Complete and cooperative in vitro assembly of computationally designed self-assembling protein nanomaterials

A suite of designed protein cages using machine learning and protein fragment-based protocols

Designed protein cages and related materials provide unique opportunities for applications in biotechnology and medicine, but their creation remains challenging. Here, we apply computational approaches to design a suite of tetrahedrally symmetric, self-assembling protein cages. For the generation of docked conformations, we emphasize a protein fragment-based approach, while for sequence design of the de novo interface, a comparison of knowledge-based and machine learning protocols highlights the power and increased experimental success achieved using ProteinMPNN. An analysis of design outcomes provides insights for improving interface design protocols, including prioritizing fragment-based motifs, balancing interface hydrophobicity and polarity, and identifying preferred polar contact patterns. In all, we report five structures for seven protein cages, along with two structures of intermediate assemblies, with the highest resolution reaching 2.0 Å using cryo-EM. This set of designed cages adds substantially to the body of available protein nanoparticles, and to methodologies for their creation.

The recent advancements in protein nanoparticles for immunotherapy

In recent years, the advancement of nanoparticle-based immunotherapy has introduced an innovative strategy for combatting diseases. Compared with other types of nanoparticles, protein nanoparticles have obtained substantial attention owing to their remarkable biocompatibility, biodegradability, ease of modification, and finely designed spatial structures. Nature provides several protein nanoparticle platforms, including viral capsids, ferritin, and albumin, which hold significant potential for disease treatment. These naturally occurring protein nanoparticles not only serve as effective drug delivery platforms but also augment antigen delivery and targeting capabilities through techniques like genetic modification and covalent conjugation. Motivated by nature's originality and driven by progress in computational methodologies, scientists have crafted numerous protein nanoparticles with intricate assembly structures, showing significant potential in the development of multivalent vaccines. Consequently, both naturally occurring and de novo designed protein nanoparticles are anticipated to enhance the effectiveness of immunotherapy. This review consolidates the advancements in protein nanoparticles for immunotherapy across diseases including cancer and other diseases like influenza, pneumonia, and hepatitis.

Design of a symmetry-broken tetrahedral protein cage by a method of internal steric occlusion

Methods in protein design have made it possible to create large and complex, self-assembling protein cages with diverse applications. These have largely been based on highly symmetric forms exemplified by the Platonic solids. Prospective applications of protein cages would be expanded by strategies for breaking the designed symmetry, for example, so that only one or a few (instead of many) copies of an exterior domain or motif might be displayed on their surfaces. Here we demonstrate a straightforward design approach for creating symmetry-broken protein cages able to display singular copies of outward-facing domains. We modify the subunit of an otherwise symmetric protein cage through fusion to a small inward-facing domain, only one copy of which can be accommodated in the cage interior. Using biochemical methods and native mass spectrometry, we show that co-expression of the original subunit and the modified subunit, which is further fused to an outward-facing anti-GFP DARPin domain, leads to self-assembly of a protein cage presenting just one copy of the DARPin protein on its exterior. This strategy of designed occlusion provides a facile route for creating new types of protein cages with unique properties.

Exploring Host-Guest Interactions within a 600 kDa DegP Protease Cage Complex Using Hydrodynamics Measurements and Methyl-TROSY NMR

The DegP protease-chaperone operates within the periplasm of Gram-negative bacteria, where it assists in the regulation of protein homeostasis, promotes virulence, and is essential to survival under stress. To carry out these tasks, DegP forms a network of preorganized apo oligomers that facilitate the capture of substrates within distributions of cage-like complexes which expand to encapsulate clients of various sizes. Although the architectures of DegP cage complexes are well understood, little is known about the structures, dynamics, and interactions of client proteins within DegP cages and the relationship between client structural dynamics and function. Here, we probe host-guest interactions within a 600 kDa DegP cage complex throughout the DegP activation cycle using a model α-helical client protein through a combination of hydrodynamics measurements, methyl-transverse relaxation optimized spectroscopy-based solution nuclear magnetic resonance studies, and proteolytic activity assays. We find that in the presence of the client, DegP cages assemble cooperatively with few intermediates. Our data further show that the N-terminal half of the bound client, which projects into the interior of the cages, is predominantly unfolded and flexible, and exchanges between multiple conformational states over a wide range of time scales. Finally, we show that a concerted structural transition of the protease domains of DegP occurs upon client engagement, leading to activation. Together, our findings support a model of DegP as a highly cooperative and dynamic molecular machine that stabilizes unfolded states of clients, primarily via interactions with their C-termini, giving rise to efficient cleavage.

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. ProteinMPNN had a similar success rate to Rosetta, yielding 13 new experimentally confirmed assemblies, but required orders of magnitude less computation and no manual refinement. The interfaces designed by ProteinMPNN were substantially more polar than those designed by Rosetta, which facilitated in vitro assembly of the designed nanomaterials from independently purified components. Crystal structures of several of the assemblies confirmed the accuracy of the design method at high resolution. Our results showcase the potential of deep learning-based methods to unlock the widespread application of designed protein-protein interfaces and self-assembling protein nanomaterials in biotechnology.

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.

Mosaic quadrivalent influenza vaccine single nanoparticle characterization

Recent work by our laboratory and others indicates that co-display of multiple antigens on protein-based nanoparticles may be key to induce cross-reactive antibodies that provide broad protection against disease. To reach the ultimate goal of a universal vaccine for seasonal influenza, a mosaic influenza nanoparticle vaccine (FluMos-v1) was developed for clinical trial (NCT04896086). FluMos-v1 is unique in that it is designed to co-display four recently circulating haemagglutinin (HA) strains; however, current vaccine analysis techniques are limited to nanoparticle population analysis, thus, are unable to determine the valency of an individual nanoparticle. For the first time, we demonstrate by total internal reflection fluorescence microscopy and supportive physical-chemical methods that the co-display of four antigens is indeed achieved in single nanoparticles. Additionally, we have determined percentages of multivalent (mosaic) nanoparticles with four, three, or two HA proteins. The integrated imaging and physicochemical methods we have developed for single nanoparticle multivalency will serve to further understand immunogenicity data from our current FluMos-v1 clinical trial.

Applying artificial intelligence and computational modeling to nanomedicine

Achieving specific and targeted delivery of nanomedicines to diseased tissues is a major challenge. This is because the process of designing, formulating, testing, and selecting a nanoparticle delivery vehicle for a specific disease target is governed by complex multivariate interactions. Computational modeling and artificial intelligence are well-suited for analyzing and modeling large multivariate datasets in short periods of time. Computational approaches can be applied to help design nanomedicine formulations, interpret nanoparticle-biological interactions, and create models from high-throughput screening techniques to improve the selection of the ideal nanoparticle carrier. In the future, many steps in the nanomedicine development process will be done computationally, reducing the number of experiments and time needed to select the ideal nanomedicine formulation.

Site-specific crosslinking and assembly of tetrameric β-glucuronidase improve glycyrrhizin hydrolysis

In this study, eight nonconserved residues with exposed surfaces and flexible conformations of the homotetrameric PGUS (β-glucuronidase from Aspergillus oryzae Li-3) were identified. Single-point mutation into cysteine enabled the thiol-maleimide reaction and site-specific protein assembly using a two-arm polyethylene glycol (PEG)-maleimide crosslinker (Mal2 ). The Mal2 (1k) (with 1 kDa PEG spacer)-crosslinked PGUS assemblies showed low crosslinking efficiency and unimproved thermostability except for G194C-Mal2 (1k). To improve the crosslinking efficiency, a lengthened crosslinker Mal2 (2k) (with 2 kDa PEG spacer) was used to produce PGUS assembly and a highly improved thermostability was achieved with a half-life of 47.2-169.2 min at 70°C, which is 1.04-3.74 times that of wild type PGUS. It is found that the thermostability of PGUS assembly was closely associated with the formation of inter-tetramer assembly and intratetramer crosslinking, rather than the PEGylation of the enzyme. Therefore, the four-arm PEG-maleimide crosslinker Mal4 (2k) (with 2 kDa PEG spacer) was employed to simultaneously increase the inter-tetramer assembly and intratetramer crosslinking, and the resulting PGUS assemblies showed further improved thermostabilities compared with Mal2 (2k)-crosslinked assemblies. Finally, the application of PGUS assemblies with significantly improved thermostability to the bioconversion of GL proved that the PGUS assembly is a strong catalyst for glycyrrhizin (GL) hydrolysis in industrial applications.

Design of a symmetry-broken tetrahedral protein cage by a method of internal steric occlusion

Methods in protein design have made it possible to create large and complex, self-assembling protein cages with diverse applications. These have largely been based on highly symmetric forms exemplified by the Platonic solids. Prospective applications of protein cages would be expanded by strategies for breaking the designed symmetry, e.g., so that only one or a few (instead of many) copies of an exterior domain or motif might be displayed on their surfaces. Here we demonstrate a straightforward design approach for creating symmetry-broken protein cages able to display singular copies of outward-facing domains. We modify the subunit of an otherwise symmetric protein cage through fusion to a small inward-facing domain, only one copy of which can be accommodated in the cage interior. Using biochemical methods and native mass spectrometry, we show that co-expression of the original subunit and the modified subunit, which is further fused to an outward-facing anti-GFP DARPin domain, leads to self-assembly of a protein cage presenting just one copy of the DARPin protein on its exterior. This strategy of designed occlusion provides a facile route for creating new types of protein cages with unique properties.

Multivalent antigen display on nanoparticle immunogens increases B cell clonotype diversity and neutralization breadth to pneumoviruses

Nanoparticles for multivalent display and delivery of vaccine antigens have emerged as a promising avenue for enhancing B cell responses to protein subunit vaccines. Here, we evaluated B cell responses in rhesus macaques immunized with prefusion-stabilized respiratory syncytial virus (RSV) F glycoprotein trimer compared with nanoparticles displaying 10 or 20 copies of the same antigen. We show that multivalent display skews antibody specificities and drives epitope-focusing of responding B cells. Antibody cloning and repertoire sequencing revealed that focusing was driven by the expansion of clonally distinct B cells through recruitment of diverse precursors. We identified two antibody lineages that developed either ultrapotent neutralization or pneumovirus cross-neutralization from precursor B cells with low initial affinity for the RSV-F immunogen. This suggests that increased avidity by multivalent display facilitates the activation and recruitment of these cells. Diversification of the B cell response by multivalent nanoparticle immunogens has broad implications for vaccine design.

A Suite of Designed Protein Cages Using Machine Learning Algorithms and Protein Fragment-Based Protocols

Designed protein cages and related materials provide unique opportunities for applications in biotechnology and medicine, while methods for their creation remain challenging and unpredictable. In the present study, we apply new computational approaches to design a suite of new tetrahedrally symmetric, self-assembling protein cages. For the generation of docked poses, we emphasize a protein fragment-based approach, while for de novo interface design, a comparison of computational protocols highlights the power and increased experimental success achieved using the machine learning program ProteinMPNN. In relating information from docking and design, we observe that agreement between fragment-based sequence preferences and ProteinMPNN sequence inference correlates with experimental success. Additional insights for designing polar interactions are highlighted by experimentally testing larger and more polar interfaces. In all, using X-ray crystallography and cryo-EM, we report five structures for seven protein cages, with atomic resolution in the best case reaching 2.0 Å. We also report structures of two incompletely assembled protein cages, providing unique insights into one type of assembly failure. The new set of designed cages and their structures add substantially to the body of available protein nanoparticles, and to methodologies for their creation.

Nanoparticle display of prefusion coronavirus spike elicits S1-focused cross-reactive antibody response against diverse coronavirus subgenera

Multivalent antigen display is a fast-growing area of interest toward broadly protective vaccines. Current nanoparticle-based vaccine candidates demonstrate the ability to confer antibody-mediated immunity against divergent strains of notably mutable viruses. In coronaviruses, this work is predominantly aimed at targeting conserved epitopes of the receptor binding domain. However, targeting conserved non-RBD epitopes could limit the potential for antigenic escape. To explore new potential targets, we engineered protein nanoparticles displaying coronavirus prefusion-stabilized spike (CoV_S-2P) trimers derived from MERS-CoV, SARS-CoV-1, SARS-CoV-2, hCoV-HKU1, and hCoV-OC43 and assessed their immunogenicity in female mice. Monotypic SARS-1 nanoparticles elicit cross-neutralizing antibodies against MERS-CoV and protect against MERS-CoV challenge. MERS and SARS nanoparticles elicit S1-focused antibodies, revealing a conserved site on the S N-terminal domain. Moreover, mosaic nanoparticles co-displaying distinct CoV_S-2P trimers elicit antibody responses to distant cross-group antigens and protect male and female mice against MERS-CoV challenge. Our findings will inform further efforts toward the development of pan-coronavirus vaccines.

Emerging frontiers in drug delivery with special focus on novel techniques for targeted therapies

The management and treatment of disease are achieved via the use of pharmacologically active substances or drugs. Drugs do not, however, have an intrinsic ability to be effective; rather, how well they work depends on how they are administered or supplied. Treatment of a variety of biological illnesses, such as autoimmune disorders, cancer, and bacterial infections, requires effective drug delivery. Drug absorption, distribution, metabolism, duration of therapeutic impact, pharmacokinetics, excretion, and toxicity can all be impacted by drug administration. Improved chemistry and materials are required for the delivery of therapeutic concentration of novel treatments to the specified targets within the body, as well as for the necessary duration of time. This requirement is accompanied by the development of new therapeutics. Formulating a medication as a DDS is a promising strategy for directly addressing numerous typical barriers to adherence, such as frequent dosage, such as frequent dosage, side effects, and a delayed beginning of the action. In the current review, we give a compendium of drug delivery and controlled release and subsequently highlight some of the newest developments in the realm, with a particular emphasis on cutting-edge methods for targeted therapy. In each instance, we outline the obstacles to efficient drug administration as well as the chemical and material developments that are allowing the sector to overcome these obstacles and have a positive clinical impact.

Quantitation of strain-specific hemagglutinin trimers in mosaic quadrivalent influenza nanoparticle vaccine by ELISA

An enzyme linked immunosorbent assay (ELISA) method was developed to analyze the assembly of a tetravalent mosaic influenza nanoparticle (NP) vaccine, Flumos-v1, consisting of hemagglutinin trimers (HAT) from H1 (A/Idaho/07/2018), H3 (A/Perth/1008/2019), HBV (Vic-B/Colorado/06/2017) and HBY (Yam-B/Phuket/3073/2013) strains. The sandwich ELISA assay used lectin from Galanthus nivalis as a universal capture reagent for all HAT strains and specific monoclonal antibody (mAb) to detect corresponding hemagglutinin antigen. The mAb binding of HATs incorporated into NPs diverged from those for single HAT solutions, resulting in inaccurate quantitation of assembled HATs. An optimized zwittergent treatment was used to fully dissociate the influenza NP and aligned binding activities in each pair of single HAT and dissociated HAT from NP. The dissociated HATs were then quantified against their corresponding HAT standard solutions for three development lots of FluMos-v1 vaccine and the assembly ratio of all four HATs was calculated. The molar ratio of different HATs incorporated into this quadrivalent NP vaccine was consistent and determined as H3:H1: HBV: HBY ∼ 1.00:0.92:0.96:0.87, which was close the expected 1:1:1:1 ratio and confirmed a proper assembling of multivalent NP.

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. ProteinMPNN had a similar success rate to Rosetta, yielding 13 new experimentally confirmed assemblies, but required orders of magnitude less computation and no manual refinement. The interfaces designed by ProteinMPNN were substantially more polar than those designed by Rosetta, which facilitated in vitro assembly of the designed nanomaterials from independently purified components. Crystal structures of several of the assemblies confirmed the accuracy of the design method at high resolution. Our results showcase the potential of deep learning-based methods to unlock the widespread application of designed protein-protein interfaces and self-assembling protein nanomaterials in biotechnology.

De Novo Design of Polyhedral Protein Assemblies: Before and After the AI Revolution

Self-assembling polyhedral protein biomaterials have gained attention as engineering targets owing to their naturally evolved sophisticated functions, ranging from protecting macromolecules from the environment to spatially controlling biochemical reactions. Precise computational design of de novo protein polyhedra is possible through two main types of approaches: methods from first principles, using physical and geometrical rules, and more recent data-driven methods based on artificial intelligence (AI), including deep learning (DL). Here, we retrospect first principle- and AI-based approaches for designing finite polyhedral protein assemblies, as well as advances in the structure prediction of such assemblies. We further highlight the possible applications of these materials and explore how the presented approaches can be combined to overcome current challenges and to advance the design of functional protein-based biomaterials.

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.

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. Here we describe RPXDock, a fast, flexible, and modular software package for sequence-independent rigid-body protein docking across a wide range of symmetric architectures that is easily customizable for further development. RPXDock uses an efficient hierarchical search and a residue-pair transform (RPX) scoring method to rapidly search through multidimensional docking space. We describe the structure of the software, provide practical guidelines for its use, and describe the available functionalities including a variety of score functions and filtering tools that can be used to guide and refine docking results towards desired configurations.

An Ultrastable Self-Assembled Antibacterial Nanospears Made of Protein

Protein-based nanomaterials have broad applications in the biomedical and bionanotechnological sectors owing to their outstanding properties such as high biocompatibility and biodegradability, structural stability, sophisticated functional versatility, and being environmentally benign. They have gained considerable attention in drug delivery, cancer therapeutics, vaccines, immunotherapies, biosensing, and biocatalysis. However, so far, in the battle against the increasing reports of antibiotic resistance and emerging drug-resistant bacteria, unique nanostructures of this kind are lacking, hindering their potential next-generation antibacterial agents. Here, the discovery of a class of supramolecular nanostructures with well-defined shapes, geometries, or architectures (termed "protein nanospears") based on engineered proteins, exhibiting exceptional broad-spectrum antibacterial activities, is reported. The protein nanospears are engineered via spontaneous cleavage-dependent or precisely tunable self-assembly routes using mild metal salt-ions (Mg²⁺ , Ca²⁺ , Na⁺ ) as a molecular trigger. The nanospears' dimensions collectively range from entire nano- to micrometer scale. The protein nanospears display exceptional thermal and chemical stability yet rapidly disassemble upon exposure to high concentrations of chaotropes (>1 mm sodium dodecyl sulfate (SDS)). Using a combination of biological assays and electron microscopy imaging, it is revealed that the nanospears spontaneously induce rapid and irreparable damage to bacterial morphology via a unique action mechanism provided by their nanostructure and enzymatic action, a feat inaccessible to traditional antibiotics. These protein-based nanospears show promise as a potent tool to combat the growing threats of resistant bacteria, inspiring a new way to engineer other antibacterial protein nanomaterials with diverse structural and dimensional architectures and functional properties.

Computational design of non-porous, pH-responsive antibody nanoparticles

Programming protein nanomaterials to respond to changes in environmental conditions is a current challenge for protein design and important for targeted delivery of biologics. We describe the design of octahedral non-porous nanoparticles with the three symmetry axes (four-fold, three-fold, and two-fold) occupied by three distinct protein homooligomers: a de novo designed tetramer, an antibody of interest, and a designed trimer programmed to disassemble below a tunable pH transition point. The nanoparticles assemble cooperatively from independently purified components, and a cryo-EM density map reveals that the structure is very close to the computational design model. The designed nanoparticles can package a variety of molecular payloads, are endocytosed following antibody-mediated targeting of cell surface receptors, and undergo tunable pH-dependent disassembly at pH values ranging between to 5.9-6.7. To our knowledge, these are the first designed nanoparticles with more than two structural components and with finely tunable environmental sensitivity, and they provide new routes to antibody-directed targeted delivery.

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.

Potentiometric sensor based on a computationally designed molecularly imprinted receptor

Molecularly imprinted polymer (MIP)-based polymeric membrane potentiometric sensors are ideal candidates for detection of organic species. The development of such sensors has opened new attractive horizons for potentiometric sensing. However, it should be noted that in the preparation of these MIP receptors, the selection of the functional monomer usually depends on empirical trial- and error-based optimization, which involves tedious and time-consuming experiments. In this work, the computer-aided design and synthesis of an MIP receptor are applied in the fabrication of an MIP-based potentiometric sensor. The density functional theory calculation with the B3LYP model and 6-31G(d) basis set is used to study the interactions between the functional monomer and template molecules. The binding energies of the complexations between the template molecule and different functional monomers are used as a criterion for the selection of the proper monomer. The designed MIP is then synthesized and employed as the receptor for the fabrication of the potentiometric sensor. As a proof-of-concept experiment, the antibiotic sulfadiazine has been selected as a model and 4 functional monomers, 2-hydroxyethyl methacrylate, methyl methacrylate, N-isopropylacrylamide and N-phenylacrylamide, have been chosen. The designed MIP-based sensor exhibits excellent sensitivity with a linear range of 1-10 μM and also shows a good selectivity. We believe that the proposed computer-aided synthesis technique for the MIP receptor selection can provide a general and facile way to replace the traditional empirical MIP preparation method in the fabrication of MIP-based electrochemical and optical sensors.

Design and immunological evaluation of two-component protein nanoparticle vaccines for East Coast fever

Nanoparticle vaccines usually prime stronger immune responses than soluble antigens. Within this class of subunit vaccines, the recent development of computationally designed self-assembling two-component protein nanoparticle scaffolds provides a powerful and versatile platform for displaying multiple copies of one or more antigens. Here we report the generation of three different nanoparticle immunogens displaying 60 copies of p67C, an 80 amino acid polypeptide from a candidate vaccine antigen of Theileria parva, and their immunogenicity in cattle. p67C is a truncation of p67, the major surface protein of the sporozoite stage of T. parva, an apicomplexan parasite that causes an often-fatal bovine disease called East Coast fever (ECF) in sub-Saharan Africa. Compared to I32-19 and I32-28, we found that I53-50 nanoparticle scaffolds displaying p67C had the best biophysical characteristics. p67C-I53-50 also outperformed the other two nanoparticles in stimulating p67C-specific IgG1 and IgG2 antibodies and CD4⁺ T-cell responses, as well as sporozoite neutralizing capacity. In experimental cattle vaccine trials, p67C-I53-50 induced significant immunity to ECF, suggesting that the I53-50 scaffold is a promising candidate for developing novel nanoparticle vaccines. To our knowledge this is the first application of computationally designed nanoparticles to the development of livestock vaccines.

Reversible Assembly of an Artificial Protein Nanocage Using Alkaline Earth Metal Ions

Protein nanocages are of increasing interest for use as drug capsules, but the encapsulation and release of drug molecules at appropriate times require the reversible association and dissociation of the nanocages. One promising approach to addressing this challenge is the design of metal-dependent associating proteins. Such designed proteins typically have Cys or His residues at the protein surface for connecting the associating proteins through metal-ion coordination. However, Cys and His residues favor interactions with soft and borderline metal ions, such as Au⁺ and Zn²⁺, classified by the hard and soft acids and bases concept, restricting the types of metal ions available to drive association. Here, we show the alkaline earth (AE) metal-dependent association of the recently designed artificial protein nanocage TIP60, which is composed of 60-mer fusion proteins. The introduction of a Glu (hard base) mutation to the fusion protein (K67E mutant) prevented the formation of the 60-mer but formed the expected cage structure in the presence of Ca, Sr, or Ba ions (hard acids). Cryogenic electron microscopy (cryo-EM) analysis indicated a Ba ion at the interface of the subunits. Furthermore, we demonstrated the encapsulation and release of single-stranded DNA molecules using this system. Our results provide insights into the design of AE metal-dependent association and dissociation mechanisms for proteins.

Co-display of diverse spike proteins on nanoparticles broadens sarbecovirus neutralizing antibody responses

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants poses continuous challenges in combating the virus. Here, we describe vaccination strategies to broaden SARS-CoV-2 and sarbecovirus immunity by combining spike proteins based on different viruses or viral strains displayed on two-component protein nanoparticles. First, we combined spike proteins based on ancestral and Beta SARS-CoV-2 strains to broaden SARS-CoV-2 immune responses. Inclusion of Beta spike improved neutralizing antibody responses against SARS-CoV-2 Beta, Gamma, and Omicron BA.1 and BA.4/5. A third vaccination with ancestral SARS-CoV-2 spike also improved cross-neutralizing antibody responses against SARS-CoV-2 variants, in particular against the Omicron sublineages. Second, we combined SARS-CoV and SARS-CoV-2 spike proteins to broaden sarbecovirus immune responses. Adding SARS-CoV spike to a SARS-CoV-2 spike vaccine improved neutralizing responses against SARS-CoV and SARS-like bat sarbecoviruses SHC014 and WIV1. These results should inform the development of broadly active SARS-CoV-2 and pan-sarbecovirus vaccines and highlight the versatility of two-component nanoparticles for displaying diverse antigens.

Engineering Self-Assembling Protein Nanoparticles for Therapeutic Delivery

Despite remarkable advances over the past several decades, many therapeutic nanomaterials fail to overcome major in vivo delivery barriers. Controlling immunogenicity, optimizing biodistribution, and engineering environmental responsiveness are key outstanding delivery problems for most nanotherapeutics. However, notable exceptions exist including some lipid and polymeric nanoparticles, some virus-based nanoparticles, and nanoparticle vaccines where immunogenicity is desired. Self-assembling protein nanoparticles offer a powerful blend of modularity and precise designability to the field, and have the potential to solve many of the major barriers to delivery. In this review, we provide a brief overview of key designable features of protein nanoparticles and their implications for therapeutic delivery applications. We anticipate that protein nanoparticles will rapidly grow in their prevalence and impact as clinically relevant delivery platforms.

Shape-Morphing of an Artificial Protein Cage with Unusual Geometry Induced by a Single Amino Acid Change

Artificial protein cages are constructed from multiple protein subunits. The interaction between the subunits, notably the angle formed between them, controls the geometry of the resulting cage. Here, using the artificial protein cage, “TRAP-cage”, we show that a simple alteration in the position of a single amino acid responsible for Au(I)-mediated subunit–subunit interactions in the constituent ring-shaped building blocks results in a more acute dihedral angle between them. In turn, this causes a dramatic shift in the structure from a 24-ring cage with an octahedral symmetry to a 20-ring cage with a C2 symmetry. This symmetry change is accompanied by a decrease in the number of Au(I)-mediated bonds between cysteines and a concomitant change in biophysical properties of the cage.

Protein Cages: From Fundamentals to Advanced Applications

Proteins that self-assemble into polyhedral shell-like structures are useful molecular containers both in nature and in the laboratory. Here we review efforts to repurpose diverse protein cages, including viral capsids, ferritins, bacterial microcompartments, and designed capsules, as vaccines, drug delivery vehicles, targeted imaging agents, nanoreactors, templates for controlled materials synthesis, building blocks for higher-order architectures, and more. A deep understanding of the principles underlying the construction, function, and evolution of natural systems has been key to tailoring selective cargo encapsulation and interactions with both biological systems and synthetic materials through protein engineering and directed evolution. The ability to adapt and design increasingly sophisticated capsid structures and functions stands to benefit the fields of catalysis, materials science, and medicine.

Virus-like particles against infectious disease and cancer: guidance for the nano-architect

Virus-like particles (VLPs) can play important roles in prevention and therapy for infectious diseases and cancer. Here we describe recent advances in rational construction of VLP assemblies, as well as new approaches to enhance long-lasting antibody and CD8⁺ T cell responses. DNA origami and computational protein design identified optimal spacing of antigens. Chemical biology advances enabled simple and irreversible VLP decoration with protein or polysaccharide antigens. Mosaic VLPs co-displayed antigens to generate cross-reactive antibodies against different influenza strains and coronaviruses. The mode of action of adjuvants inside VLPs was established through knock-outs and repackaging of innate immune stimuli. VLPs themselves showed their power as adjuvants in cancer models. Finally, landmark clinical results were obtained against malaria and the SARS-CoV-2 pandemic.

Supramolecular assembly of protein building blocks: from folding to function

Several phenomena occurring throughout the life of living things start and end with proteins. Various proteins form one complex structure to control detailed reactions. In contrast, one protein forms various structures and implements other biological phenomena depending on the situation. The basic principle that forms these hierarchical structures is protein self-assembly. A single building block is sufficient to create homogeneous structures with complex shapes, such as rings, filaments, or containers. These assemblies are widely used in biology as they enable multivalent binding, ultra-sensitive regulation, and compartmentalization. Moreover, with advances in the computational design of protein folding and protein-protein interfaces, considerable progress has recently been made in the de novo design of protein assemblies. Our review presents a description of the components of supramolecular protein assembly and their application in understanding biological phenomena to therapeutics.

Protein Assembly by Design

Proteins are nature's primary building blocks for the construction of sophisticated molecular machines and dynamic materials, ranging from protein complexes such as photosystem II and nitrogenase that drive biogeochemical cycles to cytoskeletal assemblies and muscle fibers for motion. Such natural systems have inspired extensive efforts in the rational design of artificial protein assemblies in the last two decades. As molecular building blocks, proteins are highly complex, in terms of both their three-dimensional structures and chemical compositions. To enable control over the self-assembly of such complex molecules, scientists have devised many creative strategies by combining tools and principles of experimental and computational biophysics, supramolecular chemistry, inorganic chemistry, materials science, and polymer chemistry, among others. Owing to these innovative strategies, what started as a purely structure-building exercise two decades ago has, in short order, led to artificial protein assemblies with unprecedented structures and functions and protein-based materials with unusual properties. Our goal in this review is to give an overview of this exciting and highly interdisciplinary area of research, first outlining the design strategies and tools that have been devised for controlling protein self-assembly, then describing the diverse structures of artificial protein assemblies, and finally highlighting the emergent properties and functions of these assemblies.

Single-particle mass analysis of intact ribosomes by mass photometry and Orbitrap-based charge detection mass spectrometry

Standard methods for mass analysis measure ensembles of thousand to millions of molecules. This approach enables analysis of monodisperse recombinant proteins, whereas some heterogeneous protein assemblies pose a significant challenge, whereby co-occurring stoichiometries, sub-complexes, and modifications hamper analysis using native mass spectrometry. To tackle the challenges posed by mass heterogeneity, single-particle methods may come to the rescue. Recently, two such approaches have been introduced, namely, mass photometry (MP) and Orbitrap-based charge detection mass spectrometry (CDMS). Both methods assess masses of individual molecules, albeit adhering to distinct physical principles. To evaluate these methods side by side, we analyzed a set of ribosomal particles, representing polydisperse ribonucleoprotein assemblies in the MDa range. MP and CDMS provide accurate masses for intact ribosomes and enable quantitative analysis of concomitant distinct particles within each ribosome sample. Here, we discuss pros and cons of these single-molecule techniques, also in the context of other techniques used for mass analysis.

High-Resolution Native Mass Spectrometry

Native mass spectrometry (MS) involves the analysis and characterization of macromolecules, predominantly intact proteins and protein complexes, whereby as much as possible the native structural features of the analytes are retained. As such, native MS enables the study of secondary, tertiary, and even quaternary structure of proteins and other biomolecules. Native MS represents a relatively recent addition to the analytical toolbox of mass spectrometry and has over the past decade experienced immense growth, especially in enhancing sensitivity and resolving power but also in ease of use. With the advent of dedicated mass analyzers, sample preparation and separation approaches, targeted fragmentation techniques, and software solutions, the number of practitioners and novel applications has risen in both academia and industry. This review focuses on recent developments, particularly in high-resolution native MS, describing applications in the structural analysis of protein assemblies, proteoform profiling of—among others—biopharmaceuticals and plasma proteins, and quantitative and qualitative analysis of protein–ligand interactions, with the latter covering lipid, drug, and carbohydrate molecules, to name a few.

Structure-based design of novel polyhedral protein nanomaterials

Organizing matter at the atomic scale is a central goal of nanotechnology. Bottom-up approaches, in which molecular building blocks are programmed to assemble via supramolecular interactions, are a proven and versatile route to new and useful nanomaterials. Although a wide variety of molecules have been used as building blocks, proteins have several intrinsic features that present unique opportunities for designing nanomaterials with sophisticated functions. There has been tremendous recent progress in designing proteins to fold and assemble to highly ordered structures. Here we review the leading approaches to the design of closed polyhedral protein assemblies, highlight the importance of considering the assembly process itself, and discuss various applications and future directions for the field. We emphasize throughout the exciting opportunities presented by recent advances as well as challenges that remain.