Structure-based design of novel polyhedral 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. 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 protein design-From new structures to programmable functions

Methods from artificial intelligence (AI) trained on large datasets of sequences and structures can now "write" proteins with new shapes and molecular functions de novo, without starting from proteins found in nature. In this Perspective, I will discuss the state of the field of de novo protein design at the juncture of physics-based modeling approaches and AI. New protein folds and higher-order assemblies can be designed with considerable experimental success rates, and difficult problems requiring tunable control over protein conformations and precise shape complementarity for molecular recognition are coming into reach. Emerging approaches incorporate engineering principles-tunability, controllability, and modularity-into the design process from the beginning. Exciting frontiers lie in deconstructing cellular functions with de novo proteins and, conversely, constructing synthetic cellular signaling from the ground up. As methods improve, many more challenges are unsolved.

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.

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.

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.

Self-assembling nanocarriers from engineered proteins: Design, functionalization, and application for drug delivery

Self-assembling proteins are valuable building blocks for constructing drug nanocarriers due to their self-assembly behavior, monodispersity, biocompatibility, and biodegradability. Genetic and chemical modifications allow for modular design of protein nanocarriers with effective drug encapsulation, targetability, stimuli responsiveness, and in vivo half-life. Protein nanocarriers have been developed to deliver various therapeutic molecules including small molecules, proteins, and nucleic acids with proven in vitro and in vivo efficacy. This article reviews recent advances in protein nanocarriers that are not derived from natural protein nanostructures, such as protein cages or virus like particles. The protein nanocarriers described here are self-assembled from rationally or de novo designed recombinant proteins, as well as recombinant proteins complexed with other biomolecules, presenting properties that are unique from those of natural protein carriers. Design, functionalization, and therapeutic application of protein nanocarriers will be discussed.

Probing the Internal pH and Permeability of a Carboxysome Shell

The carboxysome is a protein-based nanoscale organelle in cyanobacteria and many proteobacteria, which encapsulates the key CO₂-fixing enzymes ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) and carbonic anhydrase (CA) within a polyhedral protein shell. The intrinsic self-assembly and architectural features of carboxysomes and the semipermeability of the protein shell provide the foundation for the accumulation of CO₂ within carboxysomes and enhanced carboxylation. Here, we develop an approach to determine the interior pH conditions and inorganic carbon accumulation within an α-carboxysome shell derived from a chemoautotrophic proteobacterium Halothiobacillus neapolitanus and evaluate the shell permeability. By incorporating a pH reporter, pHluorin2, within empty α-carboxysome shells produced in Escherichia coli, we probe the interior pH of the protein shells with and without CA. Our in vivo and in vitro results demonstrate a lower interior pH of α-carboxysome shells than the cytoplasmic pH and buffer pH, as well as the modulation of the interior pH in response to changes in external environments, indicating the shell permeability to bicarbonate ions and protons. We further determine the saturated HCO₃^– concentration of 15 mM within α-carboxysome shells and show the CA-mediated increase in the interior CO₂ level. Uncovering the interior physiochemical microenvironment of carboxysomes is crucial for understanding the mechanisms underlying carboxysomal shell permeability and enhancement of Rubisco carboxylation within carboxysomes. Such fundamental knowledge may inform reprogramming carboxysomes to improve metabolism and recruit foreign enzymes for enhanced catalytical performance.

Hybrid Macrocyclic Polymers: Self-Assembly Containing Cucurbit[m]uril-pillar[n]arene

Supramolecular self-assembly by hybrid macrocycles containing both cucurbit[m]uril (CB[m]) and pillar[n]arene was discussed and summarized in this review. Due to different solubility, diverse-sized cavities, and various driving forces in recognizing guests, the role of CB[m] and pillar[n]arene in such hybrid macrocyclic systems could switch between competitor in capturing specialized guests, and cooperator for building advanced hybridized macrocycles, by controlling their characteristics in host–guest inclusions. Furthermore, both CB[m] and pillar[n]arene were employed for fabricating advanced supramolecular self-assemblies such as mechanically interlocked molecules and supramolecular polymers. In those self-assemblies, CB[m] and pillar[n]arene played significant roles in, e.g., microreactor for catalyzing particular reactions to bridge different small pieces together, molecular “joint” to connect different monomers into larger assemblies, and “stabilizer” in accommodating the guest molecules to adopt a favorite structure geometry ready for assembling.

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.

Alcohol-perturbed self-assembly of the tobacco mosaic virus coat protein

The self-assembly of the tobacco mosaic virus coat protein is significantly altered in alcohol-water mixtures. Alcohol cosolvents stabilize the disk aggregate and prevent the formation of helical rods at low pH. A high alcohol content favours stacked disk assemblies and large rafts, while a low alcohol concentration favours individual disks and short stacks. These effects appear to be caused by the hydrophobicity of the alcohol additive, with isopropyl alcohol having the strongest effect and methanol the weakest. We discuss several effects that may contribute to preventing the protein-protein interactions between disks that are necessary to form helical rods.

Computational Design of Single-Peptide Nanocages with Nanoparticle Templating

Protein complexes perform a diversity of functions in natural biological systems. While computational protein design has enabled the development of symmetric protein complexes with spherical shapes and hollow interiors, the individual subunits often comprise large proteins. Peptides have also been applied to self-assembly, and it is of interest to explore such short sequences as building blocks of large, designed complexes. Coiled-coil peptides are promising subunits as they have a symmetric structure that can undergo further assembly. Here, an α-helical 29-residue peptide that forms a tetrameric coiled coil was computationally designed to assemble into a spherical cage that is approximately 9 nm in diameter and presents an interior cavity. The assembly comprises 48 copies of the designed peptide sequence. The design strategy allowed breaking the side chain conformational symmetry within the peptide dimer that formed the building block (asymmetric unit) of the cage. Dynamic light scattering (DLS) and transmission electron microscopy (TEM) techniques showed that one of the seven designed peptide candidates assembled into individual nanocages of the size and shape. The stability of assembled nanocages was found to be sensitive to the assembly pathway and final solution conditions (pH and ionic strength). The nanocages templated the growth of size-specific Au nanoparticles. The computational design serves to illustrate the possibility of designing target assemblies with pre-determined specific dimensions using short, modular coiled-coil forming peptide sequences.

Bioengineering bacterial encapsulin nanocompartments as targeted drug delivery system

The development of Drug Delivery Systems (DDS) has led to increasingly efficient therapies for the treatment and detection of various diseases. DDS use a range of nanoscale delivery platforms produced from polymeric of inorganic materials, such as micelles, and metal and polymeric nanoparticles, but their variant chemical composition make alterations to their size, shape, or structures inherently complex. Genetically encoded protein nanocages are highly promising DDS candidates because of their modular composition, ease of recombinant production in a range of hosts, control over assembly and loading of cargo molecules and biodegradability. One example of naturally occurring nanocompartments are encapsulins, recently discovered bacterial organelles that have been shown to be reprogrammable as nanobioreactors and vaccine candidates. Here we report the design and application of a targeted DDS platform based on the Thermotoga maritima encapsulin reprogrammed to display an antibody mimic protein called Designed Ankyrin repeat protein (DARPin) on the outer surface and to encapsulate a cytotoxic payload. The DARPin9.29 chosen in this study specifically binds to human epidermal growth factor receptor 2 (HER2) on breast cancer cells, as demonstrated in an in vitro cell culture model. The encapsulin-based DDS is assembled in one step in vivo by co-expressing the encapsulin-DARPin9.29 fusion protein with an engineered flavin-binding protein mini-singlet oxygen generator (MiniSOG), from a single plasmid in Escherichia coli. Purified encapsulin-DARPin_miniSOG nanocompartments bind specifically to HER2 positive breast cancer cells and trigger apoptosis, indicating that the system is functional and specific. The DDS is modular and has the potential to form the basis of a multi-receptor targeted system by utilising the DARPin screening libraries, allowing use of new DARPins of known specificities, and through the proven flexibility of the encapsulin cargo loading mechanism, allowing selection of cargo proteins of choice.