De novo design of a transmembrane Zn²⁺-transporting four-helix bundle

Computational design of de novo bioenergetic membrane proteins

The major energy-producing reactions of biochemistry occur at biological membranes. Computational protein design now provides the opportunity to elucidate the underlying principles of these processes and to construct bioenergetic pathways on our own terms. Here, we review recent achievements in this endeavour of 'synthetic bioenergetics', with a particular focus on new enabling tools that facilitate the computational design of biocompatible de novo integral membrane proteins. We use recent examples to showcase some of the key computational approaches in current use and highlight that the overall philosophy of 'surface-swapping' - the replacement of solvent-facing residues with amino acids bearing lipid-soluble hydrophobic sidechains - is a promising avenue in membrane protein design. We conclude by highlighting outstanding design challenges and the emerging role of AI in sequence design and structure ideation.

Validation of de novo designed water-soluble and transmembrane β-barrels by in silico folding and melting

In silico validation of de novo designed proteins with deep learning (DL)-based structure prediction algorithms has become mainstream. However, formal evidence of the relationship between a high-quality predicted model and the chance of experimental success is lacking. We used experimentally characterized de novo water-soluble and transmembrane β-barrel designs to show that AlphaFold2 and ESMFold excel at different tasks. ESMFold can efficiently identify designs generated based on high-quality (designable) backbones. However, only AlphaFold2 can predict which sequences have the best chance of experimentally folding among similar designs. We show that ESMFold can generate high-quality structures from just a few predicted contacts and introduce a new approach based on incremental perturbation of the prediction ("in silico melting"), which can reveal differences in the presence of favorable contacts between designs. This study provides a new insight on DL-based structure prediction models explainability and on how they could be leveraged for the design of increasingly complex proteins; in particular membrane proteins which have historically lacked basic in silico validation tools.

Adaptable Self-Assembly of a PEG Dendrimer-Coiled Coil Conjugate

Self-assembly of designed molecules has enabled the construction of a variety of functional nanostructures. Specifically, adaptable self-assembly has demonstrated several advantageous features for smart materials. Here, we demonstrate that an α-helical coiled coil conjugated with a dendrimer can adapt to spatial restriction due to the strong steric repulsion between dendrimer chains. The adaptable transformation of a tetrameric coiled coil to a trimeric coiled coil can be confirmed using analytical ultracentrifugation upon conjugation of the dendrimer to the coiled coil-forming building block. Interestingly, circular dichroism spectroscopy analysis of the dendrimer conjugate revealed an unconventional trend: the multimerization of the coiled coil is inversely dependent on concentration. This result implies that the spatial crowding between the bulky dendritic chains is significantly stronger than that between linear chains, thereby affecting the overall assembly process. We further illustrated the application potential by decorating the surface of gold nanorods (AuNRs) with the adaptable coiled coil. The dendrimer-coiled coil peptide conjugate can be utilized to fabricate organic-inorganic nanohybrids with enhanced colloidal and thermal stabilities. This study demonstrates that the coiled coil can engage in the adaptable mode of self-assembly with the potential to form dynamic peptide-based materials.

Beyond the Triple Helix: Exploration of the Hierarchical Assembly Space of Collagen-like Peptides

The de novo design of self-assembling peptides has garnered significant attention in scientific research. While alpha-helical assemblies have been extensively studied, exploration of polyproline type II (PPII) helices, such as those found in collagen, remains relatively limited. In this study, we focused on understanding the sequence-structure relationship in hierarchical assemblies of collagen-like peptides, using defense collagen SP-A as a model. By dissecting the sequence derived from SP-A and synthesizing short collagen-like peptides, we successfully constructed a discrete bundle of hollow triple helices. Mutation studies pinpointed amino acid sequences, including hydrophobic and charged residues that are critical for oligomer formation. These insights guided the de novo design of collagen-like peptides, resulting in the formation of diverse quaternary structures, including discrete and heterogenous bundled oligomers, 2D nanosheets, and pH-responsive nanoribbons. Our study represents a significant advancement in the understanding and harnessing of collagen higher-order assemblies beyond the triple helix.

Design of Functional Globular β-Sheet Miniproteins

The design of discrete β-sheet peptides is far less advanced than e. g. the design of α-helical peptides. The reputation of β-sheet peptides as being poorly soluble and aggregation-prone often hinders active design efforts. Here, we show that this reputation is unfounded. We demonstrate this by looking at the β-hairpin and WW domain. Their structure and folding have been extensively studied and they have long served as model systems to investigate protein folding and folding kinetics. The resulting fundamental understanding has led to the development of hyperstable β-sheet scaffolds that fold at temperatures of 100 °C or high concentrations of denaturants. These have been used to design functional miniproteins with protein or nucleic acid binding properties, in some cases with such success that medical applications are conceivable. The β-sheet scaffolds are not always completely rigid, but can be specifically designed to respond to changes in pH, redox potential or presence of metal ions. Some engineered β-sheet peptides also exhibit catalytic properties, although not comparable to those of natural proteins. Previous reviews have focused on the design of stably folded and non-aggregating β-sheet sequences. In our review, we now also address design strategies to obtain functional miniproteins from β-sheet folding motifs.

Engineering strategies and challenges of endolysin as an antibacterial agent against Gram-negative bacteria

Bacteriophage endolysin is a novel antibacterial agent that has attracted much attention in the prevention and control of drug-resistant bacteria due to its unique mechanism of hydrolysing peptidoglycans. Although endolysin exhibits excellent bactericidal effects on Gram-positive bacteria, the presence of the outer membrane of Gram-negative bacteria makes it difficult to lyse them extracellularly, thus limiting their application field. To enhance the extracellular activity of endolysin and facilitate its crossing through the outer membrane of Gram-negative bacteria, researchers have adopted physical, chemical, and molecular methods. This review summarizes the characterization of endolysin targeting Gram-negative bacteria, strategies for endolysin modification, and the challenges and future of engineering endolysin against Gram-negative bacteria in clinical applications, to promote the application of endolysin in the prevention and control of Gram-negative bacteria.

Lipid vesicle-based molecular robots

A molecular robot, which is a system comprised of one or more molecular machines and computers, can execute sophisticated tasks in many fields that span from nanomedicine to green nanotechnology. The core parts of molecular robots are fairly consistent from system to system and always include (i) a body to encapsulate molecular machines, (ii) sensors to capture signals, (iii) computers to make decisions, and (iv) actuators to perform tasks. This review aims to provide an overview of approaches and considerations to develop molecular robots. We first introduce the basic technologies required for constructing the core parts of molecular robots, describe the recent progress towards achieving higher functionality, and subsequently discuss the current challenges and outlook. We also highlight the applications of molecular robots in sensing biomarkers, signal communications with living cells, and conversion of energy. Although molecular robots are still in their infancy, they will unquestionably initiate massive change in biomedical and environmental technology in the not too distant future.

Design of Ligand-Operable Protein-Cages That Open Upon Specific Protein Binding

Protein nanocages have diverse applications in medicine and biotechnology, including molecular delivery. However, although numerous studies have demonstrated the ability of protein nanocages to encapsulate various molecular species, limited methods are available for subsequently opening a nanocage for cargo release under specific conditions. A modular platform with a specific protein-target-based mechanism of nanocage opening is notably lacking. To address this important technology gap, we present a new class of designed protein cages, the Ligand-Operable Cage (LOC). LOCs primarily comprise a protein nanocage core and a fused surface binding adaptor. The geometry of the LOC is designed so that binding of a target protein ligand (or multiple copies thereof) to the surface binder is sterically incompatible with retention of the assembled state of the cage. Therefore, the tight binding of a target ligand drives cage disassembly by mass action, subsequently exposing the encapsulated cargo. LOCs are modular; direct substitution of the surface binder sequence can reprogram the nanocage to open in response to any target protein ligand of interest. We demonstrate these design principles using both a natural and a designed protein cage as the core, with different proteins acting as the triggering ligand and with different reporter readouts─fluorescence unquenching and luminescence─for cage disassembly. These developments advance the critical problem of targeted molecular delivery and detection.

Next generation of multispecific antibody engineering

Multispecific antibodies recognize two or more epitopes located on the same or distinct targets. This added capability through protein design allows these man-made molecules to address unmet medical needs that are no longer possible with single targeting such as with monoclonal antibodies or cytokines alone. However, the approach to the development of these multispecific molecules has been met with numerous road bumps, which suggests that a new workflow for multispecific molecules is required. The investigation of the molecular basis that mediates the successful assembly of the building blocks into non-native quaternary structures will lead to the writing of a playbook for multispecifics. This is a must do if we are to design workflows that we can control and in turn predict success. Here, we reflect on the current state-of-the-art of therapeutic biologics and look at the building blocks, in terms of proteins, and tools that can be used to build the foundations of such a next-generation workflow.

Illuminating protein space with a programmable generative model

Three billion years of evolution has produced a tremendous diversity of protein molecules1, but the full potential of proteins is likely to be much greater. Accessing this potential has been challenging for both computation and experiments because the space of possible protein molecules is much larger than the space of those likely to have functions. Here we introduce Chroma, a generative model for proteins and protein complexes that can directly sample novel protein structures and sequences, and that can be conditioned to steer the generative process towards desired properties and functions. To enable this, we introduce a diffusion process that respects the conformational statistics of polymer ensembles, an efficient neural architecture for molecular systems that enables long-range reasoning with sub-quadratic scaling, layers for efficiently synthesizing three-dimensional structures of proteins from predicted inter-residue geometries and a general low-temperature sampling algorithm for diffusion models. Chroma achieves protein design as Bayesian inference under external constraints, which can involve symmetries, substructure, shape, semantics and even natural-language prompts. The experimental characterization of 310 proteins shows that sampling from Chroma results in proteins that are highly expressed, fold and have favourable biophysical properties. The crystal structures of two designed proteins exhibit atomistic agreement with Chroma samples (a backbone root-mean-square deviation of around 1.0 Å). With this unified approach to protein design, we hope to accelerate the programming of protein matter to benefit human health, materials science and synthetic biology.

Synthetic α-Helical Nanopore Reactor for Chemical Sensing

The use of nanopores for the single-molecule sensing of folded proteins and biomacromolecules has recently gained attention. Here, we introduce a simplified synthetic α-helical transmembrane pore, pPorA, as a nanoreactor and sensor that exhibits functional versatility comparable to that of engineered protein and DNA nanopores. The pore, built from the assembly of synthetic 40-amino-acid-long peptides, is designed to contain cysteine residues within the lumen and at the pore terminus for site-specific chemical modification probed using single-channel electrical recordings. The reaction of the pore with differently charged activated thiol reagents was studied, wherein positively charged reagents electrophoretically driven into the pore resulted in pore blocking in discrete steps upon covalent bond formation. The asymmetric blockage patterns resulting from cis and trans-side addition of reagents reveal the pore orientation in the lipid membrane. Furthermore, activated PEG thiols covalently blocked the pores over a longer duration in a charge-independent manner, establishing the large diameter and orientation of the formed pores. While the covalent binding of thiol reagents caused a drop in the pore conductance, cationic cyclic octasaccharides produced time-resolved translocation events, confirming the structural flexibility and tunability of the pores. The ability of the pore to accommodate large analytes and the considerable current amplitude variation following bond formation events are promising for developing platforms to resolve multistep chemical reactions at the single-molecule level for applications in synthetic nanobiotechnology.

Atomically Accurate Design of Metalloproteins with Predefined Coordination Geometries

We report a new computational protein design method for the construction of oligomeric protein assemblies around metal centers with predefined coordination geometries. We apply this method to design two homotrimeric assemblies, Tet4 and TP1, with tetrahedral and trigonal-pyramidal tris(histidine) metal coordination geometries, respectively, and demonstrate that both assemblies form the targeted metal centers with ≤0.2 Å accuracy. Although Tet4 and TP1 are constructed from the same parent protein building block, they are distinct in terms of their overall architectures, the environment surrounding the metal centers, and their metal-based reactivities, illustrating the versatility of our approach.

Transient water wires mediate selective proton transport in designed channel proteins

Selective proton transport through proteins is essential for forming and using proton gradients in cells. Protons are conducted along hydrogen-bonded 'wires' of water molecules and polar side chains, which, somewhat surprisingly, are often interrupted by dry apolar stretches in the conduction pathways, inferred from static protein structures. Here we hypothesize that protons are conducted through such dry spots by forming transient water wires, often highly correlated with the presence of the excess protons in the water wire. To test this hypothesis, we performed molecular dynamics simulations to design transmembrane channels with stable water pockets interspersed by apolar segments capable of forming flickering water wires. The minimalist designed channels conduct protons at rates similar to viral proton channels, and they are at least 106-fold more selective for H+ over Na+. These studies inform the mechanisms of biological proton conduction and the principles for engineering proton-conductive materials.

Exploiting conformational dynamics to modulate the function of designed proteins

With the recent success in calculating protein structures from amino acid sequences using artificial intelligence-based algorithms, an important next step is to decipher how dynamics is encoded by the primary protein sequence so as to better predict function. Such dynamics information is critical for protein design, where strategies could then focus not only on sequences that fold into particular structures that perform a given task, but would also include low-lying excited protein states that could influence the function of the designed protein. Herein, we illustrate the importance of dynamics in modulating the function of C34, a designed α/β protein that captures β-strands of target ligands and is a member of a family of proteins designed to sequester β-strands and β hairpins of aggregation-prone molecules that lead to a variety of pathologies. Using a strategy to "see" regions of apo C34 that are invisible to NMR spectroscopy as a result of pervasive conformational exchange, as well as a mutagenesis approach whereby C34 molecules are stabilized into a single conformer, we determine the structures of the predominant conformations that are sampled by C34 and show that these attenuate the affinity for cognate peptide. Subsequently, the observed motion is exploited to develop an allosterically regulated peptide binder whose binding affinity can be controlled through the addition of a second molecule. Our study emphasizes the unique role that NMR can play in directing the design process and in the construction of new molecules with more complex functionality.

Multi-Stimuli-Responsive and Mechano-Actuated Biomimetic Membrane Nanopores Self-Assembled from DNA

In bioinspired design, biological templates are mimicked in structure and function by highly controllable synthetic means. Of interest are static barrel-like nanopores that enable molecular transport across membranes for use in biosensing, sequencing, and biotechnology. However, biological ion channels offer additional functions such as dynamic changes of the entire pore shape between open and closed states, and triggering of dynamic processes with biochemical and physical stimuli. To better capture this complexity, this report presents multi-stimuli and mechano-responsive biomimetic nanopores which are created with DNA nanotechnology. The nanopores switch between open and closed states, whereby specific binding of DNA and protein molecules as stimuli locks the pores in the open state. Furthermore, the physical stimulus of high transmembrane voltage switches the pores into a closed state. In addition, the pore diameters are larger and more tunable than those of natural templates. These multi-stimuli-responsive and mechanically actuated nanopores mimic several aspects of complex biological channels yet offer easier control over pore size, shape and stimulus response. The designer pores are expected to be applied in biosensing and synthetic biology.

Enzyme-Powered Tubular Microrobotic Jets as Bioinspired Micropumps for Active Transmembrane Drug Transport

In nature, there exist a variety of transport proteins on cell membranes capable of actively moving cargos across biological membranes, which plays a vital role in the living activities of cells. Emulating such biological pumps in artificial systems may bring in-depth insights on the principles and functions of cell behaviors. However, it poses great challenges due to difficulty in the sophisticated construction of active channels at the cellular scale. Here, we report the development of bionic micropumps for active transmembrane transportation of molecular cargos across living cells that is realized by enzyme-powered microrobotic jets. By immobilizing urease onto the surface of a silica-based microtube, the prepared microjet is capable of catalyzing the decomposition of urea in surrounding environments and generating microfluidic flow through the inside channel for self-propulsion, which is verified by both numerical simulation and experimental results. Therefore, once naturally endocytosed by the cell, the microjet enables the diffusion and, more importantly, active transportation of molecular substances between the extracellular and intracellular ends with the assistance of generated microflow, thus serving as an artificial biomimetic micropump. Furthermore, by constructing enzymatic micropumps on cancer cell membranes, enhanced delivery of anticancer doxorubicin into cells as well as improved killing efficacy are achieved, which demonstrates the effectiveness of the active transmembrane drug transport strategy in cancer treatment. This work not only extends the applications of micro/nanomachines in biomedical fields but also provides a promising platform for future cell biology research at cellular and subcellular scales.

Molecular torsion springs: alteration of helix curvature in frustrated tertiary folds

The first abiotic foldamer tertiary structures have been recently reported in the form of aromatic helix-turn-helix motifs based on oligo-quinolinecarboxamides held together by intramolecular hydrogen bonds. Tertiary folds were predicted by computational modelling of the hydrogen-bonding interfaces between helices and later verified by X-ray crystallography. However, the prognosis of how the conformational preference inherent to each helix influences the tertiary structure warranted further investigation. Several new helix-turn-helix sequences were synthesised in which some hydrogen bonds have been removed. Contrary to expectations, this change did not strongly destabilise the tertiary folds. On closer inspection, a new crystal structure revealed that helices adopt their natural curvature when some hydrogen bonds are missing and undergo some spring torsion upon forming the said hydrogen bonds, thus potentially giving rise to a conformational frustration. This phenomenon sheds light on the aggregation behaviour of the helices when they are not linked by a turn unit.

Towards de novo design of transmembrane α-helical assemblies using structural modelling and molecular dynamics simulation

Computational de novo protein design involves iterative processes consisting of amino acid sequence design, structural modelling and scoring, and design validation by synthesis and experimental characterisation. Recent advances in protein structure prediction and modelling methods have enabled the highly efficient and accurate design of water-soluble proteins. However, the design of membrane proteins remains a major challenge. To advance membrane protein design, considering the higher complexity of membrane protein folding, stability, and dynamic interactions between water, ions, lipids, and proteins is an important task. For introducing explicit solvents and membranes to these design methods, all-atom molecular dynamics (MD) simulations of designed proteins provide useful information that cannot be obtained experimentally. In this review, we first describe two major approaches to designing transmembrane α-helical assemblies, consensus and de novo design. We further illustrate recent MD studies of membrane protein folding related to protein design, as well as advanced treatments in molecular models and conformational sampling techniques in the simulations. Finally, we discuss the possibility to introduce MD simulations after the existing static modelling and screening of design decoys as an additional step for refinement of the design, which considers membrane protein folding dynamics and interactions with explicit membranes.

What Can De Novo Protein Design Bring to the Treatment of Hematological Disorders?

Protein therapeutics have been widely used to treat hematological disorders. With the advent of de novo protein design, protein therapeutics are not limited to ameliorating natural proteins but also produce novel protein sequences, folds, and functions with shapes and functions customized to bind to the therapeutic targets. De novo protein techniques have been widely used biomedically to design novel diagnostic and therapeutic drugs, novel vaccines, and novel biological materials. In addition, de novo protein design has provided new options for treating hematological disorders. Scientists have designed protein switches called Colocalization-dependent Latching Orthogonal Cage-Key pRoteins (Co-LOCKR) that perform computations on the surface of cells. De novo designed molecules exhibit a better capacity than the currently available tyrosine kinase inhibitors in chronic myeloid leukemia therapy. De novo designed protein neoleukin-2/15 enhances chimeric antigen receptor T-cell activity. This new technique has great biomedical potential, especially in exploring new treatment methods for hematological disorders. This review discusses the development of de novo protein design and its biological applications, with emphasis on the treatment of hematological disorders.

Computationally designed GPCR quaternary structures bias signaling pathway activation

Communication across membranes controls critical cellular processes and is achieved by receptors translating extracellular signals into selective cytoplasmic responses. While receptor tertiary structures can be readily characterized, receptor associations into quaternary structures are challenging to study and their implications in signal transduction remain poorly understood. Here, we report a computational approach for predicting receptor self-associations, and designing receptor oligomers with various quaternary structures and signaling properties. Using this approach, we designed chemokine receptor CXCR4 dimers with reprogrammed binding interactions, conformations, and abilities to activate distinct intracellular signaling proteins. In agreement with our predictions, the designed CXCR4s dimerized through distinct conformations and displayed different quaternary structural changes upon activation. Consistent with the active state models, all engineered CXCR4 oligomers activated the G protein Gi, but only specific dimer structures also recruited β-arrestins. Overall, we demonstrate that quaternary structures represent an important unforeseen mechanism of receptor biased signaling and reveal the existence of a bias switch at the dimer interface of several G protein-coupled receptors including CXCR4, mu-Opioid and type-2 Vasopressin receptors that selectively control the activation of G proteins vs β-arrestin-mediated pathways. The approach should prove useful for predicting and designing receptor associations to uncover and reprogram selective cellular signaling functions.

A generic framework for hierarchical de novo protein design

De novo protein design enables the exploration of novel sequences and structures absent from the natural protein universe. De novo design also stands as a stringent test for our understanding of the underlying physical principles of protein folding and may lead to the development of proteins with unmatched functional characteristics. The first fundamental challenge of de novo design is to devise "designable" structural templates leading to sequences that will adopt the predicted fold. Here, we built on the TopoBuilder (TB) de novo design method, to automatically assemble structural templates with native-like features starting from string descriptors that capture the overall topology of proteins. Our framework eliminates the dependency of hand-crafted and fold-specific rules through an iterative, data-driven approach that extracts geometrical parameters from structural tertiary motifs. We evaluated the TopoBuilder framework by designing sequences for a set of five protein folds and experimental characterization revealed that several sequences were folded and stable in solution. The TopoBuilder de novo design framework will be broadly useful to guide the generation of artificial proteins with customized geometries, enabling the exploration of the protein universe.

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.

Protein-protein interaction prediction with deep learning: A comprehensive review

Most proteins perform their biological function by interacting with themselves or other molecules. Thus, one may obtain biological insights into protein functions, disease prevalence, and therapy development by identifying protein-protein interactions (PPI). However, finding the interacting and non-interacting protein pairs through experimental approaches is labour-intensive and time-consuming, owing to the variety of proteins. Hence, protein-protein interaction and protein-ligand binding problems have drawn attention in the fields of bioinformatics and computer-aided drug discovery. Deep learning methods paved the way for scientists to predict the 3-D structure of proteins from genomes, predict the functions and attributes of a protein, and modify and design new proteins to provide desired functions. This review focuses on recent deep learning methods applied to problems including predicting protein functions, protein-protein interaction and their sites, protein-ligand binding, and protein design.

Assembly of transmembrane pores from mirror-image peptides

Tailored transmembrane alpha-helical pores with desired structural and functional versatility have promising applications in nanobiotechnology. Herein, we present a transmembrane pore DpPorA, based on the natural pore PorACj, built from D-amino acid α-helical peptides. Using single-channel current recordings, we show that DpPorA peptides self-assemble into uniform cation-selective pores in lipid membranes and exhibit properties distinct from their L-amino acid counterparts. DpPorA shows resistance to protease and acts as a functional nanopore sensor to detect cyclic sugars, polypeptides, and polymers. Fluorescence imaging reveals that DpPorA forms well-defined pores in giant unilamellar vesicles facilitating the transport of hydrophilic molecules. A second D-amino acid peptide based on the polysaccharide transporter Wza forms transient pores confirming sequence specificity in stable, functional pore formation. Finally, molecular dynamics simulations reveal the specific alpha-helical packing and surface charge conformation of the D-pores consistent with experimental observations. Our findings will aid the design of sophisticated pores for single-molecule sensing related technologies.

Alpha-helix nanopores have a range of potential applications and the inclusion of non-natural amino acids allows for modification. Here, the authors report on the creation of alpha-helix pores using D-amino acids and show the pores formed, have different properties to the L-counterparts and were resistant to proteases.

Statistic Copolymers Working as Growth Factor-Binding Mimics of Fibronectin

Growth factors (GFs) play important roles in biological system and are widely used in tissue regeneration. However, their application is greatly hindered by short in vivo lifetime of GFs. GFs are bound to fibronectin dynamically in the extracellular matrix, which inspired the authors to mimic the GF binding domain of fibronectin and design GF-binding amphiphilic copolymers bearing positive charges. The optimal amino acid polymer can bind to a variety of representative GFs, such as bone morphogenetic protein-2 (BMP-2) and TGF-β1 from the transforming growth factor-β superfamily, PDGF-AA and PDGF-BB from the platelet-derived growth factor family, FGF-10 and FGF-21 from the fibroblast growth factor family, epidermal growth factor from the EGF family and hepatocyte growth factor from the plasminogen-related growth factor family, with binding affinities up to the nanomolar level. 3D scaffolds immobilized with the optimal copolymer enable sustained release of loaded BMP-2 without burst release and significantly enhances the in vivo function of BMP-2 for bone formation. This strategy opens new avenues in designing GF-binding copolymers as synthetic mimics of fibronectin for diverse applications.

Metal ion-regulated assembly of designed modular protein cages

Coiled-coil (CC) dimers are versatile, customizable building modules for the design of diverse protein architectures unknown in nature. Incorporation of dynamic self-assembly, regulated by a selected chemical signal, represents an important challenge in the construction of functional polypeptide nanostructures. Here, we engineered metal binding sites to render an orthogonal set of CC heterodimers Zn(II)-responsive as a generally applicable principle. The designed peptides assemble into CC heterodimers only in the presence of Zn(II) ions, reversibly dissociate by metal ion sequestration, and additionally act as pH switches, with low pH triggering disassembly. The developed Zn(II)-responsive CC set is used to construct programmable folding of CC-based nanostructures, from protein triangles to a two-chain bipyramidal protein cage that closes and opens depending on the metal ion. This demonstrates that dynamic self-assembly can be designed into CC-based protein cages by incorporation of metal ion-responsive CC building modules that act as conformational switches and that could also be used in other contexts.

Assembly of alpha-helical transmembrane pores through an intermediate state

Pore-forming alpha-helical proteins are well known for their dynamic assembly mechanism and it has been challenging to delineate the pore-forming structures in membranes. Previously, attempts have been made to elucidate their assembly mechanism and there is a large gap due to complex pathways by which these membrane-active pores impart their effect. Here we demonstrate a multi-step structural assembly pathway of alpha-helical peptide pores formed by a 37 amino acid synthetic peptide, pPorU, based on the natural porin from Corynebacterium urealyticum using single-channel electrical recordings. More specifically, we report detectable intermediate states during the membrane insertion and pore formation of pPorU. The fully assembled pore exhibited unusually large stable conductance, voltage-dependent gating, and functional blockage by cyclic sugars generally applicable to a range of transmembrane pores. Furthermore, we used rationally designed mutants to understand the role of specific amino acids in the assembly of these peptide pores. Mutant peptides that differ from wild-type peptides produced noisy and unstable intermediate states and low conductance pores, demonstrating sequence specificity in the pore-formation process supported by molecular dynamics simulations. We suggest that our study contributes to understanding the mechanism of action of naturally occurring alpha-helical pore-forming proteins and should be of broad interest to build peptide-based nanopore sensors.

De novo-designed transmembrane domains tune engineered receptor functions

De novo-designed receptor transmembrane domains (TMDs) present opportunities for precise control of cellular receptor functions. We developed a de novo design strategy for generating programmed membrane proteins (proMPs): single-pass α-helical TMDs that self-assemble through computationally defined and crystallographically validated interfaces. We used these proMPs to program specific oligomeric interactions into a chimeric antigen receptor (CAR) that we expressed in mouse primary T cells and found that both in vitro CAR T cell cytokine release and in vivo antitumor activity scaled linearly with the oligomeric state encoded by the receptor TMD, from monomers up to tetramers. All programmed CARs stimulated substantially lower T cell cytokine release relative to the commonly used CD28 TMD, which we show elevated cytokine release through lateral recruitment of the endogenous T cell costimulatory receptor CD28. Precise design using orthogonal and modular TMDs thus provides a new way to program receptor structure and predictably tune activity for basic or applied synthetic biology.

Interactions between Artificial Channel Protein, Water Molecules, and Ions Based on Theoretical Approaches

Contemporary techniques of molecular modeling allow for rational design of several specific classes of artificial proteins. Transmembrane channels are among these classes. A recent successful synthesis of self-assembling, highly symmetrical 12- or 16-helix channels by David Baker’s group prompted us to study interactions between one of these proteins, TMHC6, and low-molecular-weight components of the environment: water molecules and ions. To examine protein stability in a polar environment, molecular dynamics (MD) with classical force fields of the AMBER family was employed. Further characteristics of the chosen interactions were obtained using interaction energy calculations with usage of partially polarizable GFN-FF force field of Spicher and Grimme, symmetry-adapted perturbation theory (SAPT) and atoms in molecules (AIM) approaches for models of residues from the channel entry, crucial for interactions with water molecules and ions. The comparison of the interaction energy values between the gas phase and solvent reaction field gives the quantitative estimation of the strength of the interactions. The energy decomposition via the SAPT method showed that the electrostatics forces play a dominant role in the substructure stabilization. An application of the AIM theory enabled a description of the intermolecular hydrogen bonds and other noncovalent interactions.

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.

Effects of Hydrophobic Residues on the Intracellular Self-Assembly of De Novo Designed Peptide Tags and Their Orthogonality

Protein assemblies forming nano- to micro-sized structures underlie versatile biological events in living systems. For mimicking and engineering these protein assemblies through a bottom-up approach, self-assembling peptides (SAPs) that form nanofibril structures via β-sheets serve as potential practical tags. Nevertheless, the development of SAP tags is still in its infancy, and insight into the relationship between peptide sequences and intracellular self-assembly is limited. In this study, we focused on hydrophobic residues in SAPs and examined the self-assembly of SAP-tagged superfolder GFPs (green fluorescent proteins) in COS-7 cells. Based on XEXK (X; hydrophobic amino acids: F, L, I, V, W, or Y) sequence units, we designed a panel of Xn peptides with different hydrophobic residues (X) and chain lengths (n). We observed that the self-assembly propensity, the size of the assemblies, the influence on protein denaturation, and the subcellular localization differed significantly depending on the hydrophobic amino acid. F9, L9, I7, and V13 peptides formed μm-scaled granules, W13 formed small oligomeric clusters in the cytoplasm, and Y15 formed assemblies in the nucleus. In addition, we investigated the orthogonality of their interaction. Strikingly, W13- and Y15-tagged proteins interacted independently and formed two distinct assemblies in cells. Herein, we have demonstrated the great opportunities for rationalizing artificial protein assemblies and orthogonal structures in an intracellular context using the designed SAPs.

RosettaSurf—A surface-centric computational design approach

Proteins are typically represented by discrete atomic coordinates providing an accessible framework to describe different conformations. However, in some fields proteins are more accurately represented as near-continuous surfaces, as these are imprinted with geometric (shape) and chemical (electrostatics) features of the underlying protein structure. Protein surfaces are dependent on their chemical composition and, ultimately determine protein function, acting as the interface that engages in interactions with other molecules. In the past, such representations were utilized to compare protein structures on global and local scales and have shed light on functional properties of proteins. Here we describe RosettaSurf, a surface-centric computational design protocol, that focuses on the molecular surface shape and electrostatic properties as means for protein engineering, offering a unique approach for the design of proteins and their functions. The RosettaSurf protocol combines the explicit optimization of molecular surface features with a global scoring function during the sequence design process, diverging from the typical design approaches that rely solely on an energy scoring function. With this computational approach, we attempt to address a fundamental problem in protein design related to the design of functional sites in proteins, even when structurally similar templates are absent in the characterized structural repertoire. Surface-centric design exploits the premise that molecular surfaces are, to a certain extent, independent of the underlying sequence and backbone configuration, meaning that different sequences in different proteins may present similar surfaces. We benchmarked RosettaSurf on various sequence recovery datasets and showcased its design capabilities by generating epitope mimics that were biochemically validated. Overall, our results indicate that the explicit optimization of surface features may lead to new routes for the design of functional proteins.

Finely orchestrated protein-protein interactions are at the heart of virtually all fundamental cellular processes. Altering these processes or encoding new functions in proteins has been a long-standing goal in computational protein design. Protein design methods commonly rely on scoring functions that seek to identify amino acid sequences that optimize structural configurations of atoms while minimizing a variety of physics-based and statistical terms. The objectives of the large majority of computational design protocols have been focused on obtaining a predefined structural conformation. However, routinely introducing a functional aspect on designer proteins has been more challenging. Our results suggest that the molecular surface features can be a useful optimization parameter to guide the design process towards functional surfaces that mimic known protein binding sites and interact with their intended targets. Specifically, we demonstrate that our design method can optimize experimental libraries through computational screening, creating a basis for highly specific protein binders, as well as design a potent immunogen that engages with site-specific antibodies. The ability to create proteins with novel functions will be transformative for biomedical applications, providing many opportunities for the design of novel immunogens, protein components for synthetic biology, and other protein-based biotechnologies.

Triggered Assembly of a DNA-Based Membrane Channel

Chemistry is in a powerful position to synthetically replicate biomolecular structures. Adding functional complexity is key to increase the biomimetics' value for science and technology yet is difficult to achieve with poorly controlled building materials. Here, we use defined DNA blocks to rationally design a triggerable synthetic nanopore that integrates multiple functions of biological membrane proteins. Soluble triggers bind via molecular recognition to the nanopore components changing their structure and membrane position, which controls the assembly into a defined channel for efficient transmembrane cargo transport. Using ensemble, single-molecule, and simulation analysis, our activatable pore provides insight into the kinetics and structural dynamics of DNA assembly at the membrane interface. The triggered channel advances functional DNA nanotechnology and synthetic biology and will guide the design of controlled nanodevices for sensing, cell biological research, and drug delivery.

Protein Nanoparticles: Uniting the Power of Proteins with Engineering Design Approaches

Protein nanoparticles, PNPs, have played a long-standing role in food and industrial applications. More recently, their potential in nanomedicine has been more widely pursued. This review summarizes recent trends related to the preparation, application, and chemical construction of nanoparticles that use proteins as major building blocks. A particular focus has been given to emerging trends related to applications in nanomedicine, an area of research where PNPs are poised for major breakthroughs as drug delivery carriers, particle-based therapeutics or for non-viral gene therapy.

Angstrom-scale ion channels towards single-ion selectivity

Artificial ion channels with ion permeability and selectivity comparable to their biological counterparts are highly desired for efficient separation, biosensing, and energy conversion technologies. In the past two decades, both nanoscale and sub-nanoscale ion channels have been successfully fabricated to mimic biological ion channels. Although nanoscale ion channels have achieved intelligent gating and rectification properties, they cannot realize high ion selectivity, especially single-ion selectivity. Artificial angstrom-sized ion channels with narrow pore sizes <1 nm and well-defined pore structures mimicking biological channels have accomplished high ion conductivity and single-ion selectivity. This review comprehensively summarizes the research progress in the rational design and synthesis of artificial subnanometer-sized ion channels with zero-dimensional to three-dimensional pore structures. Then we discuss cation/anion, mono-/di-valent cation, mono-/di-valent anion, and single-ion selectivities of the synthetic ion channels and highlight their potential applications in high-efficiency ion separation, energy conversion, and biological therapeutics. The gaps of single-ion selectivity between artificial and natural channels and the connections between ion selectivity and permeability of synthetic ion channels are covered. Finally, the challenges that need to be addressed in this research field and the perspective of angstrom-scale ion channels are discussed.

The stability and dynamics of computationally designed proteins

Protein stability, dynamics and function are intricately linked. Accordingly, protein designers leverage dynamics in their designs and gain insight to their successes and failures by analyzing their proteins' dynamics. Molecular dynamics (MD) simulations are a powerful computational tool for quantifying both local and global protein dynamics. This review highlights studies where MD simulations were applied to characterize the stability and dynamics of designed proteins and where dynamics were incorporated into computational protein design. First, we discuss the structural basis underlying the extreme stability and thermostability frequently observed in computationally designed proteins. Next, we discuss examples of designed proteins, where dynamics were not explicitly accounted for in the design process, whose coordinated motions or active site dynamics, as observed by MD simulation, enhanced or detracted from their function. Many protein functions depend on sizeable or subtle conformational changes, so we finally discuss the computational design of proteins to perform a specific function that requires consideration of motion by multi-state design.

Protein sequence design with a learned potential

The task of protein sequence design is central to nearly all rational protein engineering problems, and enormous effort has gone into the development of energy functions to guide design. Here, we investigate the capability of a deep neural network model to automate design of sequences onto protein backbones, having learned directly from crystal structure data and without any human-specified priors. The model generalizes to native topologies not seen during training, producing experimentally stable designs. We evaluate the generalizability of our method to a de novo TIM-barrel scaffold. The model produces novel sequences, and high-resolution crystal structures of two designs show excellent agreement with in silico models. Our findings demonstrate the tractability of an entirely learned method for protein sequence design.

Rational protein design to achieve a given protein backbone conformation is needed to engineer specific functions. Here Anand et al. describe a machine learning method using a learned neural network potential for fixed-backbone protein design.

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.

CoCoPRED: coiled-coil protein structural feature prediction from amino acid sequence using deep neural networks

MOTIVATION

Coiled-coil is composed of two or more helices that are wound around each other. It widely exists in proteins and has been discovered to play a variety of critical roles in biology processes. Generally, there are three types of structural features in coiled-coil: coiled-coil domain (CCD), oligomeric state and register. However, most of the existing computational tools only focus on one of them.

RESULTS

Here, we describe a new deep learning model, CoCoPRED, which is based on convolutional layers, bidirectional long short-term memory, and attention mechanism. It has three networks, i.e. CCD network, oligomeric state network, and register network, corresponding to the three types of structural features in coiled-coil. This means CoCoPRED has the ability of fulfilling comprehensive prediction for coiled-coil proteins. Through the 5-fold cross-validation experiment, we demonstrate that CoCoPRED can achieve better performance than the state-of-the-art models on both CCD prediction and oligomeric state prediction. Further analysis suggests the CCD prediction may be a performance indicator of the oligomeric state prediction in CoCoPRED. The attention heads in CoCoPRED indicate that registers a, b and e are more crucial for the oligomeric state prediction.

AVAILABILITY AND IMPLEMENTATION

CoCoPRED is available at http://www.csbio.sjtu.edu.cn/bioinf/CoCoPRED. The datasets used in this research can also be downloaded from the website.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

From peptides to proteins: coiled-coil tetramers to single-chain 4-helix bundles

The design of completely synthetic proteins from first principles—de novo protein design—is challenging. This is because, despite recent advances in computational protein–structure prediction and design, we do not understand fully the sequence-to-structure relationships for protein folding, assembly, and stabilization. Antiparallel 4-helix bundles are amongst the most studied scaffolds for de novo protein design. We set out to re-examine this target, and to determine clear sequence-to-structure relationships, or design rules, for the structure. Our aim was to determine a common and robust sequence background for designing multiple de novo 4-helix bundles. In turn, this could be used in chemical and synthetic biology to direct protein–protein interactions and as scaffolds for functional protein design. Our approach starts by analyzing known antiparallel 4-helix coiled-coil structures to deduce design rules. In terms of the heptad repeat, abcdefg—i.e., the sequence signature of many helical bundles—the key features that we identify are: a = Leu, d = Ile, e = Ala, g = Gln, and the use of complementary charged residues at b and c. Next, we implement these rules in the rational design of synthetic peptides to form antiparallel homo- and heterotetramers. Finally, we use the sequence of the homotetramer to derive in one step a single-chain 4-helix-bundle protein for recombinant production in E. coli. All of the assembled designs are confirmed in aqueous solution using biophysical methods, and ultimately by determining high-resolution X-ray crystal structures. Our route from peptides to proteins provides an understanding of the role of each residue in each design.

Rules for designing 4-helix bundles are defined, tested, and used to generate de novo peptide assemblies and a single-chain protein.

Spiers Memorial Lecture: Analysis and de novo design of membrane-interactive peptides

Membrane-peptide interactions play critical roles in many cellular and organismic functions, including protection from infection, remodeling of membranes, signaling, and ion transport. Peptides interact with membranes in a variety of ways: some associate with membrane surfaces in either intrinsically disordered conformations or well-defined secondary structures. Peptides with sufficient hydrophobicity can also insert vertically as transmembrane monomers, and many associate further into membrane-spanning helical bundles. Indeed, some peptides progress through each of these stages in the process of forming oligomeric bundles. In each case, the structure of the peptide and the membrane represent a delicate balance between peptide-membrane and peptide-peptide interactions. We will review this literature from the perspective of several biologically important systems, including antimicrobial peptides and their mimics, α-synuclein, receptor tyrosine kinases, and ion channels. We also discuss the use of de novo design to construct models to test our understanding of the underlying principles and to provide useful leads for pharmaceutical intervention of diseases.

Mechanism of negative membrane curvature generation by I-BAR domains

The membrane sculpting ability of BAR domains has been attributed to the intrinsic curvature of their banana-shaped dimeric structure. However, there is often a mismatch between this intrinsic curvature and the diameter of the membrane tubules generated. I-BAR domains are especially mysterious since they are almost flat but generate high negative membrane curvature. Here, we use atomistic implicit-solvent computer modeling to show that the membrane bending of the IRSp53 I-BAR domain is dictated by its higher oligomeric structure, whose curvature is completely unrelated to the intrinsic curvature of the dimer. Two other I-BARs give similar results, whereas a flat F-BAR sheet develops a concave membrane-binding interface, consistent with its observed positive membrane curvature generation. Laterally interacting helical spirals of I-BAR dimers on tube interiors are stable and have an enhanced binding energy that is sufficient for membrane bending to experimentally observed tubule diameters at a reasonable surface density.

Bottom-up fabrication of a proteasome-nanopore that unravels and processes single proteins

The precise assembly and engineering of molecular machines capable of handling biomolecules play crucial roles in most single-molecule methods. In this work we use components from all three domains of life to fabricate an integrated multiprotein complex that controls the unfolding and threading of individual proteins across a nanopore. This 900 kDa multicomponent device was made in two steps. First, we designed a stable and low-noise β-barrel nanopore sensor by linking the transmembrane region of bacterial protective antigen to a mammalian proteasome activator. An archaeal 20S proteasome was then built into the artificial nanopore to control the unfolding and linearized transport of proteins across the nanopore. This multicomponent molecular machine opens the door to two approaches in single-molecule protein analysis, in which selected substrate proteins are unfolded, fed to into the proteasomal chamber and then addressed either as fragmented peptides or intact polypeptides.

Transforming de novo protein α3D into a mechanically stable protein by zinc binding

α₃D is a de novo designed three-helix bundle protein. Like most naturally occurring helical proteins, it is mechanically labile with an unfolding force of <15 pN, revealed by atomic force microscopy-based single-molecule force spectroscopy (AFM-SMFS). This protein has been further designed with a tri-cysteine metal-binding site, named α₃DIV, which can bind heavy transition metals. Here, we demonstrate that incorporating such a metal-binding site can transform this mechanically labile protein into a stable one. We show that zinc binds to the tri-cysteine site and increases the unfolding force to ∼160 pN. This force is one order of magnitude higher than that of the apo-protein (<15 pN). Moreover, the unfolding mechanism of Zn-α₃DIV indicates the correct zinc binding with the tri-cysteine site, forming three mechanostable Zn-thiolate bonds. Thus, α₃DIV could be a potential α-helical structure-based building block for synthesizing biomaterials with tunable mechanical properties.

Principles of Small-Molecule Transport through Synthetic Nanopores

Synthetic nanopores made from DNA replicate the key biological processes of transporting molecular cargo across lipid bilayers. Understanding transport across the confined lumen of the nanopores is of fundamental interest and of relevance to their rational design for biotechnological applications. Here we reveal the transport principles of organic molecules through DNA nanopores by synergistically combining experiments and computer simulations. Using a highly parallel nanostructured platform, we synchronously measure the kinetic flux across hundreds of individual pores to obtain rate constants. The single-channel transport kinetics are close to the theoretical maximum, while selectivity is determined by the interplay of cargo charge and size, the pores' sterics and electrostatics, and the composition of the surrounding lipid bilayer. The narrow distribution of transport rates implies a high structural homogeneity of DNA nanopores. The molecular passageway through the nanopore is elucidated via coarse-grained constant-velocity steered molecular dynamics simulations. The ensemble simulations pinpoint with high resolution and statistical validity the selectivity filter within the channel lumen and determine the energetic factors governing transport. Our findings on these synthetic pores' structure-function relationship will serve to guide their rational engineering to tailor transport selectivity for cell biological research, sensing, and drug delivery.