Biology subjects

Borst, A. J.

Publications and source records attributed to Borst, A. J..

30 records · Page 2Linked to original sources

bioRxiv2023

Local structural flexibility drives oligomorphism in computationally designed protein assemblies

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

bioengineering↗

bioRxiv2023

Design of four component T=4 tetrahedral, octahedral, and icosahedral protein nanocages through programmed symmetry breaking

Four, eight or twenty C3 symmetric protein trimers can be arranged with tetrahedral (T-sym), octahedral (O-sym) or icosahedral (I-sym) point group symmetry to generate closed cage-like structures1,2. Generating more complex closed structures requires breaking perfect point group symmetry. Viruses do this in the icosahedral case using quasi-symmetry or pseudo-symmetry to access higher triangulation number architectures3-9, but nature appears not to have explored higher triangulation number tetrahedral or octahedral symmetries. Here, we describe a general design strategy for building T = 4 architectures starting from simpler T = 1 structures through pseudo-symmetrization of trimeric building blocks. Electron microscopy confirms the structures of T = 4 cages with 48 (T-sym), 96 (O-sym), and 240 (I-sym) subunits, each with four distinct chains and six different protein-protein interfaces, and diameters of 33nm, 43nm, and 75nm, respectively. Higher triangulation number viruses possess very sophisticated functionalities; our general route to higher triangulation number nanocages should similarly enable a next generation of multiple antigen displaying vaccine candidates10,11 and targeted delivery vehicles12,13.

biochemistry↗

bioRxiv2023

Blueprinting expandable nanomaterials with standardized protein building blocks

A wooden house frame consists of many different lumber pieces, but because of the regularity of these building blocks, the structure can be designed using straightforward geometrical principles. The design of multicomponent protein assemblies in comparison has been much more complex, largely due to the irregular shapes of protein structures1. Here we describe extendable linear, curved, and angled protein building blocks, as well as inter-block interactions that conform to specified geometric standards; assemblies designed using these blocks inherit their extendability and regular interaction surfaces, enabling them to be expanded or contracted by varying the number of modules, and reinforced with secondary struts. Using X-ray crystallography and electron microscopy, we validate nanomaterial designs ranging from simple polygonal and circular oligomers that can be concentrically nested, up to large polyhedral nanocages and unbounded straight "train track" assemblies with reconfigurable sizes and geometries that can be readily blueprinted. Because of the complexity of protein structures and sequence-structure relationships, it has not been previously possible to build up large protein assemblies by deliberate placement of protein backbones onto a blank 3D canvas; the simplicity and geometric regularity of our design platform now enables construction of protein nanomaterials according to "back of an envelope" architectural blueprints.

biochemistry↗

bioRxiv2023

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.

bioengineering↗

bioRxiv2022

Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models

There has been considerable recent progress in designing new proteins using deep learning methods1-9. Despite this progress, a general deep learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher order symmetric architectures, has yet to be described. Diffusion models10,11 have had considerable success in image and language generative modeling but limited success when applied to protein modeling, likely due to the complexity of protein backbone geometry and sequence-structure relationships. Here we show that by fine tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding, and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold Diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of new designs. In a manner analogous to networks which produce images from user-specified inputs, RFdiffusion enables the design of diverse, complex, functional proteins from simple molecular specifications.

biochemistry↗

bioRxiv2022

Accurate Computational Design of 3D Protein Crystals

Protein crystallization plays a central role in structural biology1, with broad impact2 in pharmaceutical formulation3, drug delivery4, biosensing5, and biocatalysis6,7. Despite this importance, the process of protein crystallization remains poorly understood and highly empirical8-10, with largely unpredictable crystal contacts, lattice packing arrangements, and space group preferences, and the programming of protein crystallization through precisely engineered sidechain-sidechain interactions across multiple protein-protein interfaces is an outstanding challenge. Here we develop a general computational approach to designing three-dimensional (3D) protein crystals with pre-specified lattice architectures at atomic accuracy that hierarchically constrains the overall degree of freedoms (DOFs) of the system. We use the approach to design three pairs of oligomers that can be individually purified, and upon mixing, spontaneously self-assemble into large 3D crystals (>100 m). Small-angle X-ray scattering and X-ray crystallography show these crystals are nearly identical to the computational design models, with the design target F4132 and I432 space groups and closely corresponding overall architectures and protein-protein interfaces. The crystal unit cell dimensions can be systematically redesigned while retaining space group symmetry and overall architecture, and the crystals are both extremely porous and highly stable, enabling the robust scaffolding of inorganic nanoparticle arrays. Our approach thus enables the computational design of protein crystals with high accuracy, and since both structure and assembly are encoded in the primary sequence, provides a powerful new platform for biological material engineering.

synthetic biology↗

bioRxiv2022

Precisely patterned nanofibers made from extendable protein multiplexes

Molecular systems with coincident cyclic and superhelical symmetry axes have considerable advantages for materials design as they can be readily lengthened or shortened by changing the length of the constituent monomers. Among proteins, alpha helical coiled coils have such symmetric extendable architectures, but are limited by the relatively fixed geometry and flexibility of the helical protomers. Here, we describe a systematic approach to generating modular and rigid repeat protein oligomers with coincident C2 to C8 and superhelical symmetry axes that can be readily extended by repeat propagation. From these building blocks, we demonstrate that a wide range of unbounded fibers can be systematically designed by introducing hydrophilic surface patches that force staggering of the monomers; the geometry of such fibers can be precisely tuned by varying the number of repeat units in the monomer and the placement of the hydrophilic patches.

bioengineering↗

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↗

bioRxiv2022

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. 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 novel nanoparticles we describe may be broadly useful in biotechnological applications.

biochemistry↗

bioRxiv2022

Immunization with a self-assembling nanoparticle vaccine displaying EBV gH/gL protects humanized mice against lethal viral challenge

Summary/AbstractEpstein-Barr virus (EBV) is a cancer-associated pathogen responsible for 140,000 deaths per year. EBV is also the etiological agent of infectious mononucleosis and is associated with multiple sclerosis and rheumatoid arthritis. Thus, an EBV vaccine could alleviate significant morbidity and mortality. EBV is orally transmitted and has tropism for both epithelial cells and B cells which are present in the oral cavity. Therefore, a prophylactic vaccine would need to prevent infection of both cell types. Passive transfer neutralizing monoclonal antibodies targeting the viral gH/gL glycoprotein complex prevent experimental EBV infection in humanized mice and rhesus macaques, suggesting that gH/gL is an attractive vaccine candidate. Here, we produced and evaluated the immunogenicity of several nanoparticle immunogens displaying gH/gL with distinct valencies and geometries. After one or two immunizations, all nanoparticles elicited superior binding and neutralizing titers relative to monomeric gH/gL. Antibodies elicited by a computationally designed self-assembling nanoparticle that displays 60 copies of the gH/gL protein conferred protection against a lethal dose of EBV in a humanized mouse challenge model, whereas antibodies elicited by monomeric gH/gL did not. Taken together, these data motivate further development of gH/gL nanoparticle vaccine candidates for EBV.

immunology↗

bioRxiv2021

Structure-based design of stabilized recombinant influenza neuraminidase tetramers

Influenza virus neuraminidase (NA) is a major antiviral drug target and has recently reemerged as a key target of antibody-mediated protective immunity. Here we show that recombinant NAs across all non-bat subtypes adopt various tetrameric conformations, including a previously unreported "open" state that may help explain poorly understood variations in NA stability across viral strains and subtypes. We used homology-directed protein design to uncover the structural principles underlying these distinct tetrameric conformations and stabilize multiple recombinant NAs in the "closed" state. In addition to improving thermal stability, conformational stabilization improved affinity to protective antibodies elicited by viral infection, including antibodies targeting a quaternary epitope and the broadly conserved catalytic site. The stabilized NA proteins can also be integrated into viruses without affecting fitness. Our findings provide a deeper understanding of NA structure, stability, and antigenicity, as well as a roadmap towards structure-based discovery of NA-directed therapeutics and vaccines.

microbiology↗

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↗