architectures for two assemblies, while six cryo-EM reconstructions for the third likely represent a subset of its solution-phase structures. Structural modeling and molecular dynamics simulations indicate that constrained flexibility within the subunits of each assembly promotes a defined range of architectures rather than nonspecific aggregation. Redesigning the flexible region in one building block rescues the intended monomorphic assembly. These findings highlight structural flexibility as a powerful design principle, enabling exploration of new structural and functional spaces in protein assembly design.

Computationally designed mRNA-launched protein nanoparticle vaccines

Both protein nanoparticle and mRNA vaccines were clinically de-risked during the COVID-19 pandemic1-6. These vaccine modalities have complementary strengths: antigen display on protein nanoparticles can enhance the magnitude, quality, and durability of antibody responses7-10, while mRNA vaccines can be rapidly manufactured11 and elicit antigen-specific CD4 and CD8 T cells12,13. Here we leverage a computationally designed icosahedral protein nanoparticle that was redesigned for optimal secretion from eukaryotic cells14 to develop an mRNA-launched nanoparticle vaccine for SARS-CoV-2. The nanoparticle, which displays 60 copies of a stabilized variant of the Wuhan-Hu-1 Spike receptor binding domain (RBD)15, formed monodisperse, antigenically intact assemblies upon secretion from transfected cells. An mRNA vaccine encoding the secreted RBD nanoparticle elicited 5- to 28-fold higher levels of neutralizing antibodies than an mRNA vaccine encoding membrane-anchored Spike, induced higher levels of CD8 T cells than the same immunogen when delivered as an adjuvanted protein nanoparticle, and protected mice from vaccine-matched and -mismatched SARS-CoV-2 challenge. Our data establish that delivering protein nanoparticle immunogens via mRNA vaccines can combine the benefits of each modality and, more broadly, highlight the utility of computational protein design in genetic immunization strategies.

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.

CELL-E 2: Translating Proteins to Pictures and Back with a Bidirectional Text-to-Image Transformer

We present CELL-E 2, a novel bidirectional transformer that can generate images depicting protein subcellular localization from the amino acid sequences (and vice versa). Protein localization is a challenging problem that requires integrating sequence and image information, which most existing methods ignore. CELL-E 2 extends the work of CELL-E, not only capturing the spatial complexity of protein localization and produce probability estimates of localization atop a nucleus image, but also being able to generate sequences from images, enabling de novo protein design. We train and finetune CELL-E 2 on two large-scale datasets of human proteins. We also demonstrate how to use CELL-E 2 to create hundreds of novel nuclear localization signals (NLS). Results and interactive demos are featured at https://bohuanglab.github.io/CELL-E_2/.

Accurate computational design of three-dimensional protein crystals

Protein crystallization plays a central role in structural biology. Despite this, the process of crystallization remains poorly understood and highly empirical, with crystal contacts, lattice packing arrangements and space group preferences being largely unpredictable. Programming protein crystallization through precisely engineered side-chain-side-chain interactions across protein-protein interfaces is an outstanding challenge. Here we develop a general computational approach for designing three-dimensional protein crystals with prespecified lattice architectures at atomic accuracy that hierarchically constrains the overall number of degrees of freedom of the system. We design three pairs of oligomers that can be individually purified, and upon mixing, spontaneously self-assemble into >100 µm three-dimensional crystals. The structures of these crystals are nearly identical to the computational design models, closely corresponding in both overall architecture and the specific protein-protein interactions. The dimensions of the crystal unit cell can be systematically redesigned while retaining the space group symmetry and overall architecture, and the crystals are extremely porous and highly stable. Our approach enables the computational design of protein crystals with high accuracy, and the designed protein crystals, which have both structural and assembly information encoded in their primary sequences, provide a powerful platform for biological materials engineering.

Local structural flexibility drives oligomorphism in computationally designed protein assemblies

Many naturally occurring protein assemblies have dynamic structures that allow them to perform specialized functions. For example, clathrin coats adopt a wide variety of architectures to adapt to vesicular cargos of various sizes. Although computational methods for designing novel self-assembling proteins have advanced substantially over the past decade, most existing methods focus on designing static structures with high accuracy. Here we characterize the structures of three distinct computationally designed protein assemblies that each form multiple unanticipated architectures, and identify flexibility in specific regions of the subunits of each assembly as the source of structural diversity. Cryo-EM single-particle reconstructions and native mass spectrometry showed that only two distinct architectures were observed in two of the three cases, while we obtained six cryo-EM reconstructions that likely represent a subset of the architectures present in solution in the third case. Structural modeling and molecular dynamics simulations indicated that the surprising observation of a defined range of architectures, instead of non-specific aggregation, can be explained by constrained flexibility within the building blocks. Our results suggest that deliberate use of structural flexibility as a design principle will allow exploration of previously inaccessible structural and functional space in designed protein assemblies.

Prospects for developing an Hepatitis C virus E1E2-based nanoparticle vaccine

Globally, more than 58 million people are chronically infected with Hepatitis C virus (HCV) with 1.5 million new infections occurring each year. An effective vaccine for HCV is therefore a major unmet medical and public health need. Since HCV rapidly accumulates mutations, vaccines must elicit the production of broadly neutralising antibodies (bnAbs) in a reproducible fashion. Decades of research have generated a number of HCV vaccine candidates. Based on the available data and research through clinical development, a vaccine antigen based on the E1E2 glycoprotein complex appears to be the best choice, but robust induction of humoral and cellular responses leading to virus neutralisation has not yet been achieved. One issue that has arisen in developing an HCV vaccine (and many other vaccines as well) is the platform used for antigen delivery. The majority of viral vaccine trials have employed subunit vaccines. However, subunit vaccines often have limited immunogenicity, as seen for HCV, and thus multiple formats must be examined in order to elicit a robust anti-HCV immune response. Nanoparticle vaccines are gaining prominence in the field due to their ability to facilitate a controlled multivalent presentation and trafficking to lymph nodes, where they can interact with both arms of the immune system. This review discusses the potential for development of a nanoparticle-based HCV E1E2 vaccine, with an emphasis on the potential benefits of such an approach along with the major challenges facing the incorporation of E1E2 into nanoparticulate delivery systems and how those challenges can be addressed.

Hierarchical design of pseudosymmetric protein nanoparticles

Discrete protein assemblies ranging from hundreds of kilodaltons to hundreds of megadaltons in size are a ubiquitous feature of biological systems and perform highly specialized functions 1-3. Despite remarkable recent progress in accurately designing new self-assembling proteins, the size and complexity of these assemblies has been limited by a reliance on strict symmetry 4,5. Inspired by the pseudosymmetry observed in bacterial microcompartments and viral capsids, we developed a hierarchical computational method for designing large pseudosymmetric self-assembling protein nanomaterials. We computationally designed pseudosymmetric heterooligomeric components and used them to create discrete, cage-like protein assemblies with icosahedral symmetry containing 240, 540, and 960 subunits. At 49, 71, and 96 nm diameter, these nanoparticles are the largest bounded computationally designed protein assemblies generated to date. More broadly, by moving beyond strict symmetry, our work represents an important step towards the accurate design of arbitrary self-assembling nanoscale protein objects.