Biology subjects

Dosey, A.

Publications and source records attributed to Dosey, A..

11 recordsLinked to original sources

bioRxiv2026

Defining the molecular interaction between influenza hemagglutinin and MHC-II

The hemagglutinin (HA) of some influenza viruses can interact with major histocompatibility complex class II (MHC-II), but how these proteins interact is unclear. Here we demonstrate that diverse H5 HAs can use MHC-II to enter cells, with avian MHC-II enabling more efficient entry than human MHC-II for most H5 HAs. To define the molecular interface, we use pseudovirus deep mutational scanning to measure how mutations to H5 HA affect its interaction with tufted duck MHC-II, and identify mutations that restrict HA to exclusively MHC-II or sialic acid receptors. We leverage identification of H5 HA mutations that increase binding to tufted duck MHC-II to determine a 4.8 [A] cryo-EM model of the complex. To support the structural model, we measure how all mutations to tufted duck MHC-II affect its interaction with H5 HA, and find the alpha chain is the dominant determinant but beta chain sites near the peptide-binding groove also contribute. To generalize these findings, we use deep mutational scanning to show that a H7 HA interacts with MHC-II similarly to H5 HA. Finally, we show that H1, H2, H3, and H9 HAs interact with avian or human MHC-II, although interactions vary among strains that evolved in different hosts.

microbiology↗

bioRxiv2025

De novo design of protein nanoparticles with integrated functional motifs

Computational design of self-assembling proteins has long relied on pre-existing structures and sequences, fundamentally limiting control over their structural and functional properties. Recent machine learning-based methods have transformed our ability to design functional small de novo proteins and oligomers, yet methods to design large de novo protein assemblies with structures tailored to specific applications are still underexplored. Here, we develop a generalizable method for designing de novo symmetric protein complexes that incorporate target functional motifs into their structures. We report 34 new protein nanoparticles that form on-target assemblies with cubic point group symmetries. The nanoparticles exhibit a wide variety of backbones that were designed with atom-level accuracy, as evidenced by several cryo-EM and crystal structures that reveal minimal deviations from the design models. We use the method to generate a de novo antigen-tailored nanoparticle vaccine that elicits robust immune responses in mice. These results establish a generalizable approach that can be used to design functional self-assembling protein complexes with structures tailored to specific applications.

biochemistry↗

bioRxiv2025

From sequence to scaffold: computational design of protein nanoparticle vaccines from AlphaFold2-predicted building blocks

Self-assembling protein nanoparticles are being increasingly utilized in the design of next-generation vaccines due to their ability to induce antibody responses of superior magnitude, breadth, and durability. Computational protein design offers a route to novel nanoparticle scaffolds with structural and biochemical features tailored to specific vaccine applications. Although strategies for designing new self-assembling proteins have been established, the recent development of powerful machine learning-based tools for protein structure prediction and design provides an opportunity to overcome several of their limitations. Here, we leveraged these tools to develop a generalizable method for designing novel self-assembling proteins starting from AlphaFold2 predictions of oligomeric protein building blocks. We used the method to generate six new 60-subunit protein nanoparticles with icosahedral symmetry, and single-particle cryo-electron microscopy reconstructions of three of them revealed that they were designed with atomic-level accuracy. To transform one of these nanoparticles into a functional immunogen, we reoriented its termini through circular permutation, added a genetically encoded oligomannose-type glycan, and displayed a stabilized trimeric variant of the influenza hemagglutinin receptor binding domain through a rigid de novo linker. The resultant immunogen elicited potent receptor-blocking and neutralizing antibody responses in mice. Our results demonstrate the practical utility of machine learning-based protein modeling tools in the design of nanoparticle vaccines. More broadly, by eliminating the requirement for experimentally determined structures of protein building blocks, our method dramatically expands the number of starting points available for designing new self-assembling proteins. Significance StatementSelf-assembling protein nanoparticle vaccines have steadily gained traction in both academic and industry vaccine development over the last decade. Recent work has shown that computationally designing new self-assembling proteins allows the structural and functional features of nanoparticle vaccines to be precisely tailored, and that this can significantly affect the vaccine-elicited immune response. To date, designing such nanoparticle vaccines has required the use of known crystal structures as starting points. Here we show how new machine learning tools can be leveraged to design new self-assembling protein nanoparticle immunogens in the absence of experimentally determined structures of the building blocks that elicit strong immune responses in mice.

biochemistry↗

bioRxiv2025

Stabilization of H5 highly pathogenic avian influenza hemagglutinin improves vaccine-elicited neutralizing antibody responses

Transmission of highly pathogenic avian influenza from H5 clade 2.3.4.4b has expanded in recent years to infect large populations of birds and mammals, heightening the risk of a human pandemic. Influenza viruses adapted to transmission in birds and some other animals tend to have a less stable hemagglutinin (HA) than seasonal influenza viruses, enabling membrane fusion at comparatively high pH levels. Here, we combine five mutations within H5 HA that dramatically increase its melting temperature and promote stable closure of the HA trimer. Structural analysis by cryo-electron microscopy revealed that the stabilizing mutations create several new hydrophobic interactions, while maintaining local HA structure. We found that vaccinating mice with stabilized H5 HA immunogens resulted in higher hemagglutination inhibition and neutralization titers than non-stabilized comparators. Epitope mapping of vaccine-elicited polyclonal antibody responses using negative stain electron microscopy and deep mutational scanning showed that site E on the side of the HA receptor binding domain was immunodominant across all groups; however, the stabilized immunogens shifted responses toward the receptor binding site (RBS), eliciting a higher proportion of neutralizing antibodies. These findings highlight that H5 HA-stabilizing mutations enhance the quality of antibody responses across different vaccine formats, underscoring their potential to improve pandemic preparedness vaccines targeting viruses from this widely circulating clade. One Sentence SummaryVaccinating with stabilized H5 HA increases the proportion of humoral responses against neutralizing RBS epitopes.

immunology↗

bioRxiv2024

Deep mutational scanning of H5 hemagglutinin to inform influenza virus surveillance

H5 influenza is a potential pandemic threat. Previous studies have identified molecular phenotypes of the viral hemagglutinin (HA) protein that contribute to pandemic risk, including cell entry, receptor preference, HA stability, and reduced neutralization by polyclonal sera. Here we use pseudovirus deep mutational scanning to measure how all mutations to a clade 2.3.4.4b H5 HA affect each phenotype. We identify mutations that allow HA to better bind 2-6-linked sialic acids, and show that some viruses already carry mutations that stabilize HA. We also identify recent viral strains with reduced neutralization to sera elicited by candidate vaccine virus. Overall, the systematic nature of deep mutational scanning combined with the safety of pseudoviruses enables comprehensive characterization of mutations to inform surveillance of H5 influenza.

microbiology↗

bioRxiv2024

Protein nanoparticle vaccines induce potent neutralizing antibody responses against MERS-CoV

Middle East respiratory syndrome coronavirus (MERS-CoV) is a zoonotic betacoronavirus that causes severe and often lethal respiratory illness in humans. The MERS-CoV spike (S) protein is the viral fusogen and the target of neutralizing antibodies, and has therefore been the focus of vaccine design efforts. Currently there are no licensed vaccines against MERS-CoV and only a few candidates have advanced to Phase I clinical trials. Here we developed MERS-CoV vaccines utilizing a computationally designed protein nanoparticle platform that has generated safe and immunogenic vaccines against various enveloped viruses, including a licensed vaccine for SARS-CoV-2. Two-component protein nanoparticles displaying MERS-CoV S-derived antigens induced robust neutralizing antibody responses and protected mice against challenge with mouse-adapted MERS-CoV. Electron microscopy polyclonal epitope mapping and serum competition assays revealed the specificities of the dominant antibody responses elicited by immunogens displaying the prefusion-stabilized S-2P trimer, receptor binding domain (RBD), or N-terminal domain (NTD). An RBD nanoparticle vaccine elicited antibodies targeting multiple non-overlapping epitopes in the RBD, whereas anti-NTD antibodies elicited by the S-2P- and NTD-based immunogens converged on a single antigenic site. Our findings demonstrate the potential of two-component nanoparticle vaccine candidates for MERS-CoV and suggest that this platform technology could be broadly applicable to betacoronavirus vaccine development.

immunology↗

bioRxiv2023

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. HIGHLIGHTSO_LIComputational design of a closed trimeric HA head ("trihead") antigen platform. C_LIO_LIDesign of a rigid, extendable linker between displayed antigen and underlying protein nanoparticle enables precise variation of antigen spacing. C_LIO_LIDecreased antigen spacing of triheads elicits antibodies with the highest HAI, neutralizing activity, and cross-reactivity. C_LIO_LIChanges to antigen spacing alter epitope specificities of vaccine-elicited antibodies. C_LI

immunology↗

bioRxiv2023

Combinatorial immune refocusing within the influenza hemagglutinin head elicits cross-neutralizing antibody responses

The head domain of influenza hemagglutinin (HA) elicits potently neutralizing yet mostly strain-specific antibodies during infection and vaccination. Here we evaluated a series of immunogens that combined several immunofocusing techniques for their ability to enhance the functional breadth of vaccine-elicited immune responses. We designed a series of "trihead" nanoparticle immunogens that display native-like closed trimeric heads from the HAs of several H1N1 influenza viruses, including hyperglycosylated variants and hypervariable variants that incorporate natural and designed sequence diversity at key positions in the periphery of the receptor binding site (RBS). Nanoparticle immunogens displaying triheads or hyperglycosylated triheads elicited higher HAI and neutralizing activity against vaccine-matched and -mismatched H1 viruses than corresponding immunogens lacking either trimer-stabilizing mutations or hyperglycosylation, indicating that both of these engineering strategies contributed to improved immunogenicity. By contrast, mosaic nanoparticle display and antigen hypervariation did not significantly alter the magnitude or breadth of vaccine-elicited antibodies. Serum competition assays and electron microscopy polyclonal epitope mapping revealed that the trihead immunogens, especially when hyperglycosylated, elicited a high proportion of antibodies targeting the RBS, as well as cross-reactive antibodies targeting a conserved epitope on the side of the head. Our results yield important insights into antibody responses against the HA head and the ability of several structure-based immunofocusing techniques to influence vaccine-elicited antibody responses. HIGHLIGHTSO_LIGeneralization of trihead antigen platform to several H1 hemagglutinins, including hyperglycosylated and hypervariable variants C_LIO_LITrimer-stabilizing mutations in trihead nanoparticle immunogens lead to lower levels of non-neutralizing antibody responses in both mice and rabbits C_LIO_LIHyperglycosylated triheads elicit higher antibody responses against broadly neutralizing epitopes C_LI

immunology↗

bioRxiv2022

Top-down design of protein nanomaterials with reinforcement learning

The multisubunit protein assemblies that play critical roles in biology are the result of evolutionary selection for function of the entire assembly, and hence the subunits in structures such as icosahedral viral capsids often fit together with remarkable shape complementarity1,2. In contrast, the large multisubunit assemblies that have been created by de novo protein design, notably the icosahedral nanocages used in a new generation of potent vaccines3-7, have been built by first designing symmetric oligomers with cyclic symmetry and then assembling these into nanocages while keeping the internal structure fixed8-14, which results in more porous structures with less extensive shape matching between the components. Such hierarchical "bottom-up" design approaches have the advantage that one interface can be designed and validated in the context of the cyclic oligomer building block15,16, but the disadvantage that the structural and functional features of the assemblies are limited by the properties of the predesigned building blocks. To overcome this limitation, we set out to develop a "top-down" reinforcement learning based approach to protein nanomaterial design in which both the structures of the subunits and the interactions between them are built up coordinately in the context of the entire assembly. We developed a Monte Carlo tree search (MCTS) method17,18 which assembles protein monomer structures in the context of an overall architecture guided by a loss function which enables specification of any desired overall structural properties such as shape and porosity. We demonstrate the power of the approach by designing hyperstable icosahedral assemblies more compact than any previously observed protein icosahedral structure (designed or naturally occurring), that have very low porosity and are robust to fusion and display of proteins as complex as influenza hemagglutinin. CryoEM structures of two designs are very close to the computational design models. Our top-down reinforcement learning approach should enable the design of a wide variety of complex protein nanomaterials by direct optimization of overall system properties.

synthetic biology↗

bioRxiv2017

The Dense-Core Vesicle Maturation Protein CCCP-1 Binds both RAB-2 and Membranes through a Conserved C-terminal Coiled-coil Domain

Dense-core vesicles (DCVs) are secretory organelles that store and release modulatory neurotransmitters from neurons and endocrine cells. Recently, the conserved coiled-coil protein CCCP-1 was identified as a component of the DCV biogenesis pathway in the nematode C. elegans. CCCP-1 binds the small GTPase RAB-2 and colocalizes with it at the trans-Golgi. Here we report a structure-function analysis of CCCP-1 to identify domains of the protein important for its localization, binding to RAB-2, and function in DCV biogenesis. We find that the CCCP-1 C-terminal domain (CC3) has multiple activities. CC3 is necessary and sufficient for CCCP-1 localization and for binding to RAB-2, and is required for the function of CCCP-1 in DCV biogenesis. Additionally, CCCP-1 binds membranes directly through its CC3 domain, indicating that CC3 may comprise a previously uncharacterized lipid-binding motif. We conclude that CCCP-1 is a coiled-coil protein that binds an activated Rab and localizes to the Golgi via its C-terminus, properties similar to members of the golgin family of proteins. CCCP-1 also shares biophysical features with golgins; it has an elongated shape and forms oligomers.\n\nSynopsis statementCCCP-1 is a coiled-coil protein important for dense-core vesicle (DCV) biogenesis. A structure-function analysis of CCCP-1 shows that its C-terminal domain is required for (1) localization to membrane compartments near the trans-Golgi, (2) binding to activated RAB-2, (3) function in DCV biogenesis, and (4) direct binding to membranes. CCCP-1 has an elongated shape and forms oligomers. These findings suggest that CCCP-1 resembles members of the golgin family of proteins that act as membrane tethers.

biochemistry↗

bioRxiv2016

Vitrification after multiple rounds of sample application and blotting improves particle density on cryo-electron microscopy grids.

Single particle cryo-electron microscopy (cryoEM) is becoming widely adopted as a tool for structural characterization of biomolecules at near-atomic resolution. Vitrification of the sample to obtain a dense distribution of particles within a single field of view remains a major bottleneck for the success of such experiments. Here, we describe a simple and cost-effective method to increase the density of frozen-hydrated particles on grids with holey carbon support films. It relies on performing multiple rounds of sample application and blotting prior to plunge freezing in liquid ethane. We show that this approach is generally applicable and significantly increases particle density for a range of samples, such as small protein complexes, viruses and filamentous assemblies. The method is versatile, easy to implement, minimizes sample requirements and can enable characterization of samples that would otherwise resist structural studies using single particle cryoEM.

biochemistry↗