De novo design of self-assembling helical protein filaments

Supramolecular rosette intermediated homochiral double helix

Precise organization of organic molecules into homochiral double-helix remains a challenge due to the difficulty in controlling both self-assembly process and chirality transfer across length scales. Here, we report that a type of bisnaphthalene bisurea molecule could assemble into chirality-controlled nanoscale double-helices by a supramolecular rosette-intermediated hierarchical self-assembly mechanism. A solvent-mixing self-assembly protocol is adopted to direct bisnaphthalene bisurea cyclization into chiral discrete rosettes through cooperative intramolecular and intermolecular hydrogen bonds. Controlled hexagonal packing of rosettes at higher concentrations gives one-dimensional single-stranded nanofibers, which intertwine into well-defined double-helix nanostructures with preferred chirality that depends on the absolute configurations of bisnaphthalene bisurea. The hierarchical organization of bisnaphthalene bisurea molecules enables effective excitation energy delocalization within the double-helix, which contributes to near-unity energy transfer from double-helix to adsorbed acceptor dyes even in donor/acceptor ratios over 1000, leading to bright circularly polarized luminescence from the originally achiral acceptor. The experimental and theoretical simulation results not only provide a hierarchical strategy to fabricate homochiral double-helix but also bring insights in understanding the high-efficiency light-harvesting process in photosystem II.

Membraneless Compartmentalization of Cell-Free Transcription-Translation by Polymer-Assisted Liquid-Liquid Phase Separation

Living cells use liquid-liquid phase separation (LLPS) to compartmentalize metabolic functions into mesoscopic-sized droplets. Deciphering the mechanisms at play in LLPS is therefore critical to understanding the structuration and functions of cells at the subcellular level. Although observed and achieved to a significant degree of control in vivo, the reconstitution of LLPS integrating advanced biological functions, such as gene expression, has been so far limited in vitro. LLPS of cell-free transcription-translation (TXTL) reactions require multi-step experimental approaches that lack biomimetic and have relatively poor efficacy, thus limiting their usage in cell-free engineered systems such as synthetic cells. Here the polymer-assisted LLPS of TXTL reactions are reported as the single-pot one-step compartmentalization of a model complex metabolic system obtain without using solvents or surfactants. LLPS occurs by adding the biocompatible polymers poly(ethylene glycol), poly(vinyl alcohol), and dextran to a TXTL reaction, that remains highly active. These polymers serve as partitioning agents that localize TXTL in mesoscopic-sized droplets rich in dextran. Cytoplasmic and membrane-interacting proteins are synthesized preferentially inside these droplets, and localize either uniformly or preferentially at the interface, depending on their nature. The LLPS-TXTL system presented in this work is a step toward the design of synthetic membraneless active organelles.

ProBID-Net: a deep learning model for protein-protein binding interface design

Protein-protein interactions are pivotal in numerous biological processes. The computational design of these interactions facilitates the creation of novel binding proteins, crucial for advancing biopharmaceutical products. With the evolution of artificial intelligence (AI), protein design tools have swiftly transitioned from scoring-function-based to AI-based models. However, many AI models for protein design are constrained by assuming complete unfamiliarity with the amino acid sequence of the input protein, a feature most suited for de novo design but posing challenges in designing protein-protein interactions when the receptor sequence is known. To bridge this gap in computational protein design, we introduce ProBID-Net. Trained using natural protein-protein complex structures and protein domain-domain interface structures, ProBID-Net can discern features from known target protein structures to design specific binding proteins based on their binding sites. In independent tests, ProBID-Net achieved interface sequence recovery rates of 52.7%, 43.9%, and 37.6%, surpassing or being on par with ProteinMPNN in binding protein design. Validated using AlphaFold-Multimer, the sequences designed by ProBID-Net demonstrated a close correspondence between the design target and the predicted structure. Moreover, the model's output can predict changes in binding affinity upon mutations in protein complexes, even in scenarios where no data on such mutations were provided during training (zero-shot prediction). In summary, the ProBID-Net model is poised to significantly advance the design of protein-protein interactions.

Liquid Phase Exfoliation of Protein Parent Crystals into Nanosheets and Fibrils Based on Orthogonal Supramolecular Interactions

Proteins are attractive building blocks for fabricating diverse and precise nanomaterials. However, the facile fabrication of multidimensional artificial assemblies is highly challenging. Here, inspired by the large-scale production technique of inorganic nanomaterials, we demonstrate the application of liquid phase exfoliation (LPE) on native protein ConA by the design of synthetic ligands. These ligands provide distinct in-plane and out-of-plane supramolecular interactions, allowing the generation of multidimensional architectures based on the same protein by dissociating a single interaction in solution, including 3D porous protein crystals, 2D sizable nanosheets, and 1D fibrils. Importantly, the exfoliated 2D sheets were dozens of times larger than the self-assembled nanosheets, resulting in a dramatic enhancement of the intrinsic bioactivity of the building blocks by receptor clustering and less endocytosis. These findings enable the successful application of LPE on biomacromolecules and open up an alternative avenue to generate advanced multidimensional nanomaterials, without the need for complex protein design and careful adjustment of self-assembly conditions.

Nucleation limited assembly and polarized growth of a de novo-designed allosterically modulatable protein filament

The design of inducibly assembling protein nanomaterials is an outstanding challenge. Here, we describe the computational design of a protein filament formed from a monomeric subunit which binds a peptide ligand. The cryoEM structure of the micron scale fibers is very close to the computational design model. The ligand acts as a tunable allosteric modulator: while not part of the fiber subunit-subunit interfaces, the assembly of the filament is dependent on ligand addition, with longer peptides having more extensive interaction surfaces with the monomer promoting more rapid growth. Seeded growth and capping experiments reveal that the filaments grow primarily from one end. Oligomers containing 12 copies of the peptide ligand nucleate fiber assembly from monomeric subunit and peptide mixtures at concentrations where assembly occurs very slowly, likely by generating critical local concentrations of monomer in the assembly competent conformation. Following filament assembly, the peptide ligand can be exchanged with free peptide in solution, and it can be readily fused to any functional protein of interest, opening the door to a wide variety of tunable engineered materials.

Generative β-hairpin design using a residue-based physicochemical property landscape

De novo peptide design is a new frontier that has broad application potential in the biological and biomedical fields. Most existing models for de novo peptide design are largely based on sequence homology that can be restricted based on evolutionarily derived protein sequences and lack the physicochemical context essential in protein folding. Generative machine learning for de novo peptide design is a promising way to synthesize theoretical data that are based on, but unique from, the observable universe. In this study, we created and tested a custom peptide generative adversarial network intended to design peptide sequences that can fold into the β-hairpin secondary structure. This deep neural network model is designed to establish a preliminary foundation of the generative approach based on physicochemical and conformational properties of 20 canonical amino acids, for example, hydrophobicity and residue volume, using extant structure-specific sequence data from the PDB. The beta generative adversarial network model robustly distinguishes secondary structures of β hairpin from α helix and intrinsically disordered peptides with an accuracy of up to 96% and generates artificial β-hairpin peptide sequences with minimum sequence identities around 31% and 50% when compared against the current NCBI PDB and nonredundant databases, respectively. These results highlight the potential of generative models specifically anchored by physicochemical and conformational property features of amino acids to expand the sequence-to-structure landscape of proteins beyond evolutionary limits.

De novo design of allosterically switchable protein assemblies

Allosteric modulation of protein function, wherein the binding of an effector to a protein triggers conformational changes at distant functional sites, plays a central part in the control of metabolism and cell signalling1-3. There has been considerable interest in designing allosteric systems, both to gain insight into the mechanisms underlying such 'action at a distance' modulation and to create synthetic proteins whose functions can be regulated by effectors4-7. However, emulating the subtle conformational changes distributed across many residues, characteristic of natural allosteric proteins, is a significant challenge8,9. Here, inspired by the classic Monod-Wyman-Changeux model of cooperativity10, we investigate the de novo design of allostery through rigid-body coupling of peptide-switchable hinge modules11 to protein interfaces12 that direct the formation of alternative oligomeric states. We find that this approach can be used to generate a wide variety of allosterically switchable systems, including cyclic rings that incorporate or eject subunits in response to peptide binding and dihedral cages that undergo effector-induced disassembly. Size-exclusion chromatography, mass photometry13 and electron microscopy reveal that these designed allosteric protein assemblies closely resemble the design models in both the presence and absence of peptide effectors and can have ligand-binding cooperativity comparable to classic natural systems such as haemoglobin14. Our results indicate that allostery can arise from global coupling of the energetics of protein substructures without optimized side-chain-side-chain allosteric communication pathways and provide a roadmap for generating allosterically triggerable delivery systems, protein nanomachines and cellular feedback control circuitry.

Symmetry-Guided Inverse Design of Self-Assembling Multiscale DNA Origami Tilings

Recent advances enable the creation of nanoscale building blocks with complex geometries and interaction specificities for self-assembly. This nearly boundless design space necessitates design principles for defining the mutual interactions between multiple particle species to target a user-specified complex structure or pattern. In this article, we develop a symmetry-based method to generate the interaction matrices that specify the assembly of two-dimensional tilings, which we illustrate using equilateral triangles. By exploiting the allowed 2D symmetries, we develop an algorithmic approach by which any periodic 2D tiling can be generated from an arbitrarily large number of subunit species, notably addressing an unmet challenge of engineering 2D crystals with periodicities that can be arbitrarily larger than the subunit size. To demonstrate the utility of our design approach, we encode specific interactions between triangular subunits synthesized by DNA origami and show that we can guide their self-assembly into tilings with a wide variety of symmetries, using up to 12 unique species of triangles. By conjugating specific triangles with gold nanoparticles, we fabricate gold-nanoparticle supracrystals whose lattice parameter spans up to 300 nm. Finally, to generate economical design rules, we compare the design economy of various tilings. In particular, we show that (1) higher symmetries allow assembly of larger unit cells with fewer subunits and (2) linear supracrystals can be designed more economically using linear primitive unit cells. This work provides a simple algorithmic approach to designing periodic assemblies, aiding in the multiscale assembly of supracrystals of nanostructured "meta-atoms" with engineered plasmonic functions.

Smart Molecular Imaging and Theranostic Probes by Enzymatic Molecular In Situ Self-Assembly

Enzymatic molecular in situ self-assembly (E-MISA) that enables the synthesis of high-order nanostructures from synthetic small molecules inside a living subject has emerged as a promising strategy for molecular imaging and theranostics. This strategy leverages the catalytic activity of an enzyme to trigger probe substrate conversion and assembly in situ, permitting prolonging retention and congregating many molecules of probes in the targeted cells or tissues. Enhanced imaging signals or therapeutic functions can be achieved by responding to a specific enzyme. This E-MISA strategy has been successfully applied for the development of enzyme-activated smart molecular imaging or theranostic probes for in vivo applications. In this Perspective, we discuss the general principle of controlling in situ self-assembly of synthetic small molecules by an enzyme and then discuss the applications for the construction of "smart" imaging and theranostic probes against cancers and bacteria. Finally, we discuss the current challenges and perspectives in utilizing the E-MISA strategy for disease diagnoses and therapies, particularly for clinical translation.

De novo design of pH-responsive self-assembling helical protein filaments

Biological evolution has led to precise and dynamic nanostructures that reconfigure in response to pH and other environmental conditions. However, designing micrometre-scale protein nanostructures that are environmentally responsive remains a challenge. Here we describe the de novo design of pH-responsive protein filaments built from subunits containing six or nine buried histidine residues that assemble into micrometre-scale, well-ordered fibres at neutral pH. The cryogenic electron microscopy structure of an optimized design is nearly identical to the computational design model for both the subunit internal geometry and the subunit packing into the fibre. Electron, fluorescent and atomic force microscopy characterization reveal a sharp and reversible transition from assembled to disassembled fibres over 0.3 pH units, and rapid fibre disassembly in less than 1 s following a drop in pH. The midpoint of the transition can be tuned by modulating buried histidine-containing hydrogen bond networks. Computational protein design thus provides a route to creating unbound nanomaterials that rapidly respond to small pH changes.

Next-Generation Vaccine Development with Nanomaterials: Recent Advances, Possibilities, and Challenges

Nanomaterials are becoming important tools for vaccine development owing to their tunable and adaptable nature. Unique properties of nanomaterials afford opportunities to modulate trafficking through various tissues, complement or augment adjuvant activities, and specify antigen valency and display. This versatility has enabled recent work designing nanomaterial vaccines for a broad range of diseases, including cancer, inflammatory diseases, and various infectious diseases. Recent successes of nanoparticle vaccines during the coronavirus disease 2019 (COVID-19) pandemic have fueled enthusiasm further. In this review, the most recent developments in nanovaccines for infectious disease, cancer, inflammatory diseases, allergic diseases, and nanoadjuvants are summarized. Additionally, challenges and opportunities for clinical translation of this unique class of materials are discussed.

DNA-Nanostructure-Guided Assembly of Proteins into Programmable Shapes

The development of methods to synthesize artificial protein complexes with precisely controlled configurations will enable diverse biological and medical applications. Using DNA to link proteins provides programmability that can be difficult to achieve with other methods. Here, we use DNA origami as an "assembler" to guide the linking of protein-DNA conjugates using a series of oligonucleotide hybridization and displacement operations. We constructed several isomeric protein nanostructures, including a dimer, two types of trimer structures, and three types of tetramer assemblies, on a DNA origami platform by using a C3-symmetric building block composed of a protein trimer modified with DNA handles. Our approach expands the scope for the precise assembly of protein-based nanostructures and will enable the formulation of functional protein complexes with stoichiometric and geometric control.

De novo design of modular protein hydrogels with programmable intra- and extracellular viscoelasticity

Relating the macroscopic properties of protein-based materials to their underlying component microstructure is an outstanding challenge. Here, we exploit computational design to specify the size, flexibility, and valency of de novo protein building blocks, as well as the interaction dynamics between them, to investigate how molecular parameters govern the macroscopic viscoelasticity of the resultant protein hydrogels. We construct gel systems from pairs of symmetric protein homo-oligomers, each comprising 2, 5, 24, or 120 individual protein components, that are crosslinked either physically or covalently into idealized step-growth biopolymer networks. Through rheological assessment, we find that the covalent linkage of multifunctional precursors yields hydrogels whose viscoelasticity depends on the crosslink length between the constituent building blocks. In contrast, reversibly crosslinking the homo-oligomeric components with a computationally designed heterodimer results in viscoelastic biomaterials exhibiting fluid-like properties under rest and low shear, but solid-like behavior at higher frequencies. Exploiting the unique genetic encodability of these materials, we demonstrate the assembly of protein networks within living mammalian cells and show via fluorescence recovery after photobleaching (FRAP) that mechanical properties can be tuned intracellularly in a manner similar to formulations formed extracellularly. We anticipate that the ability to modularly construct and systematically program the viscoelastic properties of designer protein-based materials could have broad utility in biomedicine, with applications in tissue engineering, therapeutic delivery, and synthetic biology.

Precisely patterned nanofibres 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 fibres can be systematically designed by introducing hydrophilic surface patches that force staggering of the monomers; the geometry of such fibres can be precisely tuned by varying the number of repeat units in the monomer and the placement of the hydrophilic patches.

Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review

In recent years, the widespread application of artificial intelligence algorithms in protein structure, function prediction, and de novo protein design has significantly accelerated the process of intelligent protein design and led to many noteworthy achievements. This advancement in protein intelligent design holds great potential to accelerate the development of new drugs, enhance the efficiency of biocatalysts, and even create entirely new biomaterials. Protein characterization is the key to the performance of intelligent protein design. However, there is no consensus on the most suitable characterization method for intelligent protein design tasks. This review describes the methods, characteristics, and representative applications of traditional descriptors, sequence-based and structure-based protein characterization. It discusses their advantages, disadvantages, and scope of application. It is hoped that this could help researchers to better understand the limitations and application scenarios of these methods, and provide valuable references for choosing appropriate protein characterization techniques for related research in the field, so as to better carry out protein research.

Preparation and Unique Three-Dimensional Self-Assembly Property of Starfish Ferritin

The structure and assembly properties of ferritin derived from aquatic products remain to be explored. Constructing diverse three-dimensional (3D) protein architectures with the same building blocks has important implications for nutrient delivery, medicine and materials science. Herein, ferritin from Asterias forbesii (AfFer) was prepared, and its crystal structure was resolved at 1.91 Å for the first time. Notably, different from the crystal structure of other reported ferritin, AfFer exhibited a BCT lattice arrangement in its crystals. Bioinspired by the crystal structure of AfFer, we described an effective approach for manufacturing 3D porous, crystalline nanoarchitectures by redesigning the shared protein interface involved in different 3D protein arrays. Based on this strategy, two 3D superlattices of body-centered tetragonal and simple cubicwere constructed with ferritin molecules as the building blocks. This study provided a potentially generalizable strategy for constructing different 3D protein-based crystalline biomaterials with the same building blocks.

Spatially Patterned, Porous Protein Crystals as Multifunctional Materials

While the primary use of protein crystals has historically been in crystallographic structure determination, they have recently emerged as promising materials with many advantageous properties such as high porosity, biocompatibility, stability, structural and functional versatility, and genetic/chemical tailorability. Here, we report that the utility of protein crystals as functional materials can be further augmented through their spatial patterning and control of their morphologies. To this end, we took advantage of the chemically and kinetically controllable nature of ferritin self-assembly and constructed core-shell crystals with chemically distinct domains, tunable structural patterns, and morphologies. The spatial organization within ferritin crystals enabled the generation of patterned, multi-enzyme frameworks with cooperative catalytic behavior. We further exploited the differential growth kinetics of ferritin crystal facets to assemble Janus-type architectures with an anisotropic arrangement of chemically distinct domains. These examples represent a step toward using protein crystals as reaction vessels for complex multi-step reactions and broadening their utility as functional, solid-state materials. Our results demonstrate that morphology control and spatial patterning, which are key concepts in materials science and nanotechnology, can also be applied for engineering protein crystals.

Spatiotemporal Landscape for the Sophisticated Transformation of Protein Assemblies Defined by Multiple Supramolecular Interactions

Precise protein assemblies not only constitute a series of living machineries but also provide an advanced class of biomaterials. Previously, we developed the inducing ligand strategy to generate various fixed protein assemblies, without the formation of noncovalent interactions between proteins. Here, we demonstrated that controlling the symmetry and number of supramolecular interactions introduced on protein surfaces could direct the formation of unspecific interactions between proteins and induce various nanoscale assemblies, including coiling nanowires, nanotubes, and nanosheets, without manipulation of the protein's native surfaces. More importantly, these nanoscale assemblies could spontaneously evolve into more ordered architectures, crystals. We further showed that the transformation from the introduced supramolecular interactions to the interactions formed between proteins was crucial for pathway selection and outcomes of evolution. These findings reveal a transformation mechanism of protein self-assembly that has not been exploited before and may provide an approach to generate complex and transformable biomacromolecular self-assemblies.

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.

De novo design of modular protein hydrogels with programmable intra- and extracellular viscoelasticity

Relating the macroscopic properties of protein-based materials to their underlying component microstructure is an outstanding challenge. Here, we exploit computational design to specify the size, flexibility, and valency of de novo protein building blocks, as well as the interaction dynamics between them, to investigate how molecular parameters govern the macroscopic viscoelasticity of the resultant protein hydrogels. We construct gel systems from pairs of symmetric protein homo-oligomers, each comprising 2, 5, 24, or 120 individual protein components, that are crosslinked either physically or covalently into idealized step-growth biopolymer networks. Through rheological assessment and molecular dynamics (MD) simulation, we find that the covalent linkage of multifunctional precursors yields hydrogels whose viscoelasticity depends on the crosslink length between the constituent building blocks. In contrast, reversibly crosslinking the homo-oligomeric components with a computationally designed heterodimer results in non-Newtonian biomaterials exhibiting fluid-like properties under rest and low shear, but shear-stiffening solid-like behavior at higher frequencies. Exploiting the unique genetic encodability of these materials, we demonstrate the assembly of protein networks within living mammalian cells and show via fluorescence recovery after photobleaching (FRAP) that mechanical properties can be tuned intracellularly, in correlation with matching formulations formed extracellularly. We anticipate that the ability to modularly construct and systematically program the viscoelastic properties of designer protein-based materials could have broad utility in biomedicine, with applications in tissue engineering, therapeutic delivery, and synthetic biology.

Solvent-resolved self-assemblies of cholesteryl-cyanostilbene conjugates with photo- and thermo-responsiveness

It remains challenging to construct multifunctional chiral stimulus-responsive molecules and to modulate their morphology at the nanoscale. In this paper, we synthesized a novel chiral molecule with both photoactive and potentially bioactive properties and found that the morphological changes of its self-assembly were influenced by solvent polarity and light exposure. This work enabled the synthesized molecule to undergo Z-E isomerization efficiently under light irradiation by introducing highly oriented hydrogen bonds into the cyanostilbene part. The photoisomerization of the cyanostilbene part from Z- to E-type was further exploited, leading to morphological changes from nanohelices to vesicles with chiroptical evolution. The light-modulated supramolecular chirality and nanostructure provide a green and efficient method for the design of responsive chiral materials.

Designing artificial pathways for improving chemical production

Metabolic engineering exploits manipulation of catalytic and regulatory elements to improve a specific function of the host cell, often the synthesis of interesting chemicals. Although naturally occurring pathways are significant resources for metabolic engineering, these pathways are frequently inefficient and suffer from a series of inherent drawbacks. Designing artificial pathways in a rational manner provides a promising alternative for chemicals production. However, the entry barrier of designing artificial pathway is relatively high, which requires researchers a comprehensive and deep understanding of physical, chemical and biological principles. On the other hand, the designed artificial pathways frequently suffer from low efficiencies, which impair their further applications in host cells. Here, we illustrate the concept and basic workflow of retrobiosynthesis in designing artificial pathways, as well as the most currently used methods including the knowledge- and computer-based approaches. Then, we discuss how to obtain desired enzymes for novel biochemistries, and how to trim the initially designed artificial pathways for further improving their functionalities. Finally, we summarize the current applications of artificial pathways from feedstocks utilization to various products synthesis, as well as our future perspectives on designing artificial pathways.

Recording of cellular physiological histories along optically readable self-assembling protein chains

Observing cellular physiological histories is key to understanding normal and disease-related processes. Here we describe expression recording islands-a fully genetically encoded approach that enables both continual digital recording of biological information within cells and subsequent high-throughput readout in fixed cells. The information is stored in growing intracellular protein chains made of self-assembling subunits, human-designed filament-forming proteins bearing different epitope tags that each correspond to a different cellular state or function (for example, gene expression downstream of neural activity or pharmacological exposure), allowing the physiological history to be read out along the ordered subunits of protein chains with conventional optical microscopy. We use expression recording islands to record gene expression timecourse downstream of specific pharmacological and physiological stimuli in cultured neurons and in living mouse brain, with a time resolution of a fraction of a day, over periods of days to weeks.

Time-tagged ticker tapes for intracellular recordings

Recording transcriptional histories of a cell would enable deeper understanding of cellular developmental trajectories and responses to external perturbations. Here we describe an engineered protein fiber that incorporates diverse fluorescent marks during its growth to store a ticker tape-like history. An embedded HaloTag reporter incorporates user-supplied dyes, leading to colored stripes that map the growth of each individual fiber to wall clock time. A co-expressed eGFP tag driven by a promoter of interest records a history of transcriptional activation. High-resolution multi-spectral imaging on fixed samples reads the cellular histories, and interpolation of eGFP marks relative to HaloTag timestamps provides accurate absolute timing. We demonstrate recordings of doxycycline-induced transcription in HEK cells and cFos promoter activation in cultured neurons, with a single-cell absolute accuracy of 30-40 minutes over a 12-hour recording. The protein-based ticker tape design we present here could be generalized to achieve massively parallel single-cell recordings of diverse physiological modalities.

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.

Greater than the sum of parts: Mechanisms of metabolic regulation by enzyme filaments

Recent work in structural biology is shedding light on how many of the enzymes of intermediary metabolism are self- and co-assembling into large, filamentous polymers or agglomerates to organize and regulate the complex and essential biochemical pathways in cells. Filament assembly provides an additional layer of regulation by modulating the intrinsic allostery of the enzyme protomers which tunes activity in response to a variety of environmental cues. Enzyme filaments dynamically assemble and disassemble in response to changes in metabolite levels and environmental cues, shifting metabolic flux on a more rapid timescale than transcriptional or translational reprogramming. Here we present recent examples of high-resolution structures of filaments from proteins in intermediary metabolism and we discuss how filament assembly modulates the activities of these and other proteins.

Design, synthesis, and characterization of protein origami based on self-assembly of a brick and staple artificial protein pair

A versatile strategy to create an inducible protein assembly with predefined geometry is demonstrated. The assembly is triggered by a binding protein that staples two identical protein bricks together in a predictable spatial conformation. The brick and staple proteins are designed for mutual directional affinity and engineered by directed evolution from a synthetic modular repeat protein library. As a proof of concept, this article reports on the spontaneous, extremely fast and quantitative self-assembly of two designed alpha-repeat (αRep) brick and staple proteins into macroscopic tubular superhelices at room temperature. Small-angle X-ray scattering (SAXS) and transmission electron microscopy (TEM with staining agent and cryoTEM) elucidate the resulting superhelical arrangement that precisely matches the a priori intended 3D assembly. The highly ordered, macroscopic biomolecular construction sustains temperatures as high as 75 °C thanks to the robust αRep building blocks. Since the α-helices of the brick and staple proteins are highly programmable, their design allows encoding the geometry and chemical surfaces of the final supramolecular protein architecture. This work opens routes toward the design and fabrication of multiscale protein origami with arbitrarily programmed shapes and chemical functions.

Synthesis of Fluorescent Lanthipeptide Cytolysin S Analogues by Late-Stage Sulfamidate Ring Opening

Nucleophilic ring opening of cyclic sulfamidates derived from amino acids is a common strategy for the synthesis of lanthionine derivatives. In this work, we report the regio-, chemo-, and stereoselective intramolecular S-alkylation of a cysteine residue with N-sulfonyl sulfamidates for the synthesis of cyclic lanthionine-containing peptides. The strategy involves the solid-phase synthesis of sulfamidate-containing peptides followed by late-stage intramolecular cyclization. This protocol allowed for the synthesis of four full-length cytolysin S (CylLS″) analogues, two α-peptides and two hybrid α/β-peptides. Their conformational preferences and biological activities were assessed and compared with those of wild-type CylLS″.

Controlled sequential in situ self-assembly and disassembly of a fluorogenic cisplatin prodrug for cancer theranostics

Temporal control of delivery and release of drugs in tumors are important in improving therapeutic outcomes to patients. Here, we report a sequential stimuli-triggered in situ self-assembly and disassembly strategy to direct delivery and release of theranostic drugs in vivo. Using cisplatin as a model anticancer drug, we design a stimuli-responsive small-molecule cisplatin prodrug (P-CyPt), which undergoes extracellular alkaline phosphatase-triggered in situ self-assembly and succeeding intracellular glutathione-triggered disassembly process, allowing to enhance accumulation and elicit burst release of cisplatin in tumor cells. Compared with cisplatin, P-CyPt greatly improves antitumor efficacy while mitigates off-target toxicity in mice with subcutaneous HeLa tumors and orthotopic HepG2 liver tumors after systemic administration. Moreover, P-CyPt also produces activated near-infrared fluorescence (at 710 nm) and dual photoacoustic imaging signals (at 700 and 750 nm), permitting high sensitivity and spatial-resolution delineation of tumor foci and real-time monitoring of drug delivery and release in vivo. This strategy leverages the advantages offered by in situ self-assembly with those of intracellular disassembly, which may act as a general platform for the design of prodrugs capable of improving drug delivery for cancer theranostics.

Geometrically programmed self-limited assembly of tubules using DNA origami colloids

Nature is replete with self-assembled materials that have one or more self-limited dimensions, including shells, tubules, and fibers. Despite significant advances in making nanometer- and micrometer-scale subunits, the programmable assembly of similar self-limiting architectures from synthetic components has remained largely out of reach. In this article, we create geometrically programmed subunits using DNA origami and study their assembly into tubules with a self-limited width. We show that the average self-limited dimension can be tuned by changing the local curvature encoded in a single subunit. Exploiting the programmability of our system, we further test the tradeoffs between fidelity and complexity embodied by two paradigms for self-limited assembly: self-closure through programmed curvature and addressable assembly through programmed specific interactions.

Self-assembly is one of the most promising strategies for making functional materials at the nanoscale, yet new design principles for making self-limiting architectures, rather than spatially unlimited periodic lattice structures, are needed. To address this challenge, we explore the tradeoffs between addressable assembly and self-closing assembly of a specific class of self-limiting structures: cylindrical tubules. We make triangular subunits using DNA origami that have specific, valence-limited interactions and designed binding angles, and we study their assembly into tubules that have a self-limited width that is much larger than the size of an individual subunit. In the simplest case, the tubules are assembled from a single component by geometrically programming the dihedral angles between neighboring subunits. We show that the tubules can reach many micrometers in length and that their average width can be prescribed through the dihedral angles. We find that there is a distribution in the width and the chirality of the tubules, which we rationalize by developing a model that considers the finite bending rigidity of the assembled structure as well as the mechanism of self-closure. Finally, we demonstrate that the distributions of tubules can be further sculpted by increasing the number of subunit species, thereby increasing the assembly complexity, and demonstrate that using two subunit species successfully reduces the number of available end states by half. These results help to shed light on the roles of assembly complexity and geometry in self-limited assembly and could be extended to other self-limiting architectures, such as shells, toroids, or triply periodic frameworks.

Protein Design: From the Aspect of Water Solubility and Stability

Water solubility and structural stability are key merits for proteins defined by the primary sequence and 3D-conformation. Their manipulation represents important aspects of the protein design field that relies on the accurate placement of amino acids and molecular interactions, guided by underlying physiochemical principles. Emulated designer proteins with well-defined properties both fuel the knowledge-base for more precise computational design models and are used in various biomedical and nanotechnological applications. The continuous developments in protein science, increasing computing power, new algorithms, and characterization techniques provide sophisticated toolkits for solubility design beyond guess work. In this review, we summarize recent advances in the protein design field with respect to water solubility and structural stability. After introducing fundamental design rules, we discuss the transmembrane protein solubilization and de novo transmembrane protein design. Traditional strategies to enhance protein solubility and structural stability are introduced. The designs of stable protein complexes and high-order assemblies are covered. Computational methodologies behind these endeavors, including structure prediction programs, machine learning algorithms, and specialty software dedicated to the evaluation of protein solubility and aggregation, are discussed. The findings and opportunities for Cryo-EM are presented. This review provides an overview of significant progress and prospects in accurate protein design for solubility and stability.

Cryo-EM of Helical Polymers

While the application of cryogenic electron microscopy (cryo-EM) to helical polymers in biology has a long history, due to the huge number of helical macromolecular assemblies in viruses, bacteria, archaea, and eukaryotes, the use of cryo-EM to study synthetic soft matter noncovalent polymers has been much more limited. This has mainly been due to the lack of familiarity with cryo-EM in the materials science and chemistry communities, in contrast to the fact that cryo-EM was developed as a biological technique. Nevertheless, the relatively few structures of self-assembled peptide nanotubes and ribbons solved at near-atomic resolution by cryo-EM have demonstrated that cryo-EM should be the method of choice for a structural analysis of synthetic helical filaments. In addition, cryo-EM has also demonstrated that the self-assembly of soft matter polymers has enormous potential for polymorphism, something that may be obscured by techniques such as scattering and spectroscopy. These cryo-EM structures have revealed how far we currently are from being able to predict the structure of these polymers due to their chaotic self-assembly behavior.

Precise Assembly of Proteins and Carbohydrates for Next-Generation Biomaterials

The complexity and diversity of biomacromolecules make them a unique class of building blocks for generating precise assemblies. They are particularly available to a new generation of biomaterials integrated with living systems due to their intrinsic properties such as accurate recognition, self-organization, and adaptability. Therefore, many excellent approaches have been developed, leading to a variety of quite practical outcomes. Here, we review recent advances in the fabrication and application of artificially precise assemblies by employing proteins and carbohydrates as building blocks, followed by our perspectives on some of new challenges, goals, and opportunities for the future research directions in this field.

Harnessing the Structural and Functional Diversity of Protein Filaments as Biomaterial Scaffolds

The natural ability of many proteins to polymerize into highly structured filaments has been harnessed as scaffolds to align functional molecules in a diverse range of biomaterials. Protein-engineering methodologies also enable the structural and physical properties of filaments to be tailored for specific biomaterial applications through genetic engineering or filaments built from the ground up using advances in the computational prediction of protein folding and assembly. Using these approaches, protein filament-based biomaterials have been engineered to accelerate enzymatic catalysis, provide routes for the biomineralization of inorganic materials, facilitate energy production and transfer, and provide support for mammalian cells for tissue engineering. In this review, we describe how the unique structural and functional diversity in natural and computationally designed protein filaments can be harnessed in biomaterials. In addition, we detail applications of these protein assemblies as material scaffolds with a particular emphasis on applications that exploit unique properties of specific filaments. Through the diversity of protein filaments, the biomaterial engineer's toolbox contains many modular protein filaments that will likely be incorporated as the main structural component of future biomaterials.

Protein design via deep learning

Proteins with desired functions and properties are important in fields like nanotechnology and biomedicine. De novo protein design enables the production of previously unseen proteins from the ground up and is believed as a key point for handling real social challenges. Recent introduction of deep learning into design methods exhibits a transformative influence and is expected to represent a promising and exciting future direction. In this review, we retrospect the major aspects of current advances in deep-learning-based design procedures and illustrate their novelty in comparison with conventional knowledge-based approaches through noticeable cases. We not only describe deep learning developments in structure-based protein design and direct sequence design, but also highlight recent applications of deep reinforcement learning in protein design. The future perspectives on design goals, challenges and opportunities are also comprehensively discussed.

Structures of synthetic helical filaments and tubes based on peptide and peptido-mimetic polymers

Synthetic peptide and peptido-mimetic filaments and tubes represent a diverse class of nanomaterials with a broad range of potential applications, such as drug delivery, vaccine development, synthetic catalyst design, encapsulation, and energy transduction. The structures of these filaments comprise supramolecular polymers based on helical arrangements of subunits that can be derived from self-assembly of monomers based on diverse structural motifs. In recent years, structural analyses of these materials at near-atomic resolution (NAR) have yielded critical insights into the relationship between sequence, local conformation, and higher-order structure and morphology. This structural information offers the opportunity for development of new tools to facilitate the predictable and reproduciblede novodesign of synthetic helical filaments. However, these studies have also revealed several significant impediments to the latter process – most notably, the common occurrence of structural polymorphism due to the lability of helical symmetry in structural space. This article summarizes the current state of knowledge on the structures of designed peptide and peptido-mimetic filamentous assemblies, with a focus on structures that have been solved to NAR for which reliable atomic models are available.

In silico evolution of nucleic acid-binding proteins from a nonfunctional scaffold

Directed evolution emulates the process of natural selection to produce proteins with improved or altered functions. These approaches have proven to be very powerful but are technically challenging and particularly time and resource intensive. To bypass these limitations, we constructed a system to perform the entire process of directed evolution in silico. We employed iterative computational cycles of mutation and evaluation to predict mutations that confer high-affinity binding activities for DNA and RNA to an initial de novo designed protein with no inherent function. Beneficial mutations revealed modes of nucleic acid recognition not previously observed in natural proteins, highlighting the ability of computational directed evolution to access new molecular functions. Furthermore, the process by which new functions were obtained closely resembles natural evolution and can provide insights into the contributions of mutation rate, population size and selective pressure on functionalization of macromolecules in nature.

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.

De novo protein design by an energy function based on series expansion in distance and orientation dependence

MOTIVATION

Despite many successes, de novo protein design is not yet a solved problem as its success rate remains low. The low success rate is largely because we do not yet have an accurate energy function for describing the solvent-mediated interaction between amino acid residues in a protein chain. Previous studies showed that an energy function based on series expansions with its parameters optimized for side-chain and loop conformations can lead to one of the most accurate methods for side chain (OSCAR) and loop prediction (LEAP). Following the same strategy, we developed an energy function based on series expansions with the parameters optimized in four separate stages (recovering single-residue types without and with orientation dependence, selecting loop decoys and maintaining the composition of amino acids). We tested the energy function for de novo design by using Monte Carlo simulated annealing.

RESULTS

The method for protein design (OSCAR-Design) is found to be as accurate as OSCAR and LEAP for side-chain and loop prediction, respectively. In de novo design, it can recover native residue types ranging from 38% to 43% depending on test sets, conserve hydrophobic/hydrophilic residues at ∼75%, and yield the overall similarity in amino acid compositions at more than 90%. These performance measures are all statistically significantly better than several protein design programs compared. Moreover, the largest hydrophobic patch areas in designed proteins are near or smaller than those in native proteins. Thus, an energy function based on series expansion can be made useful for protein design.

AVAILABILITY AND IMPLEMENTATION

The Linux executable version is freely available for academic users at http://zhouyq-lab.szbl.ac.cn/resources/.

Dendrimer-Like Supramolecular Assembly of Proteins with a Tunable Size and Valency Through Stepwise Iterative Growth

The assembly of proteins in a programmable manner provides insight into the creation of novel functional nanomaterials for practical applications. Despite many advances, however, a rational protein assembly with an easy scalability in terms of size and valency remains a challenge. Here, a simple bottom-up approach to the supramolecular protein assembly with a tunable size and valency in a programmable manner is presented. The dendrimer-like protein assembly, simply called a "protein dendrimer," is constructed through a stepwise and alternate addition of a building block protein. Starting from zeroth-generation protein dendrimer (pG0 ) of 27 kDa, the protein dendrimer is sequentially grown to pG1 , pG2 , pG3 , to pG4 with a molecular mass of 94, 216, 483, and 959 kDa, respectively. The valency of the protein dendrimers at the periphery increases by a factor of two after each generation, allowing a tunable valency and easy functionalization. The protein dendrimers functionalizes with a targeting moiety and a cytotoxic protein cargo shows a typical feature of multi-valency in the avidity and a highly enhanced cellular cytotoxicity, exemplifying their utility as a protein delivery platform. The present approach can be effectively used in the creation of protein architectures with new functions for biotechnological and medical applications.

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.

Flavonoid-Like Components of Peanut Stem and Leaf Extract Promote Sleep by Decreasing Neuronal Excitability

SCOPE

Peanut stem and leaf (PSL), a traditional Chinese medicine, is widely used as a dietary supplement to improve sleep quality; however, the underlying mechanism is unclear. Here, the study aims to determine whether active compounds in PSL extract exert their effects by mediating neuronal excitability.

METHODS AND RESULTS

Aqueous PSL extract (500 mg kg^-1 BW) increases the duration of total sleep (TS), slow wave sleep (SWS) and rapid eye movement sleep (REMS) in BALB/c mice after 7 and 14 continuous days of intragastric administration. Two PSL extract components with flavonoid-like structures: 4',7-di-O-methylnaringenin (DMN, 61 µg kg^-1 BW) and 2'-O-methylisoliquiritigenin (MIL, 12 µg kg^-1 BW), show similar effects on sleep in BALB/c mice. Moreover, incubation with DMN (50 µM) and MIL (50 µM) acutely reduces voltage-gated sodium and potassium currents and suppresses the firing of evoked action potential in mouse cortical neurons, indicating the inhibition on neuronal excitability. Meanwhile, RNA-seq analysis predicts the potential regulation of voltage-gated channels, which is according with the molecular docking simulation that both MIL and DMN can bind to voltage gated sodium channels 1.2 (Na_v 1.2).

CONCLUSIONS

DMN and MIL are the active ingredients of PSL that improve sleep quality, suggesting that PSL promotes sleep by regulating the excitability of neurons.

Deterministic chaos in the self-assembly of β sheet nanotubes from an amphipathic oligopeptide

The self-assembly of designed peptides into filaments and other higher-order structures has been the focus of intense interest because of the potential for creating new biomaterials and biomedical devices. These peptide assemblies have also been used as models for understanding biological processes, such as the pathological formation of amyloid. We investigate the assembly of an octapeptide sequence, Ac-FKFEFKFE-NH₂, motivated by prior studies that demonstrated that this amphipathic β strand peptide self-assembled into fibrils and biocompatible hydrogels. Using high-resolution cryoelectron microscopy (cryo-EM), we are able to determine the atomic structure for two different coexisting forms of the fibrils, containing four and five β sandwich protofilaments, respectively. Surprisingly, the inner walls in both forms are parallel β sheets, while the outer walls are antiparallel β sheets. Our results demonstrate the chaotic nature of peptide self-assembly and illustrate the importance of cryo-EM structural analysis to understand the complex phase behavior of these materials at near-atomic resolution.

Encoding hierarchical assembly pathways of proteins with DNA

The structural and functional diversity of materials in nature depends on the controlled assembly of discrete building blocks into complex architectures via specific, multistep, hierarchical assembly pathways. Achieving similar complexity in synthetic materials through hierarchical assembly is challenging due to difficulties with defining multiple recognition areas on synthetic building blocks and controlling the sequence through which those recognition sites direct assembly. Here, we show that we can exploit the chemical anisotropy of proteins and the programmability of DNA ligands to deliberately control the hierarchical assembly of protein-DNA materials. Through DNA sequence design, we introduce orthogonal DNA interactions with disparate interaction strengths ("strong" and "weak") onto specific geometric regions of a model protein, stable protein 1 (Sp1). We show that the spatial encoding of DNA ligands leads to highly directional assembly via strong interactions and that, by design, the first stage of assembly increases the multivalency of weak DNA-DNA interactions that give rise to an emergent second stage of assembly. Furthermore, we demonstrate that judicious DNA design not only directs assembly along a given pathway but can also direct distinct structural outcomes from a single pathway. This combination of protein surface and DNA sequence design allows us to encode the structural and chemical information necessary into building blocks to program their multistep hierarchical assembly. Our findings represent a strategy for controlling the hierarchical assembly of proteins to realize a diverse set of protein-DNA materials by design.

From structure to application: Progress and opportunities in peptide materials development

For over 20 years, peptide materials in their hydrogel or soluble fibril form have been used for biomedical applications such as drug delivery, cell culture, vaccines, and tissue regeneration. To facilitate the translation of these materials, key areas of research still need to be addressed. Their structural characterization lags compared to amyloid proteins. Many of the structural features designed to guide materials formation are primarily being characterized by their observation in atomic resolution structures of amyloid assemblies. Herein, these motifs are examined in relation to peptide designs identifying common interactions that drive assembly and provide structural specificity. Current efforts to design complex structures, as reviewed here, highlight the need to extend the structural revolution of amyloid proteins to peptide assemblies to validate design principles. With respect to clinical applications, the fundamental interactions and responses of proteins, cells, and the immune system to peptide materials are still not well understood. Only a few trends are just now emerging for peptide materials interactions with biological systems. Understanding how peptide material properties influence these interactions will enable the translation of materials towards current and emerging applications.

Importance of Substrate-Particle Repulsion for Protein-Templated Assembly of Metal Nanoparticles

We study the protein-directed assembly of colloidal gold nanoparticles on de novo designed protein nanofiber templates. Using sequential assembly on glass substrates, we attach positively charged gold nanoparticles to protein nanofibers engineered to have a high density of negatively charged surface residues. Using a combination of electron and optical microscopy, we measure the density of particle attachment and characterize binding specificity. By varying nanoparticle size and pH of the solution, we explore the importance of charge-dependent particle-fiber and particle-substrate interactions. We find an inverse correlation between particle size and attachment density to protein nanofibers, attributed to the balance between size-dependent electrostatic particle-fiber attraction and particle-substrate repulsion. We show pH-dependent particle attachment density and binding specificity in relation to the protonation fraction of each assembly layer. Finally, we employ hyperspectral scattering microscopy to draw conclusions about particle density and interparticle spacings of optically observable particle assemblies.

Alignment of Au nanorods along de novo designed protein nanofibers studied with automated image analysis

In this study, we focus on exploring the directional assembly of anisotropic Au nanorods along de novo designed 1D protein nanofiber templates. Using machine learning and automated image processing, we analyze scanning electron microscopy (SEM) images to study how the attachment density and alignment fidelity are influenced by variables such as the aspect ratio of the Au nanorods, and the salt concentration of the solution. We find that the Au nanorods prefer to align parallel to the protein nanofibers. This preference decreases with increasing salt concentration, but is only weakly sensitive to the nanorod aspect ratio. While the overall specific Au nanorod attachment density to the protein fibers increases with increasing solution ionic strength, this increase is dominated primarily by non-specific binding to the substrate background, and we find that greater specific attachment (nanorods attached to the nanofiber template as compared to the substrates) occurs at the lower studied salt concentrations, with the maximum ratio of specific to non-specific binding occurring when the protein fiber solutions are prepared in 75 mM NaCl concentration.