Hierarchical design of pseudosymmetric protein nanocages.

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. 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. Here, 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.

Rapid and automated design of two-component protein nanomaterials using ProteinMPNN.

The design of protein-protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. Deep learning methods promise to simplify protein-protein interface design and enable its application to a wide variety of problems by researchers from various scientific disciplines. Here, we test the ability of a deep learning method for protein sequence design, ProteinMPNN, to design two-component tetrahedral protein nanomaterials and benchmark its performance against Rosetta.

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.

Rapid and automated design of two-component protein nanomaterials using ProteinMPNN.

The design of novel protein-protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. A new generation of deep learning methods promises to simplify protein-protein interface design and enable its application to a wide variety of problems by researchers from various scientific disciplines. Here we test the ability of a deep learning method for protein sequence design, ProteinMPNN, to design two-component tetrahedral protein nanomaterials and benchmark its performance against Rosetta.

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. 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. 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.

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 . 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 . 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.

Fast and versatile sequence-independent protein docking for nanomaterials design using RPXDock.

Computationally designed multi-subunit assemblies have shown considerable promise for a variety of applications, including a new generation of potent vaccines. One of the major routes to such materials is rigid body sequence-independent docking of cyclic oligomers into architectures with point group or lattice symmetries. Current methods for docking and designing such assemblies are tailored to specific classes of symmetry and are difficult to modify for novel applications.

Computational design of constitutively active cGAS.

Cyclic GMP-AMP synthase (cGAS) is a pattern recognition receptor critical for the innate immune response to intracellular pathogens, DNA damage, tumorigenesis and senescence. Binding to double-stranded DNA (dsDNA) induces conformational changes in cGAS that activate the enzyme to produce 2'-3' cyclic GMP-AMP (cGAMP), a second messenger that initiates a potent interferon (IFN) response through its receptor, STING. Here, we combined two-state computational design with informatics-guided design to create constitutively active, dsDNA ligand-independent cGAS (CA-cGAS).

De novo design of self-assembling helical protein filaments.

We describe a general computational approach to designing self-assembling helical filaments from monomeric proteins and use this approach to design proteins that assemble into micrometer-scale filaments with a wide range of geometries in vivo and in vitro. Cryo-electron microscopy structures of six designs are close to the computational design models. The filament building blocks are idealized repeat proteins, and thus the diameter of the filaments can be systematically tuned by varying the number of repeat units.

Development of a thermostable nanoemulsion adjuvanted vaccine against tuberculosis using a design-of-experiments approach.

Background: Adjuvants have the potential to increase the efficacy of protein-based vaccines but need to be maintained within specific temperature and storage conditions. Lyophilization can be used to increase the thermostability of protein pharmaceuticals; however, no marketed vaccine that contains an adjuvant is currently lyophilized, and lyophilization of oil-in-water nanoemulsion adjuvants presents a specific challenge. We have previously demonstrated the feasibility of lyophilizing a candidate adjuvanted protein vaccine against (), ID93 + GLA-SE, and the subsequent improvement of thermostability; however, further development is required to prevent physicochemical changes and degradation of the TLR4 agonist glucopyranosyl lipid adjuvant formulated in an oil-in-water nanoemulsion (SE).

Lyophilization of an Adjuvanted Mycobacterium tuberculosis Vaccine in a Single-Chamber Pharmaceutical Cartridge.

Although substantial effort has been made in the development of next-generation recombinant vaccine systems, maintenance of a cold chain is still typically required and remains a critical challenge in effective vaccine distribution. The ability to engineer alternative containment systems that improve distribution and administration represents potentially significant enhancements to vaccination strategies. In this work, we evaluate the ability to successfully lyophilize a previously demonstrated thermostable tuberculosis vaccine formulation (ID93 + GLA-SE) in a cartridge format compared to a traditional vial container format.

Quantification of Multiple Components of Complex Aluminum-Based Adjuvant Mixtures by Using Fourier Transform Infrared Spectroscopy and Partial Least Squares Modeling.

Fourier transform infrared (FTIR) spectroscopy is widely used in the pharmaceutical industry for process monitoring, compositional quantification, and characterization of critical quality attributes in complex mixtures. Advantages over other spectroscopic measurements include ease of sample preparation, quantification of multiple components from a single measurement, and the ability to quantify optically opaque samples. This method describes the use of a multivariate model for quantifying a TLR4 agonist (GLA) adsorbed onto aluminum oxyhydroxide (Alhydrogel) using FTIR spectroscopy that may be adapted to quantify other complex aluminum based adjuvant mixtures.

Particle Sizing of Nanoparticle Adjuvant Formulations by Dynamic Light Scattering (DLS) and Nanoparticle Tracking Analysis (NTA).

Dynamic light scattering (DLS) and nanoparticle tracking analysis (NTA) are two orthogonal and complementary methods of measuring size of particles in a sample. These technologies use the theory of Brownian motion by analyzing the random changes of light intensity scattered by particles in solution. Both techniques can be used to characterize particle size distribution of proteins and formulations in the nanometer to low micron range.

Quantitative measurement of Toll-like receptor 4 agonists adsorbed to Alhydrogel(®) by Fourier transform infrared-attenuated total reflectance spectroscopy.

Aluminum salts have a long history as safe and effective vaccine adjuvants. In addition, aluminum salts have high adsorptive capacities for vaccine antigens and adjuvant molecules, for example, Toll-like receptor 4 (TLR4) agonists. However, the physicochemical properties of aluminum salts make direct quantitation of adsorbed molecules challenging.

Working together: interactions between vaccine antigens and adjuvants.

The development of vaccines containing adjuvants has the potential to enhance antibody and cellular immune responses, broaden protective immunity against heterogeneous pathogen strains, enable antigen dose sparing, and facilitate efficacy in immunocompromised populations. Nevertheless, the structural interplay between antigen and adjuvant components is often not taken into account in the published literature. Interactions between antigen and adjuvant formulations should be well characterized to enable optimum vaccine stability and efficacy.

Cryogenic transmission electron microscopy of recombinant tuberculosis vaccine antigen with anionic liposomes reveals formation of flattened liposomes.

Development of lipid-based adjuvant formulations to enhance the immunogenicity of recombinant vaccine antigens is a focus of modern vaccine research. Characterizing interactions between vaccine antigens and formulation excipients is important for establishing compatibility between the different components and optimizing vaccine stability and potency. Cryogenic transmission electron microscopy (TEM) is a highly informative analytical technique that may elucidate various aspects of protein- and lipid-based structures, including morphology, size, shape, and phase structure, while avoiding artifacts associated with staining-based TEM.

Modulating potency: Physicochemical characteristics are a determining factor of TLR4-agonist nanosuspension activity.

Activity of adjuvanted vaccines is difficult to predict in vitro and in vivo. The wide compositional and conformational range of formulated adjuvants, from aluminum salts to oil-in-water emulsions, makes comparisons between physicochemical and immunological properties difficult. Even within a formulated adjuvant class, excipient selection and concentration can alter potency and physicochemical properties of the mixture.

Elimination of the cold-chain dependence of a nanoemulsion adjuvanted vaccine against tuberculosis by lyophilization.

Next-generation rationally-designed vaccine adjuvants represent a significant breakthrough to enable development of vaccines against challenging diseases including tuberculosis, HIV, and malaria. New vaccine candidates often require maintenance of a cold-chain process to ensure long-term stability and separate vials to enable bedside mixing of antigen and adjuvant. This presents a significant financial and technological barrier to worldwide implementation of such vaccines.

In vitro evaluation of TLR4 agonist activity: formulation effects.

Effective in vitro evaluation of vaccine adjuvants would allow higher throughput screening compared to in vivo studies. However, vaccine adjuvants comprise a wide range of structures and formulations ranging from soluble TLR agonists to complex lipid-based formulations. The effects of formulation parameters on in vitro bioactivity assays and the correlations with in vivo adjuvant activity is not well understood.