Bottom-up de novo design of functional proteins with complex structural features

Enzyme-Triggered Assembly of Glycan Nanomaterials

A comprehensive molecular understanding of carbohydrate aggregation is key to optimize carbohydrate utilization and to engineer bioinspired analogues with tailored shapes and properties. However, the lack of well-defined synthetic standards has substantially hampered advances in this field. Herein, we employ a phosphorylation-assisted strategy to synthesize previously inaccessible long oligomers of cellulose, chitin, and xylan. These oligomers were subjected to enzyme-triggered assembly (ETA) for the on-demand formation of well-defined carbohydrate nanomaterials, including elongated platelets, helical bundles, and hexagonal particles. Cryo-electron microscopy and electron diffraction analysis provided molecular insights into the aggregation behavior of these oligosaccharides, establishing a direct connection between the resulting morphologies and the oligosaccharide primary sequence. Our findings demonstrate that ETA is a powerful approach to elucidate the intrinsic aggregation behavior of carbohydrates in nature. Moreover, the ability to access a diverse array of morphologies, expanded with a non-natural sequence, underscores the potential of ETA, coupled with sequence design, as a robust tool for accessing programmable glycan architectures.

AI Prediction of Structural Stability of Nanoproteins Based on Structures and Residue Properties by Mean Pooled Dual Graph Convolutional Network

The structural stability of proteins is an important topic in various fields such as biotechnology, pharmaceuticals, and enzymology. Specifically, understanding the structural stability of protein is crucial for protein design. Artificial design, while pursuing high thermodynamic stability and rigidity of proteins, inevitably sacrifices biological functions closely related to protein flexibility. The thermodynamic stability of proteins is not always optimal when they are highest to perfectly perform their biological functions. Extensive theoretical and experimental screening is often required to obtain stable protein structures. Thus, it becomes critically important to develop a stability prediction model based on the balance between protein stability and bioactivity. To design protein drugs with better functionality in a broader structural space, a novel protein structural stability predictor called PSSP has been developed in this study. PSSP is a mean pooled dual graph convolutional network (GCN) model based on sequence characteristics and secondary structure, distance matrix, graph, and residue properties of a nanoprotein to provide rapid prediction and judgment. This model exhibits excellent robustness in predicting the structural stability of nanoproteins. Comparing with previous artificial intelligence algorithms, the results indicate this model can provide a rapid and accurate assessment of the structural stability of artificially designed proteins, which shows the great promises for promoting the robust development of protein design.

Chagas Disease Diagnosis with Trypanosoma cruzi-Exclusive Epitopes in GFP

Serological tests are critical tools in the fight against infectious disease. They detect antibodies produced during an adaptive immune response against a pathogen with an immunological reagent, whose antibody binding characteristics define the specificity and sensitivity of the assay. While pathogen proteins have conveniently served as reagents, their performance is limited by the natural grouping of specific and non-specific antibody binding sites, epitopes. An attractive solution is to build synthetic proteins that only contains pathogen-specific epitopes, which could theoretically reach 100% specificity. However, the genesis of de novo proteins remains a challenge. To address the uncertainty of producing a synthetic protein, we have repurposed the beta barrel of fluorescent proteins into a receptacle that can receive several epitope sequences without compromising its ability to be expressed. Here, two versions of a multiepitope protein were built using the receptacle that differ by their grouping of epitopes specific to the parasite Trypanosoma cruzi, the causative agent for Chagas disease. An evaluation of their performance as the capture reagent in ELISAs showed near-complete agreement with recommended diagnostic protocols. The results suggest that a single assay could be developed for the diagnosis of Chagas disease and that this approach could be applied to other diseases.

Modulation of FGF pathway signaling and vascular differentiation using designed oligomeric assemblies

Many growth factors and cytokines signal by binding to the extracellular domains of their receptors and driving association and transphosphorylation of the receptor intracellular tyrosine kinase domains, initiating downstream signaling cascades. To enable systematic exploration of how receptor valency and geometry affect signaling outcomes, we designed cyclic homo-oligomers with up to 8 subunits using repeat protein building blocks that can be modularly extended. By incorporating a de novo-designed fibroblast growth factor receptor (FGFR)-binding module into these scaffolds, we generated a series of synthetic signaling ligands that exhibit potent valency- and geometry-dependent Ca2+ release and mitogen-activated protein kinase (MAPK) pathway activation. The high specificity of the designed agonists reveals distinct roles for two FGFR splice variants in driving arterial endothelium and perivascular cell fates during early vascular development. Our designed modular assemblies should be broadly useful for unraveling the complexities of signaling in key developmental transitions and for developing future therapeutic applications.

Protein Design Using Structure-Prediction Networks: AlphaFold and RoseTTAFold as Protein Structure Foundation Models

Designing proteins with tailored structures and functions is a long-standing goal in bioengineering. Recently, deep learning advances have enabled protein structure prediction at near-experimental accuracy, which has catalyzed progress in protein design as well. We review recent studies that use structure-prediction neural networks to design proteins, via approaches such as activation maximization, inpainting, or denoising diffusion. These methods have led to major improvements over previous methods in wet-lab success rates for designing protein binders, metalloproteins, enzymes, and oligomeric assemblies. These results show that structure-prediction models are a powerful foundation for developing protein-design tools and suggest that continued improvement of their accuracy and generality will be key to unlocking the full potential of protein design.

Diversifying de novo TIM barrels by hallucination

De novo protein design expands the protein universe by creating new sequences to accomplish tailor-made enzymes in the future. A promising topology to implement diverse enzyme functions is the ubiquitous TIM-barrel fold. Since the initial de novo design of an idealized four-fold symmetric TIM barrel, the family of de novo TIM barrels is expanding rapidly. Despite this and in contrast to natural TIM barrels, these novel proteins lack cavities and structural elements essential for the incorporation of binding sites or enzymatic functions. In this work, we diversified a de novo TIM barrel by extending multiple βα-loops using constrained hallucination. Experimentally tested designs were found to be soluble upon expression in Escherichia coli and well-behaved. Biochemical characterization and crystal structures revealed successful extensions with defined α-helical structures. These diversified de novo TIM barrels provide a framework to explore a broad spectrum of functions based on the potential of natural TIM barrels.

Click, Compute, Create: A Review of Web-based Tools for Enzyme Engineering

Enzyme engineering, though pivotal across various biotechnological domains, is often plagued by its time-consuming and labor-intensive nature. This review aims to offer an overview of supportive in silico methodologies for this demanding endeavor. Starting from methods to predict protein structures, to classification of their activity and even the discovery of new enzymes we continue with describing tools used to increase thermostability and production yields of selected targets. Subsequently, we discuss computational methods to modulate both, the activity as well as selectivity of enzymes. Last, we present recent approaches based on cutting-edge machine learning methods to redesign enzymes. With exception of the last chapter, there is a strong focus on methods easily accessible via web-interfaces or simple Python-scripts, therefore readily useable for a diverse and broad community.

Machine learning for functional protein design

Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and structure data have radically transformed computational protein design. New methods promise to escape the constraints of natural and laboratory evolution, accelerating the generation of proteins for applications in biotechnology and medicine. To make sense of the exploding diversity of machine learning approaches, we introduce a unifying framework that classifies models on the basis of their use of three core data modalities: sequences, structures and functional labels. We discuss the new capabilities and outstanding challenges for the practical design of enzymes, antibodies, vaccines, nanomachines and more. We then highlight trends shaping the future of this field, from large-scale assays to more robust benchmarks, multimodal foundation models, enhanced sampling strategies and laboratory automation.

TIMED-Design: flexible and accessible protein sequence design with convolutional neural networks

Sequence design is a crucial step in the process of designing or engineering proteins. Traditionally, physics-based methods have been used to solve for optimal sequences, with the main disadvantages being that they are computationally intensive for the end user. Deep learning-based methods offer an attractive alternative, outperforming physics-based methods at a significantly lower computational cost. In this paper, we explore the application of Convolutional Neural Networks (CNNs) for sequence design. We describe the development and benchmarking of a range of networks, as well as reimplementations of previously described CNNs. We demonstrate the flexibility of representing proteins in a three-dimensional voxel grid by encoding additional design constraints into the input data. Finally, we describe TIMED-Design, a web application and command line tool for exploring and applying the models described in this paper. The user interface will be available at the URL: https://pragmaticproteindesign.bio.ed.ac.uk/timed. The source code for TIMED-Design is available at https://github.com/wells-wood-research/timed-design.

Therapeutic drug monitoring of immunotherapies with novel Affimer-NanoBiT sensor construct

Concentration-therapeutic efficacy relationships have been observed for several therapeutic monoclonal antibodies (TmAb), where low circulating levels can result in ineffective treatment and high concentrations can cause adverse reactions. Rapid therapeutic drug monitoring (TDM) of TmAb drugs would provide the opportunity to adjust an individual patient's dosing regimen to improve treatment results. However, TDM for immunotherapies is currently limited to centralised testing methods with long sample-collection to result timeframes. Here, we show four point-of-care (PoC) TmAb biosensors by combining anti-idiotypic Affimer proteins and NanoBiT split luciferase technology at a molecular level to provide a platform for rapid quantification (<10 minutes) for four clinically relevant TmAb (rituximab, adalimumab, ipilimumab and trastuzumab). The rituximab sensor performed best with 4 pM limit of detection (LoD) and a quantifiable range between 8 pM-2 nM with neglectable matrix effects in serum up to 1%. After dilution of serum samples, the resulting quantifiable range for all four sensors falls within the clinically relevant range and compares favourably with the sensitivity and/or time-to-result of current ELISA standards. Further development of these sensors into a PoC test may improve treatment outcome and quality of life for patients receiving immunotherapy.

Computational Protein Design - Where it goes?

Proteins have been playing a critical role in the regulation of diverse biological processes related to human life. With the increasing demand, functional proteins are sparse in this immense sequence space. Therefore, protein design has become an important task in various fields, including medicine, food, energy, materials, etc. Directed evolution has recently led to significant achievements. Molecular modification of proteins through directed evolution technology has significantly advanced the fields of enzyme engineering, metabolic engineering, medicine, and beyond. However, it is impossible to identify desirable sequences from a large number of synthetic sequences alone. As a result, computational methods, including data-driven machine learning and physics-based molecular modeling, have been introduced to protein engineering to produce more functional proteins. This review focuses on recent advances in computational protein design, highlighting the applicability of different approaches as well as their limitations.

Engineering a scalable and orthogonal platform for synthetic communication in mammalian cells

The rational design and implementation of synthetic mammalian communication systems can unravel fundamental design principles of cell communication circuits and offer a framework for engineering of designer cell consortia with potential applications in cell therapeutics. Here, we develop the foundations of an orthogonal, and scalable mammalian synthetic communication platform that exploits the programmability of synthetic receptors and selective affinity and tunability of diffusing coiled-coil peptides. Leveraging the ability of coiled-coils to exclusively bind to a cognate receptor, we demonstrate orthogonal receptor activation and Boolean logic operations at the receptor level. We show intercellular communication based on synthetic receptors and secreted multidomain coiled-coils and demonstrate a three-cell population system that can perform AND gate logic. Finally, we show CC-GEMS receptor-dependent therapeutic protein expression. Our work provides a modular and scalable framework for the engineering of complex cell consortia, with the potential to expand the aptitude of cell therapeutics and diagnostics.

Mn-porphyrins in a four-helix bundle participate in photo-induced electron transfer with a bacterial reaction center

Hybrid complexes incorporating synthetic Mn-porphyrins into an artificial four-helix bundle domain of bacterial reaction centers created a system to investigate new electron transfer pathways. The reactions were initiated by illumination of the bacterial reaction centers, whose primary photochemistry involves electron transfer from the bacteriochlorophyll dimer through a series of electron acceptors to the quinone electron acceptors. Porphyrins with diphenyl, dimesityl, or fluorinated substituents were synthesized containing either Mn or Zn. Electrochemical measurements revealed potentials for Mn(III)/Mn(II) transitions that are ~ 0.4 V higher for the fluorinated Mn-porphyrins than the diphenyl and dimesityl Mn-porphyrins. The synthetic porphyrins were introduced into the proteins by binding to a four-helix bundle domain that was genetically fused to the reaction center. Light excitation of the bacteriochlorophyll dimer of the reaction center resulted in new derivative signals, in the 400 to 450 nm region of light-minus-dark spectra, that are consistent with oxidation of the fluorinated Mn(II) porphyrins and reduction of the diphenyl and dimesityl Mn(III) porphyrins. These features recovered in the dark and were not observed in the Zn(II) porphyrins. The amplitudes of the signals were dependent upon the oxidation/reduction midpoint potentials of the bacteriochlorophyll dimer. These results are interpreted as photo-induced charge-separation processes resulting in redox changes of the Mn-porphyrins, demonstrating the utility of the hybrid artificial reaction center system to establish design guidelines for novel electron transfer reactions.

Hyper Thermostability and Liquid-Crystal-Like Properties of Designed α-Helical Peptide Nanofibers

Structural and thermodynamic transitions of artificially designed α-helical nanofibers were investigated using eight peptide variants, including four peptides with amide-modified carboxyl termini (CB peptides) and four unmodified peptides (CF peptides). Temperature-dependent circular dichroism spectroscopy and differential scanning calorimetry showed that CB peptides exhibit thermostability up to 50 °C higher than CF peptides. As a result, one of the denaturation temperatures approached nearly 130 °C, which is exceptionally high for a biomacromolecule. Thermodynamic analysis and microscopy observations also showed that CB peptides undergo a thermal transition similar to the phase transition in liquid crystals. In addition, one of the peptides showed a sharp and highly cooperative transition with a small enthalpy change at around 25 °C, which was ascribed to a giga-bundle burst of the molecular assembly. These macroscopic changes in the thermostability and crystallinity of CB peptides may be attributed to an increased amphiphilicity of the molecule in the direction of the helix axis, originating from the microscopic modification of the carboxyl-terminus.

Rational design of small-molecule responsive protein switches

Small-molecule responsive protein switches are powerful tools for controlling cellular processes. These switches are designed to respond rapidly and specifically to their inducer. They have been used in numerous applications, including the regulation of gene expression, post-translational protein modification, and signal transduction. Typically, small-molecule responsive protein switches consist of two proteins that interact with each other in the presence or absence of a small molecule. Recent advances in computational protein design already contributed to the development of protein switches with an expanded range of small-molecule inducers and increasingly sophisticated switch mechanisms. Further progress in the engineering of small-molecule responsive switches is fueled by cutting-edge computational design approaches, which will enable more complex and precise control over cellular processes and advance synthetic biology applications in biotechnology and medicine. Here, we discuss recent milestones and how technological advances are impacting the development of chemical switches.

De novo design of protein structure and function with RFdiffusion

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

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.

De Novo Design and Synthesis of Polypeptide Immunomodulators for Resetting Macrophage Polarization

Modulating the extracellular matrix microenvironment is critical for achieving the desired macrophage phenotype in immune investigations or tumor therapy. Combining de novo protein design and biosynthesis techniques, herein, we designed a biomimetic polypeptide self-assembled nano-immunomodulator to trigger the activation of a specific macrophage phenotype. It was intended to be made up of (​GGS​GGP​GGG​PAS​AAA​NSA​SRA​TSN​SP)n, the RGD motif from collagen, and the IKVAV motif from laminin. The combination of these domains allows the biomimetic polypeptide to assemble into extracellular matrix-like nanofibrils, creating an extracellular matrix-like milieu for macrophages. Furthermore, changing the concentration further provides a facile route to fine-tune macrophage polarization, which enhances antitumor immune responses by precisely resetting tumor-associated macrophage immune responses into an M1-like phenotype, which is generally considered to be tumor-killing macrophages, primarily antitumor, and immune-promoting. Unlike metal or synthetic polymer-based nanoparticles, this polypeptide-based nanomaterial exhibits excellent biocompatibility, high efficacy, and precise tunability in immunomodulatory effectiveness. These encouraging findings motivate us to continue our research into cancer immunotherapy applications in the future.

Differential sensing with arrays of de novo designed peptide assemblies

Differential sensing attempts to mimic the mammalian senses of smell and taste to identify analytes and complex mixtures. In place of hundreds of complex, membrane-bound G-protein coupled receptors, differential sensors employ arrays of small molecules. Here we show that arrays of computationally designed de novo peptides provide alternative synthetic receptors for differential sensing. We use self-assembling α-helical barrels (αHBs) with central channels that can be altered predictably to vary their sizes, shapes and chemistries. The channels accommodate environment-sensitive dyes that fluoresce upon binding. Challenging arrays of dye-loaded barrels with analytes causes differential fluorophore displacement. The resulting fluorimetric fingerprints are used to train machine-learning models that relate the patterns to the analytes. We show that this system discriminates between a range of biomolecules, drink, and diagnostically relevant biological samples. As αHBs are robust and chemically diverse, the system has potential to sense many analytes in various settings.

Physics-based approach to extend a de novo TIM barrel with rationally designed helix-loop-helix motifs

Computational protein design promises the ability to build tailor-made proteins de novo. While a range of de novo proteins have been constructed so far, the majority of these designs have idealized topologies that lack larger cavities which are necessary for the incorporation of small molecule binding sites or enzymatic functions. One attractive target for enzyme design is the TIM-barrel fold, due to its ubiquity in nature and capability to host versatile functions. With the successful de novo design of a 4-fold symmetric TIM barrel, sTIM11, an idealized, minimalistic scaffold was created. In this work, we attempted to extend this de novo TIM barrel by incorporating a helix-loop-helix motif into its βα-loops by applying a physics-based modular design approach using Rosetta. Further diversification was performed by exploiting the symmetry of the scaffold to integrate two helix-loop-helix motifs into the scaffold. Analysis with AlphaFold2 and biochemical characterization demonstrate the formation of additional α-helical secondary structure elements supporting the successful extension as intended.

Bioluminescence Goes Dark: Boosting the Performance of Bioluminescent Sensor Proteins Using Complementation Inhibitors

Bioluminescent sensor proteins have recently gained popularity in both basic research and point-of-care diagnostics. Sensor proteins based on intramolecular complementation of split NanoLuc are particularly attractive because their intrinsic modular design enables for systematic tuning of sensor properties. Here we show how the sensitivity of these sensors can be enhanced by the introduction of catalytically inactive variants of the small SmBiT subunit (DarkBiTs) as intramolecular inhibitors. Starting from previously developed bioluminescent antibody sensor proteins (LUMABS), we developed single component, biomolecular switches with a strongly reduced background signal for the detection of three clinically relevant antibodies, anti-HIV1-p17, cetuximab (CTX), and an RSV neutralizing antibody (101F). These new dark-LUMABS sensors showed 5-13-fold increases in sensitivity which translated into lower limits of detection. The use of DarkBiTs as competitive intramolecular inhibitor domains is not limited to the LUMABS sensor family and might be used to boost the performance of other bioluminescent sensor proteins based on split luciferase complementation.

Characterization of recombinant humanized collagen type III and its influence on cell behavior and phenotype

Collagen made a tremendous impact in the field of regenerative medicine as a bioactive material. For decades, collagen has been used not only as a scaffolding material but also as an active component in regulating cells' biological behavior and phenotype. However, animal-derived collagen as a major source suffered from problems of immunogenicity, risk of viral infection, and the unclear relationship between bioactive sequence and function. Recombinant humanized collagen (rhCol) provided alternatives for regenerative medicine with more controllable risks. However, the characterization of rhCol and the interaction between rhCol and cells still need further investigation, including cell behavior and phenotype. The current study preliminarily demonstrated that recombinant humanized collagen type III (rhCol III) conformed to the theoretical amino acid sequence and had an advanced structure resembling bovine collagen. Furthermore, rhCol III could facilitate basal biological behaviors of human skin fibroblasts, such as adhesion, proliferation and migration. rhCol III was beneficial for some extracellular matrix-expressing cell phenotypes. The study would shed light on the mechanism research of rhCol and cell interactions and further understanding of effectiveness in tissue regeneration.

Graphical abstract

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.

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.

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.

Assessing and enhancing foldability in designed proteins

Recent advances in protein-design methodology have led to a dramatic increase in reliability and scale. With these advances, dozens and even thousands of designed proteins are automatically generated and screened. Nevertheless, the success rate, particularly in design of functional proteins, is low and fundamental goals such as reliable de novo design of efficient enzymes remain beyond reach. Experimental analyses have consistently indicated that a major reason for design failure is inaccuracy and misfolding relative to the design conception. To address this challenge, we describe complementary methods to diagnose and ameliorate suboptimal regions in designed proteins: first, we develop a Rosetta atomistic computational mutation scanning approach to detect energetically suboptimal positions in designs (available on a web server https://pSUFER.weizmann.ac.il); second, we demonstrate that AlphaFold2 ab initio structure prediction flags regions that may misfold in designed enzymes and binders; and third, we focus FuncLib design calculations on suboptimal positions in a previously designed low-efficiency enzyme, improving its catalytic efficiency by 330-fold. Furthermore, applied to a de novo designed protein that exhibited limited stability, the same approach markedly improved stability and expressibility. Thus, foldability analysis and enhancement may dramatically increase the success rate in design of functional proteins.

Scaffolding protein functional sites using deep learning

The binding and catalytic functions of proteins are generally mediated by a small number of functional residues held in place by the overall protein structure. Here, we describe deep learning approaches for scaffolding such functional sites without needing to prespecify the fold or secondary structure of the scaffold. The first approach, "constrained hallucination," optimizes sequences such that their predicted structures contain the desired functional site. The second approach, "inpainting," starts from the functional site and fills in additional sequence and structure to create a viable protein scaffold in a single forward pass through a specifically trained RoseTTAFold network. We use these two methods to design candidate immunogens, receptor traps, metalloproteins, enzymes, and protein-binding proteins and validate the designs using a combination of in silico and experimental tests.

Oligomeric interactions maintain active-site structure in a noncooperative enzyme family

The evolutionary benefit accounting for widespread conservation of oligomeric structures in proteins lacking evidence of intersubunit cooperativity remains unclear. Here, crystal and cryo-EM structures, and enzymological data, demonstrate that a conserved tetramer interface maintains the active-site structure in one such class of proteins, the short-chain dehydrogenase/reductase (SDR) superfamily. Phylogenetic comparisons support a significantly longer polypeptide being required to maintain an equivalent active-site structure in the context of a single subunit. Oligomerization therefore enhances evolutionary fitness by reducing the metabolic cost of enzyme biosynthesis. The large surface area of the structure-stabilizing oligomeric interface yields a synergistic gain in fitness by increasing tolerance to activity-enhancing yet destabilizing mutations. We demonstrate that two paralogous SDR superfamily enzymes with different specificities can form mixed heterotetramers that combine their individual enzymological properties. This suggests that oligomerization can also diversify the functions generated by a given metabolic investment, enhancing the fitness advantage provided by this architectural strategy.

Advances of nanopore-based sensing techniques for contaminants evaluation of food and agricultural products

Food safety assurance systems are becoming more stringent in response to the growing food safety problems. Rapid, sensitive, and reliable detection technology is a prerequisite for the establishment of food safety assurance systems. Nanopore technology has been taken as one of the emerging technology capable of dealing with the detection of harmful contaminants as efficiently as possible due to the advantage of label-free, high-throughput, amplification-free, and rapid detection features. Start with the history of nanopore techniques, this review introduced the underlying knowledge of detection mechanism of nanopore-based sensing techniques. Meanwhile, sensing interfaces for the construction of nanopore sensors are comprehensively summarized. Moreover, this review covers the current advances of nanopore techniques in the application of food safety screening. Currently, the establishment of nanopore sensing devices is mainly based on the blocking current phenomenon. Sensing interfaces including biological nanopores, solid-state nanopores, DNA origami, and de novo designed nanopores can be used in the manufacture of sensing devices. Food harmful substances, including heavy metals, veterinary drugs, pesticide residues, food toxins, and other harmful substances can be quickly determined by nanopore-based sensors. Moreover, the combination of nanopore techniques with advanced materials has become one of the most effective methods to improve sensing properties.

Tertiary motifs as building blocks for the design of protein-binding peptides

Despite advances in protein engineering, the de novo design of small proteins or peptides that bind to a desired target remains a difficult task. Most computational methods search for binder structures in a library of candidate scaffolds, which can lead to designs with poor target complementarity and low success rates. Instead of choosing from pre-defined scaffolds, we propose that custom peptide structures can be constructed to complement a target surface. Our method mines tertiary motifs (TERMs) from known structures to identify surface-complementing fragments or "seeds." We combine seeds that satisfy geometric overlap criteria to generate peptide backbones and score the backbones to identify the most likely binding structures. We found that TERM-based seeds can describe known binding structures with high resolution: the vast majority of peptide binders from 486 peptide-protein complexes can be covered by seeds generated from single-chain structures. Furthermore, we demonstrate that known peptide structures can be reconstructed with high accuracy from peptide-covering seeds. As a proof of concept, we used our method to design 100 peptide binders of TRAF6, seven of which were predicted by Rosetta to form higher-quality interfaces than a native binder. The designed peptides interact with distinct sites on TRAF6, including the native peptide-binding site. These results demonstrate that known peptide-binding structures can be constructed from TERMs in single-chain structures and suggest that TERM information can be applied to efficiently design novel target-complementing binders.

Conformational Entropy as a Potential Liability of Computationally Designed Antibodies

In silico antibody discovery is emerging as a viable alternative to traditional in vivo and in vitro approaches. Many challenges, however, remain open to enabling the properties of designed antibodies to match those produced by the immune system. A major question concerns the structural features of computer-designed complementarity determining regions (CDRs), including the role of conformational entropy in determining the stability and binding affinity of the designed antibodies. To address this problem, we used enhanced-sampling molecular dynamics simulations to compare the free energy landscapes of single-domain antibodies (sdAbs) designed using structure-based (DesAb-HSA-D3) and sequence-based approaches (DesAbO), with that of a nanobody derived from llama immunization (Nb10). Our results indicate that the CDR3 of DesAbO is more conformationally heterogeneous than those of both DesAb-HSA-D3 and Nb10, and the CDR3 of DesAb-HSA-D3 is slightly more dynamic than that of Nb10, which is the original scaffold used for the design of DesAb-HSA-D3. These differences underline the challenges in the rational design of antibodies by revealing the presence of conformational substates likely to have different binding properties and to generate a high entropic cost upon binding.

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.

Algorithms for protein design

Computational Protein Design has the potential to contribute to major advances in enzyme technology, vaccine design, receptor-ligand engineering, biomaterials, nanosensors, and synthetic biology. Although Protein Design is a challenging problem, proteins can be designed by experts in Protein Design, as well as by non-experts whose primary interests are in the applications of Protein Design. The increased accessibility of Protein Design technology is attributable to the accumulated knowledge and experience with Protein Design as well as to the availability of software and online resources. The objective of this review is to serve as a guide to the relevant literature with a focus on the novel methods and algorithms that have been developed or applied for Protein Design, and to assist in the selection of algorithms for Protein Design. Novel algorithms and models that have been introduced to utilize the enormous amount of experimental data and novel computational hardware have the potential for producing substantial increases in the accuracy, reliability and range of applications of designed proteins.

Efficient Large-Scale and Scarless Genome Engineering Enables the Construction and Screening of Bacillus subtilis Biofuel Overproducers

Bacillus subtilis is a versatile microbial cell factory that can produce valuable proteins and value-added chemicals. Long fragment editing techniques are of great importance for accelerating bacterial genome engineering to obtain desirable and genetically stable host strains. Herein, we develop an efficient CRISPR-Cas9 method for large-scale and scarless genome engineering in the Bacillus subtilis genome, which can delete up to 134.3 kb DNA fragments, 3.5 times as long as the previous report, with a positivity rate of 100%. The effects of using a heterologous NHEJ system, linear donor DNA, and various donor DNA length on the engineering efficiencies were also investigated. The CRISPR-Cas9 method was then utilized for Bacillus subtilis genome simplification and construction of a series of individual and cumulative deletion mutants, which are further screened for overproducer of isobutanol, a new generation biofuel. These results suggest that the method is a powerful genome engineering tool for constructing and screening engineered host strains with enhanced capabilities, highlighting the potential for synthetic biology and metabolic engineering.

Advances in Rational Protein Engineering toward Functional Architectures and Their Applications in Food Science

Protein biomolecules including enzymes, cagelike proteins, and specific peptides have been continuously exploited as functional biomaterials applied in catalysis, nutrient delivery, and food preservation in food-related areas. However, natural proteins usually function well in physiological conditions, not industrial conditions, or may possess undesirable physical and chemical properties. Currently, rational protein design as a valuable technology has attracted extensive attention for the rational engineering or fabrication of ideal protein biomaterials with novel properties and functionality. This article starts with the underlying knowledge of protein folding and assembly and is followed by the introduction of the principles and strategies for rational protein design. Basic strategies for rational protein engineering involving experienced protein tailoring, computational prediction, computation redesign, and de novo protein design are summarized. Then, we focus on the recent progress of rational protein engineering or design in the application of food science, and a comprehensive summary ranging from enzyme manufacturing to cagelike protein nanocarriers engineering and antimicrobial peptides preparation is given. Overall, this review highlights the importance of rational protein engineering in food biomaterial preparation which could be beneficial for food science.

Structural validation and assessment of AlphaFold2 predictions for centrosomal and centriolar proteins and their complexes

Obtaining the high-resolution structures of proteins and their complexes is a crucial aspect of understanding the mechanisms of life. Experimental structure determination methods are time-consuming, expensive and cannot keep pace with the growing number of protein sequences available through genomic DNA sequencing. Thus, the ability to accurately predict the structure of proteins from their sequence is a holy grail of structural and computational biology that would remove a bottleneck in our efforts to understand as well as rationally engineer living systems. Recent advances in protein structure prediction, in particular the breakthrough with the AI-based tool AlphaFold2 (AF2), hold promise for achieving this goal, but the practical utility of AF2 remains to be explored. Focusing on proteins with essential roles in centrosome and centriole biogenesis, we demonstrate the quality and usability of the AF2 prediction models and we show that they can provide important insights into the modular organization of two key players in this process, CEP192 and CEP44. Furthermore, we used the AF2 algorithm to elucidate and then experimentally validate previously unknown prime features in the structure of TTBK2 bound to CEP164, as well as the Chibby1-FAM92A complex for which no structural information was available to date. These findings have important implications in understanding the regulation and function of these complexes. Finally, we also discuss some practical limitations of AF2 and anticipate the implications for future research approaches in the centriole/centrosome field.

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.

Interleukin-2 superkines by computational design

While computational engineering of therapeutic proteins is a desirable goal, in practice the optimization of protein–protein interactions requires substantial experimental intervention. We present here a computational approach that focuses on stabilizing core protein structures rather than engineering the protein–protein interface. Using this approach, we designed thermostabilized interleukin-2 (IL-2) variants that bind tightly to their receptor without experimental optimization, mimicking the properties of the yeast-display engineered IL-2 variant “super-2.” Our results suggest that structure-guided stabilization may be a general method for in silico affinity maturation of protein–protein interactions.

Affinity maturation of protein–protein interactions is an important approach in the development of therapeutic proteins such as cytokines. Typical experimental strategies involve targeting the cytokine-receptor interface with combinatorial libraries and then selecting for higher-affinity variants. Mutations to the binding scaffold are usually not considered main drivers for improved affinity. Here we demonstrate that computational design can provide affinity-enhanced variants of interleukin-2 (IL-2) “out of the box” without any requirement for interface engineering. Using a strategy of global IL-2 structural stabilization targeting metastable regions of the three-dimensional structure, rather than the receptor binding interfaces, we computationally designed thermostable IL-2 variants with up to 40-fold higher affinity for IL-2Rβ without any library-based optimization. These IL-2 analogs exhibited CD25-independent activities on T and natural killer (NK) cells both in vitro and in vivo, mimicking the properties of the IL-2 superkine “super-2” that was engineered through yeast surface display [A. M. Levin et al., Nature, 484, 529–533 (2012)]. Structure-guided stabilization of cytokines is a powerful approach to affinity maturation with applications to many cytokine and protein–protein interactions.

Accurate positioning of functional residues with robotics-inspired computational protein design

SignificanceComputational protein design promises to advance applications in medicine and biotechnology by creating proteins with many new and useful functions. However, new functions require the design of specific and often irregular atom-level geometries, which remains a major challenge. Here, we develop computational methods that design and predict local protein geometries with greater accuracy than existing methods. Then, as a proof of concept, we leverage these methods to design new protein conformations in the enzyme ketosteroid isomerase that change the protein's preference for a key functional residue. Our computational methods are openly accessible and can be applied to the design of other intricate geometries customized for new user-defined protein functions.

Designing synthetic transcription factors: A structural perspective

In this chapter, we discuss different design strategies of synthetic proteins, especially synthetic transcription factors. Design and engineering of synthetic transcription factors is particularly relevant for the need-based manipulation of gene expression. With recent advances in structural biology techniques and with the emergence of other precision biochemical/physical tools, accurate knowledge on structure-function relations is increasingly becoming available. Besides discussing the underlying principles of design, we go through individual cases, especially those involving four major groups of transcription factors-basic leucine zippers, zinc fingers, helix-turn-helix and homeodomains. We further discuss how synthetic biology can come together with structural biology to alter the genetic blueprint of life.

De novo metalloprotein design

Natural metalloproteins perform many functions - ranging from sensing to electron transfer and catalysis - in which the position and property of each ligand and metal, is dictated by protein structure. De novo protein design aims to define an amino acid sequence that encodes a specific structure and function, providing a critical test of the hypothetical inner workings of (metallo)proteins. To date, de novo metalloproteins have used simple, symmetric tertiary structures - uncomplicated by the large size and evolutionary marks of natural proteins - to interrogate structure-function hypotheses. In this Review, we discuss de novo design applications, such as proteins that induce complex, increasingly asymmetric ligand geometries to achieve function, as well as the use of more canonical ligand geometries to achieve stability. De novo design has been used to explore how proteins fine-tune redox potentials and catalyse both oxidative and hydrolytic reactions. With an increased understanding of structure-function relationships, functional proteins including O2-dependent oxidases, fast hydrolases, and multi-proton/multi-electron reductases, have been created. In addition, proteins can now be designed using xeno-biological metals or cofactors and principles from inorganic chemistry to derive new-to-nature functions. These results and the advances in computational protein design suggest a bright future for the de novo design of diverse, functional metalloproteins.

Rational Design of Potent Peptide Inhibitors of the PD-1:PD-L1 Interaction for Cancer Immunotherapy

Peptides have potential to be developed into immune checkpoint inhibitors, but the target interfaces are difficult to inhibit. Here, we explored an approach to mimic the binding surface of PD-1 to design inhibitors. Mimicking native PD-1 resulted in a mimetic with no activity. However, mimicking an affinity-optimized PD-1 resulted in the peptide mimetic MOPD-1 that displayed nanomolar affinity to PD-L1 and could inhibit PD-1:PD-L1 interactions in both protein- and cell-based assays. Mutagenesis and structural characterization using NMR spectroscopy and X-ray crystallography revealed that binding residues from the high affinity PD-1 are crucial for the bioactivity of MOPD-1. Furthermore, MOPD-1 was extremely stable in human serum and inhibited tumor growth in vivo, suggesting it has potential for use in cancer immunotherapy. The successful design of an inhibitor of PD-1:PD-L1 using the mimicry approach described herein illustrates the value of placing greater emphasis on optimizing the target interface before inhibitor design and is an approach that could have broader utility for the design of peptide inhibitors for other complex protein-protein interactions.

Engineered Repeat Protein Hybrids: The New Horizon for Biologic Medicines and Diagnostic Tools

ConspectusThe last decades have witnessed unprecedented scientific breakthroughs in all the fields of knowledge, from basic sciences to translational research, resulting in the drastic improvement of the lifespan and overall quality of life. However, despite these great advances, the treatment and diagnosis of some diseases remain a challenge. Inspired by nature, scientists have been exploring biomolecules and their derivatives as novel therapeutic/diagnostic agents. Among biomolecules, proteins raise much interest due to their high versatility, biocompatibility, and biodegradability.Protein binders (binders) are proteins that bind other proteins, in certain cases, inhibiting or modulating their action. Given their therapeutic potential, binders are emerging as the next generation of biopharmaceuticals. The most well-known example of binders are antibodies, and inspired by them researchers have developed alternative binders using protein design approaches. Protein design can be based on naturally occurring proteins in which, by means of rational design or combinatorial approaches, new binding interfaces can be engineered to obtain specific functions or based on de novo proteins emerging from state-of-the-art computational methodologies.Among the novel designed proteins, a class of engineered repeat proteins, the consensus tetratricopeptide repeat (CTPR) proteins, stand out due to their stability and robustness. The CTPR unit is a helix-turn-helix motif constituted of 34 amino acids, of which only 8 are essential to ensure correct folding of the structure. The small number of conserved residues of CTPR proteins leaves plenty of freedom for functional mutations, making them a base scaffold that can be easily and reproducibly tailored to endow desired functions to the protein. For example, the introduction of metal-binding residues (e.g., histidines, cysteines) drives the coordination of metal ions and the subsequent formation of nanomaterials. Additionally, the CTPR unit can be conjugated with other peptides/proteins or repeated in tandem to encode larger CTPR proteins with superhelical structures. These properties allow for the design of both binder and nanomaterial-coordination modules as well as their combination within the same molecule, making the CTPR proteins, as we have demonstrated in several recent examples, the ideal platform to develop protein-nanomaterial hybrids. Generally, the fusion of two distinct materials exploits the best properties of each; however, in protein-nanomaterial hybrids, the fusion takes on a new dimension as new properties arise.These hybrids have ushered the use of protein-based nanomaterials as biopharmaceuticals beyond their original therapeutic scope and paved the way for their use as theranostic agents. Despite several reports of protein-stabilized nanomaterials found in the literature, these systems offer limited control in the synthesis and properties of the grown nanomaterials, as the protein acts just as a stabilizing agent with no significant functional contribution. Therefore, the rational design of protein-based nanomaterials as true theranostic agents is still incipient. In this context, CTPR proteins have emerged as promising scaffolds to hold simultaneously therapeutic and diagnostic functions through protein engineering, as it has been recently demonstrated in pioneering in vitro and in vivo examples.

Multiepitope Proteins for the Differential Detection of IgG Antibodies against RBD of the Spike Protein and Non-RBD Regions of SARS-CoV-2

The COVID-19 pandemic has exposed the extent of global connectivity and collective vulnerability to emerging diseases. From its suspected origins in Wuhan, China, it spread to all corners of the world in a matter of months. The absence of high-performance, rapid diagnostic methods that could identify asymptomatic carriers contributed to its worldwide transmission. Serological tests offer numerous benefits compared to other assay platforms to screen large populations. First-generation assays contain targets that represent proteins from SARS-CoV-2. While they could be quickly produced, each actually has a mixture of specific and non-specific epitopes that vary in their reactivity for antibodies. To generate the next generation of the assay, epitopes were identified in three SARS-Cov-2 proteins (S, N, and Orf3a) by SPOT synthesis analysis. After their similarity to other pathogen sequences was analyzed, 11 epitopes outside of the receptor-binding domain (RBD) of the spike protein that showed high reactivity and uniqueness to the virus. These were incorporated into a ß-barrel protein core to create a highly chimeric protein. Another de novo protein was designed that contained only epitopes in the RBD. In-house ELISAs suggest that both multiepitope proteins can serve as targets for high-performance diagnostic tests. Our approach to bioengineer chimeric proteins is highly amenable to other pathogens and immunological uses.

Dynamics-Evolution Correspondence in Protein Structures

The genotype-phenotype mapping of proteins is a fundamental question in structural biology. In this Letter, with the analysis of a large dataset of proteins from hundreds of protein families, we quantitatively demonstrate the correlations between the noise-induced protein dynamics and mutation-induced variations of native structures, indicating the dynamics-evolution correspondence of proteins. Based on the investigations of the linear responses of native proteins, the origin of such a correspondence is elucidated. It is essential that the noise- and mutation-induced deformations of the proteins are restricted on a common low-dimensional subspace, as confirmed from the data. These results suggest an evolutionary mechanism of the proteins gaining both dynamical flexibility and evolutionary structural variability.

A Brief History of De Novo Protein Design: Minimal, Rational, and Computational

Protein design has come of age, but how will it mature? In the 1980s and the 1990s, the primary motivation for de novo protein design was to test our understanding of the informational aspect of the protein-folding problem; i.e., how does protein sequence determine protein structure and function? This necessitated minimal and rational design approaches whereby the placement of each residue in a design was reasoned using chemical principles and/or biochemical knowledge. At that time, though with some notable exceptions, the use of computers to aid design was not widespread. Over the past two decades, the tables have turned and computational protein design is firmly established. Here, I illustrate this progress through a timeline of de novo protein structures that have been solved to atomic resolution and deposited in the Protein Data Bank. From this, it is clear that the impact of rational and computational design has been considerable: More-complex and more-sophisticated designs are being targeted with many being resolved to atomic resolution. Furthermore, our ability to generate and manipulate synthetic proteins has advanced to a point where they are providing realistic alternatives to natural protein functions for applications both in vitro and in cells. Also, and increasingly, computational protein design is becoming accessible to non-specialists. This all begs the questions: Is there still a place for minimal and rational design approaches? And, what challenges lie ahead for the burgeoning field of de novo protein design as a whole?

Principles and Methods in Computational Membrane Protein Design

After decades of progress in computational protein design, the design of proteins folding and functioning in lipid membranes appears today as the next frontier. Some notable successes in the de novo design of simplified model membrane protein systems have helped articulate fundamental principles of protein folding, architecture and interaction in the hydrophobic lipid environment. These principles are reviewed here, together with the computational methods and approaches that were used to identify them. We provide an overview of the methodological innovations in the generation of new protein structures and functions and in the development of membrane-specific energy functions. We highlight the opportunities offered by new machine learning approaches applied to protein design, and by new experimental characterization techniques applied to membrane proteins. Although membrane protein design is in its infancy, it appears more reachable than previously thought.