De novo design of a fluorescence-activating β-barrel

Recent advances in predicting and modeling protein-protein interactions

Protein-protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial structures of protein complexes are now approaching the accuracy of experimental approaches for permanent interactions and show promise for elucidating transient interactions. As we describe here, the key to this success is rich evolutionary information deciphered from thousands of homologous sequences that coevolve in interacting partners. This covariation signal, revealed by sophisticated statistical and machine learning (ML) algorithms, predicts physiological interactions. Accurate artificial intelligence (AI)-based modeling of protein structures promises to provide accurate 3D models of PPIs at a proteome-wide scale.

β-Sheet Assembly Translates Conservative Single-Site Mutation into a Perturbation in Macroscopic Structure

Transferring structural information from amino acid sequence to macroscale assembly is a challenging approach for designing protein quaternary structure. However, the pathway by which the slight variations in sequence result in a global perturbation effect on the assembled structure is unknown. Herein, we design two synthetic peptides, QNL-His and QNL-Arg, with one amino acid substitution and use scanning tunneling microscopy (STM) to image individual peptides in the assembled state. The submolecular resolution of STM enables us to determine the folding structure and β-sheet supramolecular organization of peptides. QNL-His and QNL-Arg differ in their β-strand length distribution in pleated β-sheet association. These structural variations lead to distinguishable outcomes in their β-sheet assembled fibrils and phase transitions. The comparison of QNL-His versus QNL-Arg structures and macroscopic properties unveils the role of assembly to amplify the structural variations associated with a single-site mutation from a single-molecule scale to a macroscopic scale.

Improving the secretion of designed protein assemblies through negative design of cryptic transmembrane domains

Computationally designed protein nanoparticles have recently emerged as a promising platform for the development of new vaccines and biologics. For many applications, secretion of designed nanoparticles from eukaryotic cells would be advantageous, but in practice, they often secrete poorly. Here we show that designed hydrophobic interfaces that drive nanoparticle assembly are often predicted to form cryptic transmembrane domains, suggesting that interaction with the membrane insertion machinery could limit efficient secretion. We develop a general computational protocol, the Degreaser, to design away cryptic transmembrane domains without sacrificing protein stability. The retroactive application of the Degreaser to previously designed nanoparticle components and nanoparticles considerably improves secretion, and modular integration of the Degreaser into design pipelines results in new nanoparticles that secrete as robustly as naturally occurring protein assemblies. Both the Degreaser protocol and the nanoparticles we describe may be broadly useful in biotechnological applications.

De novo design of small beta barrel proteins

Small beta barrel proteins are attractive targets for computational design because of their considerable functional diversity despite their very small size (<70 amino acids). However, there are considerable challenges to designing such structures, and there has been little success thus far. Because of the small size, the hydrophobic core stabilizing the fold is necessarily very small, and the conformational strain of barrel closure can oppose folding; also intermolecular aggregation through free beta strand edges can compete with proper monomer folding. Here, we explore the de novo design of small beta barrel topologies using both Rosetta energy-based methods and deep learning approaches to design four small beta barrel folds: Src homology 3 (SH3) and oligonucleotide/oligosaccharide-binding (OB) topologies found in nature and five and six up-and-down-stranded barrels rarely if ever seen in nature. Both approaches yielded successful designs with high thermal stability and experimentally determined structures with less than 2.4 Å rmsd from the designed models. Using deep learning for backbone generation and Rosetta for sequence design yielded higher design success rates and increased structural diversity than Rosetta alone. The ability to design a large and structurally diverse set of small beta barrel proteins greatly increases the protein shape space available for designing binders to protein targets of interest.

Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics

Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.

Possibilities of Using De Novo Design for Generating Diverse Functional Food Enzymes

Food enzymes have an important role in the improvement of certain food characteristics, such as texture improvement, elimination of toxins and allergens, production of carbohydrates, enhancing flavor/appearance characteristics. Recently, along with the development of artificial meats, food enzymes have been employed to achieve more diverse functions, especially in converting non-edible biomass to delicious foods. Reported food enzyme modifications for specific applications have highlighted the significance of enzyme engineering. However, using direct evolution or rational design showed inherent limitations due to the mutation rates, which made it difficult to satisfy the stability or specific activity needs for certain applications. Generating functional enzymes using de novo design, which highly assembles naturally existing enzymes, provides potential solutions for screening desired enzymes. Here, we describe the functions and applications of food enzymes to introduce the need for food enzymes engineering. To illustrate the possibilities of using de novo design for generating diverse functional proteins, we reviewed protein modelling and de novo design methods and their implementations. The future directions for adding structural data for de novo design model training, acquiring diversified training data, and investigating the relationship between enzyme-substrate binding and activity were highlighted as challenges to overcome for the de novo design of food enzymes.

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.

De novo design of luciferases using deep learning

De novo enzyme design has sought to introduce active sites and substrate-binding pockets that are predicted to catalyse a reaction of interest into geometrically compatible native scaffolds1,2, but has been limited by a lack of suitable protein structures and the complexity of native protein sequence-structure relationships. Here we describe a deep-learning-based 'family-wide hallucination' approach that generates large numbers of idealized protein structures containing diverse pocket shapes and designed sequences that encode them. We use these scaffolds to design artificial luciferases that selectively catalyse the oxidative chemiluminescence of the synthetic luciferin substrates diphenylterazine3 and 2-deoxycoelenterazine. The designed active sites position an arginine guanidinium group adjacent to an anion that develops during the reaction in a binding pocket with high shape complementarity. For both luciferin substrates, we obtain designed luciferases with high selectivity; the most active of these is a small (13.9 kDa) and thermostable (with a melting temperature higher than 95 °C) enzyme that has a catalytic efficiency on diphenylterazine (kcat/Km = 106 M-1 s-1) comparable to that of native luciferases, but a much higher substrate specificity. The creation of highly active and specific biocatalysts from scratch with broad applications in biomedicine is a key milestone for computational enzyme design, and our approach should enable generation of a wide range of luciferases and other enzymes.

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.

De novo protein fold design through sequence-independent fragment assembly simulations

De novo protein design generally consists of two steps, including structure and sequence design. Many protein design studies have focused on sequence design with scaffolds adapted from native structures in the PDB, which renders novel areas of protein structure and function space unexplored. We developed FoldDesign to create novel protein folds from specific secondary structure (SS) assignments through sequence-independent replica-exchange Monte Carlo (REMC) simulations. The method was tested on 354 non-redundant topologies, where FoldDesign consistently created stable structural folds, while recapitulating on average 87.7% of the SS elements. Meanwhile, the FoldDesign scaffolds had well-formed structures with buried residues and solvent-exposed areas closely matching their native counterparts. Despite the high fidelity to the input SS restraints and local structural characteristics of native proteins, a large portion of the designed scaffolds possessed global folds completely different from natural proteins in the PDB, highlighting the ability of FoldDesign to explore novel areas of protein fold space. Detailed data analyses revealed that the major contributions to the successful structure design lay in the optimal energy force field, which contains a balanced set of SS packing terms, and REMC simulations, which were coupled with multiple auxiliary movements to efficiently search the conformational space. Additionally, the ability to recognize and assemble uncommon super-SS geometries, rather than the unique arrangement of common SS motifs, was the key to generating novel folds. These results demonstrate a strong potential to explore both structural and functional spaces through computational design simulations that natural proteins have not reached through evolution.

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.

Design of Diverse Asymmetric Pockets in De Novo Homo-oligomeric Proteins

A challenge for design of protein-small-molecule recognition is that incorporation of cavities with size, shape, and composition suitable for specific recognition can considerably destabilize protein monomers. This challenge can be overcome through binding pockets formed at homo-oligomeric interfaces between folded monomers. Interfaces surrounding the central homo-oligomer symmetry axes necessarily have the same symmetry and so may not be well suited to binding asymmetric molecules. To enable general recognition of arbitrary asymmetric substrates and small molecules, we developed an approach to designing asymmetric interfaces at off-axis sites on homo-oligomers, analogous to those found in native homo-oligomeric proteins such as glutamine synthetase. We symmetrically dock curved helical repeat proteins such that they form pockets at the asymmetric interface of the oligomer with sizes ranging from several angstroms, appropriate for binding a single ion, to up to more than 20 Å across. Of the 133 proteins tested, 84 had soluble expression in E. coli, 47 had correct oligomeric states in solution, 35 had small-angle X-ray scattering (SAXS) data largely consistent with design models, and 8 had negative-stain electron microscopy (nsEM) 2D class averages showing the structures coming together as designed. Both an X-ray crystal structure and a cryogenic electron microscopy (cryoEM) structure are close to the computational design models. The nature of these proteins as homo-oligomers allows them to be readily built into higher-order structures such as nanocages, and the asymmetric pockets of these structures open rich possibilities for small-molecule binder design free from the constraints associated with monomer destabilization.

Smart genetically-encoded biosensors for the chemical monitoring of living systems

Genetically-encoded biosensors provide the all-optical and non-invasive visualization of dynamic biochemical events within living systems, which has allowed the discovery of profound new insights. Twenty-five years of biosensor development has steadily improved their performance and has provided us with an ever increasing biosensor repertoire. In this feature article, we present recent advances made in biosensor development and provide a perspective on the future direction of the field.

Exploration of Structured Symmetric Cyclic Peptides as Ligands for Metal-Organic Frameworks

Despite remarkable advances in the assembly of highly structured coordination polymers and metal-organic frameworks, the rational design of such materials using more conformationally flexible organic ligands such as peptides remains challenging. In an effort to make the design of such materials fully programmable, we first developed a computational design method for generating metal-mediated 3D frameworks using rigid and symmetric peptide macrocycles with metal-coordinating sidechains. We solved the structures of six crystalline networks involving conformationally constrained 6 to 12 residue cyclic peptides with C2, C3, and S2 internal symmetry and three different types of metals (Zn2+, Co2+, or Cu2+) by single-crystal X-ray diffraction, which reveals how the peptide sequences, backbone symmetries, and metal coordination preferences drive the assembly of the resulting structures. In contrast to smaller ligands, these peptides associate through peptide-peptide interactions without full coordination of the metals, contrary to one of the assumptions underlying our computational design method. The cyclic peptides are the largest peptidic ligands reported to form crystalline coordination polymers with transition metals to date, and while more work is required to develop methods for fully programming their crystal structures, the combination of high chemical diversity with synthetic accessibility makes them attractive building blocks for engineering a broader set of new crystalline materials for use in applications such as sensing, asymmetric catalysis, and chiral separation.

Design and optimization of enzymatic activity in a de novo β-barrel scaffold

While native scaffolds offer a large diversity of shapes and topologies for enzyme engineering, their often unpredictable behavior in response to sequence modification makes de novo generated scaffolds an exciting alternative. Here we explore the customization of the backbone and sequence of a de novo designed eight stranded β‐barrel protein to create catalysts for a retro‐aldolase model reaction. We show that active and specific catalysts can be designed in this fold and use directed evolution to further optimize activity and stereoselectivity. Our results support previous suggestions that different folds have different inherent amenability to evolution and this property could account, in part, for the distribution of natural enzymes among different folds.

Biological Role of the 3β-Corner Structural Motif in Proteins

In this study, we analyze the occurrence of the unique structural motif, the 3β-corner, belonging to the Structural Classification of Proteins (SCOP) folds, in proteins of various origins. We further assess the structural and functional role of this motif as well as the clustering of the biological functions of proteins in which it occurs. It has been shown previously that the 3β-corner occurs with different probabilities in all beta proteins, alpha and beta proteins (α + β and α/β), and alpha classes occur most often in the composition of β-proteins. The 3β-corner is often found as a building block in protein structures, such as β-barrels, -sandwiches, and -sheets/-layers.

Dissecting the stability determinants of a challenging de novo protein fold using massively parallel design and experimentation

Designing entirely new protein structures remains challenging because we do not fully understand the biophysical determinants of folding stability. Yet, some protein folds are easier to design than others. Previous work identified the 43-residue ɑββɑ fold as especially challenging: The best designs had only a 2% success rate, compared to 39 to 87% success for other simple folds [G. J. Rocklin et al., Science 357, 168-175 (2017)]. This suggested the ɑββɑ fold would be a useful model system for gaining a deeper understanding of folding stability determinants and for testing new protein design methods. Here, we designed over 10,000 new ɑββɑ proteins and found over 3,000 of them to fold into stable structures using a high-throughput protease-based assay. NMR, hydrogen-deuterium exchange, circular dichroism, deep mutational scanning, and scrambled sequence control experiments indicated that our stable designs fold into their designed ɑββɑ structures with exceptional stability for their small size. Our large dataset enabled us to quantify the influence of universal stability determinants including nonpolar burial, helix capping, and buried unsatisfied polar atoms, as well as stability determinants unique to the ɑββɑ topology. Our work demonstrates how large-scale design and test cycles can solve challenging design problems while illuminating the biophysical determinants of folding.

De Novo Design of a Highly Stable Ovoid TIM Barrel: Unlocking Pocket Shape towards Functional Design

The ability to finely control the structure of protein folds is an important prerequisite to functional protein design. The TIM barrel fold is an important target for these efforts as it is highly enriched for diverse functions in nature. Although a TIM barrel protein has been designed de novo, the ability to finely alter the curvature of the central beta barrel and the overall architecture of the fold remains elusive, limiting its utility for functional design. Here, we report the de novo design of a TIM barrel with ovoid (twofold) symmetry, drawing inspiration from natural beta and TIM barrels with ovoid curvature. We use an autoregressive backbone sampling strategy to implement our hypothesis for elongated barrel curvature, followed by an iterative enrichment sequence design protocol to obtain sequences which yield a high proportion of successfully folding designs. Designed sequences are highly stable and fold to the designed barrel curvature as determined by a 2.1 Å resolution crystal structure. The designs show robustness to drastic mutations, retaining high melting temperatures even when multiple charged residues are buried in the hydrophobic core or when the hydrophobic core is ablated to alanine. As a scaffold with a greater capacity for hosting diverse hydrogen bonding networks and installation of binding pockets or active sites, the ovoid TIM barrel represents a major step towards the de novo design of functional TIM barrels.

Identifying well-folded de novo proteins in the new era of accurate structure prediction

Computational de novo protein design tailors proteins for target structures and oligomerisation states with high stability, which allows overcoming many limitations of natural proteins when redesigned for new functions. Despite significant advances in the field over the past decade, it remains challenging to predict sequences that will fold as stable monomers in solution or binders to a particular protein target; thereby requiring substantial experimental resources to identify proteins with the desired properties. To overcome this, here we leveraged the large amount of design data accumulated in the last decade, and the breakthrough in protein structure prediction from last year to investigate on improved ways of selecting promising designs before experimental testing. We collected de novo proteins from previous studies, 518 designed as monomers of different folds and 2112 as binders against the Botulinum neurotoxin, and analysed their structures with AlphaFold2, RoseTTAFold and fragment quality descriptors in combination with other properties related to surface interactions. These features showed high complementarity in rationalizing the experimental results, which allowed us to generate quite accurate machine learning models for predicting well-folded monomers and binders with a small set of descriptors. Cross-validating designs with varied orthogonal computational techniques should guide us for identifying design imperfections, rescuing designs and making more robust design selections before experimental testing.

De novo design of immunoglobulin-like domains

Antibodies, and antibody derivatives such as nanobodies, contain immunoglobulin-like (Ig) β-sandwich scaffolds which anchor the hypervariable antigen-binding loops and constitute the largest growing class of drugs. Current engineering strategies for this class of compounds rely on naturally existing Ig frameworks, which can be hard to modify and have limitations in manufacturability, designability and range of action. Here, we develop design rules for the central feature of the Ig fold architecture—the non-local cross-β structure connecting the two β-sheets—and use these to design highly stable Ig domains de novo, confirm their structures through X-ray crystallography, and show they can correctly scaffold functional loops. Our approach opens the door to the design of antibody-like scaffolds with tailored structures and superior biophysical properties.

The immunoglobulin domain framework of antibodies has been a long standing design challenge. Here, the authors describe design rules for tailoring these domains and show they can be accurately designed, de novo, with high stability and the ability to scaffold functional loops.

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.

Computer-Aided Drug Design Boosts RAS Inhibitor Discovery

The Rat Sarcoma (RAS) family (NRAS, HRAS, and KRAS) is endowed with GTPase activity to regulate various signaling pathways in ubiquitous animal cells. As proto-oncogenes, RAS mutations can maintain activation, leading to the growth and proliferation of abnormal cells and the development of a variety of human cancers. For the fight against tumors, the discovery of RAS-targeted drugs is of high significance. On the one hand, the structural properties of the RAS protein make it difficult to find inhibitors specifically targeted to it. On the other hand, targeting other molecules in the RAS signaling pathway often leads to severe tissue toxicities due to the lack of disease specificity. However, computer-aided drug design (CADD) can help solve the above problems. As an interdisciplinary approach that combines computational biology with medicinal chemistry, CADD has brought a variety of advances and numerous benefits to drug design, such as the rapid identification of new targets and discovery of new drugs. Based on an overview of RAS features and the history of inhibitor discovery, this review provides insight into the application of mainstream CADD methods to RAS drug design.

MutDock: A computational docking approach for fixed-backbone protein scaffold design

Despite the successes of antibodies as therapeutic binding proteins, they still face production and design challenges. Alternative binding scaffolds of smaller size have been developed to overcome these issues. A subset of these alternative scaffolds recognizes target molecules through mutations to a set of surface resides, which does not alter their backbone structures. While the computational design of antibodies for target epitopes has been explored in depth, the same has not been done for alternative scaffolds. The commonly used dock-and-mutate approach for binding proteins, including antibodies, is limited because it uses a constant sequence and structure representation of the scaffold. Docking fixed-backbone scaffolds with a varied group of surface amino acids increases the chances of identifying superior starting poses that can be improved with subsequent mutations. In this work, we have developed MutDock, a novel computational approach that simultaneously docks and mutates fixed backbone scaffolds for binding a target epitope by identifying a minimum number of hydrogen bonds. The approach is broadly divided into two steps. The first step uses pairwise distance alignment of hydrogen bond-forming areas of scaffold residues and compatible epitope atoms. This step considers both native and mutated rotamers of scaffold residues. The second step mutates clashing variable interface residues and thermodynamically unfavorable residues to create additional strong interactions. MutDock was used to dock two scaffolds, namely, Affibodies and DARPins, with ten randomly selected antigens. The energies of the docked poses were minimized and binding energies were compared with docked poses from ZDOCK and HADDOCK. The top MutDock poses consisted of higher and comparable binding energies than the top ZDOCK and HADDOCK poses, respectively. This work contributes to the discovery of novel binders based on smaller-sized, fixed-backbone protein scaffolds.

Key aspects of the past 30 years of protein design

Proteins are the workhorse of life. They are the building infrastructure of living systems; they are the most efficient molecular machines known, and their enzymatic activity is still unmatched in versatility by any artificial system. Perhaps proteins' most remarkable feature is their modularity. The large amount of information required to specify each protein's function is analogically encoded with an alphabet of just ∼20 letters. The protein folding problem is how to encode all such information in a sequence of 20 letters. In this review, we go through the last 30 years of research to summarize the state of the art and highlight some applications related to fundamental problems of protein evolution.

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.

The road to fully programmable protein catalysis

The ability to design efficient enzymes from scratch would have a profound effect on chemistry, biotechnology and medicine. Rapid progress in protein engineering over the past decade makes us optimistic that this ambition is within reach. The development of artificial enzymes containing metal cofactors and noncanonical organocatalytic groups shows how protein structure can be optimized to harness the reactivity of nonproteinogenic elements. In parallel, computational methods have been used to design protein catalysts for diverse reactions on the basis of fundamental principles of transition state stabilization. Although the activities of designed catalysts have been quite low, extensive laboratory evolution has been used to generate efficient enzymes. Structural analysis of these systems has revealed the high degree of precision that will be needed to design catalysts with greater activity. To this end, emerging protein design methods, including deep learning, hold particular promise for improving model accuracy. Here we take stock of key developments in the field and highlight new opportunities for innovation that should allow us to transition beyond the current state of the art and enable the robust design of biocatalysts to address societal needs.

Ig-VAE: Generative modeling of protein structure by direct 3D coordinate generation

While deep learning models have seen increasing applications in protein science, few have been implemented for protein backbone generation—an important task in structure-based problems such as active site and interface design. We present a new approach to building class-specific backbones, using a variational auto-encoder to directly generate the 3D coordinates of immunoglobulins. Our model is torsion- and distance-aware, learns a high-resolution embedding of the dataset, and generates novel, high-quality structures compatible with existing design tools. We show that the Ig-VAE can be used with Rosetta to create a computational model of a SARS-CoV2-RBD binder via latent space sampling. We further demonstrate that the model’s generative prior is a powerful tool for guiding computational protein design, motivating a new paradigm under which backbone design is solved as constrained optimization problem in the latent space of a generative model.

Many essential biochemical processes are governed by protein-protein interactions (PPIs), and our ability to make binding proteins that modulate PPIs is crucial to the creation of therapeutics and the study of cell-signaling. One critical aspect of PPI design is to capture protein conformational flexibility. Deep generative models are a class of mathematical models that are able to synthesize novel data from a finite set of training examples. Here, we make advances in computational protein design methodology by developing a deep generative model that creates protein backbones adopting the immunoglobulin fold, which is found in natural binding proteins such as antibodies. While generative models have been powerful in tasks such as image generation, using them to create proteins has remained a challenge. We solve this problem with a new model that allows for the direct generation of novel 3D molecules and show that they are of high chemical accuracy. Generated structures work well with existing protein design methods such as Rosetta, providing access to a large collection of novel immunoglobulin structures. Finally, we present a new protein design framework, called “generative design,” that shows how deep generative models such as ours can be applied to virtually any protein design problem.

The Structural Rule Distinguishing a Superfold: A Case Study of Ferredoxin Fold and the Reverse Ferredoxin Fold

Superfolds are folds commonly observed among evolutionarily unrelated multiple superfamilies of proteins. Since discovering superfolds almost two decades ago, structural rules distinguishing superfolds from the other ordinary folds have been explored but remained elusive. Here, we analyzed a typical superfold, the ferredoxin fold, and the fold which reverses the N to C terminus direction from the ferredoxin fold as a case study to find the rule to distinguish superfolds from the other folds. Though all the known structural characteristics for superfolds apply to both the ferredoxin fold and the reverse ferredoxin fold, the reverse fold has been found only in a single superfamily. The database analyses in the present study revealed the structural preferences of αβ- and βα-units; the preferences separate two α-helices in the ferredoxin fold, preventing their collision and stabilizing the fold. In contrast, in the reverse ferredoxin fold, the preferences bring two helices near each other, inducing structural conflict. The Rosetta folding simulations suggested that the ferredoxin fold is physically much more realizable than the reverse ferredoxin fold. Therefore, we propose that minimal structural conflict or minimal frustration among secondary structures is the rule to distinguish a superfold from ordinary folds. Intriguingly, the database analyses revealed that a most stringent structural rule in proteins, the right-handedness of the βαβ-unit, is broken in a set of structures to prevent the frustration, suggesting the proposed rule of minimum frustration among secondary structural units is comparably strong as the right-handedness rule of the βαβ-unit.

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.

Advances in the Computational Design of Small-Molecule-Controlled Protein-Based Circuits for Synthetic Biology

Synthetic biology approaches living systems with an engineering perspective and promises to deliver solutions to global challenges in healthcare and sustainability. A critical component is the design of biomolecular circuits with programmable input-output behaviors. Such circuits typically rely on a sensor module that recognizes molecular inputs, which is coupled to a functional output via protein-level circuits or regulating the expression of a target gene. While gene expression outputs can be customized relatively easily by exchanging the target genes, sensing new inputs is a major limitation. There is a limited repertoire of sensors found in nature, and there are often difficulties with interfacing them with engineered circuits. Computational protein design could be a key enabling technology to address these challenges, as it allows for the engineering of modular and tunable sensors that can be tailored to the circuit's application. In this article, we review recent computational approaches to design protein-based sensors for small-molecule inputs with particular focus on those based on the widely used Rosetta software suite. Furthermore, we review mechanisms that have been harnessed to couple ligand inputs to functional outputs. Based on recent literature, we illustrate how the combination of protein design and synthetic biology enables new sensors for diverse applications ranging from biomedicine to metabolic engineering. We conclude with a perspective on how strategies to address frontiers in protein design and cellular circuit design may enable the next generation of sense-response networks, which may increasingly be assembled from de novo components to display diverse and engineerable input-output behaviors.

Design of protein-binding proteins from the target structure alone

The design of proteins that bind to a specific site on the surface of a target protein using no information other than the three-dimensional structure of the target remains a challenge1-5. Here we describe a general solution to this problem that starts with a broad exploration of the vast space of possible binding modes to a selected region of a protein surface, and then intensifies the search in the vicinity of the most promising binding modes. We demonstrate the broad applicability of this approach through the de novo design of binding proteins to 12 diverse protein targets with different shapes and surface properties. Biophysical characterization shows that the binders, which are all smaller than 65 amino acids, are hyperstable and, following experimental optimization, bind their targets with nanomolar to picomolar affinities. We succeeded in solving crystal structures of five of the binder-target complexes, and all five closely match the corresponding computational design models. Experimental data on nearly half a million computational designs and hundreds of thousands of point mutants provide detailed feedback on the strengths and limitations of the method and of our current understanding of protein-protein interactions, and should guide improvements of both. Our approach enables the targeted design of binders to sites of interest on a wide variety of proteins for therapeutic and diagnostic applications.

Prediction of Fluorophore Brightness in Designed Mini Fluorescence Activating Proteins

The de novo computational design of proteins with predefined three-dimensional structure is becoming much more routine due to advancements both in force fields and algorithms. However, creating designs with functions beyond folding is more challenging. In that regard, the recent design of small beta barrel proteins that activate the fluorescence of an exogenous small molecule chromophore (DFHBI) is noteworthy. These proteins, termed mini fluorescence activating proteins (mFAPs), have been shown to increase the brightness of the chromophore more than 100-fold upon binding to the designed ligand pocket. The design process created a large library of variants with different brightness levels but gave no rational explanation for why one variant was brighter than another. Here, we use quantum mechanics and molecular dynamics simulations to investigate how molecular flexibility in the ground and excited states influences brightness. We show that the ability of the protein to resist dihedral angle rotation of the chromophore is critical for predicting brightness. Our simulations suggest that the mFAP/DFHBI complex has a rough energy landscape, requiring extensive ground-state sampling to achieve converged predictions of excited-state kinetics. While computationally demanding, this roughness suggests that mFAP protein function can be enhanced by reshaping the energy landscape toward conformations that better resist DFHBI bond rotation.

Engineering ligand-specific biosensors for aromatic amino acids and neurochemicals

Microbial biosensors have diverse applications in metabolic engineering and medicine. Specific and accurate quantification of chemical concentrations allows for adaptive regulation of enzymatic pathways and temporally precise expression of diagnostic reporters. Although biosensors should differentiate structurally similar ligands with distinct biological functions, such specific sensors are rarely found in nature and challenging to create. Using E. coli Nissle 1917, a generally regarded as safe microbe, we characterized two biosensor systems that promiscuously recognize aromatic amino acids or neurochemicals. To improve the sensors' selectivity and sensitivity, we applied rational protein engineering by identifying and mutagenizing amino acid residues and successfully demonstrated the ligand-specific biosensors for phenylalanine, tyrosine, phenylethylamine, and tyramine. Additionally, our approach revealed insights into the uncharacterized structure of the FeaR regulator, including critical residues in ligand binding. These results lay the groundwork for developing kinetically adaptive microbes for diverse applications. A record of this paper's transparent peer review process is included in the supplemental information.

Energetic Basis and Design of Enzyme Function Demonstrated Using GFP, an Excited-State Enzyme

The past decades have witnessed an explosion of de novo protein designs with a remarkable range of scaffolds. It remains challenging, however, to design catalytic functions that are competitive with naturally occurring counterparts as well as biomimetic or nonbiological catalysts. Although directed evolution often offers efficient solutions, the fitness landscape remains opaque. Green fluorescent protein (GFP), which has revolutionized biological imaging and assays, is one of the most redesigned proteins. While not an enzyme in the conventional sense, GFPs feature competing excited-state decay pathways with the same steric and electrostatic origins as conventional ground-state catalysts, and they exert exquisite control over multiple reaction outcomes through the same principles. Thus, GFP is an "excited-state enzyme". Herein we show that rationally designed mutants and hybrids that contain environmental mutations and substituted chromophores provide the basis for a quantitative model and prediction that describes the influence of sterics and electrostatics on excited-state catalysis of GFPs. As both perturbations can selectively bias photoisomerization pathways, GFPs with fluorescence quantum yields (FQYs) and photoswitching characteristics tailored for specific applications could be predicted and then demonstrated. The underlying energetic landscape, readily accessible via spectroscopy for GFPs, offers an important missing link in the design of protein function that is generalizable to catalyst design.

Bioprospecting of microbial enzymes: current trends in industry and healthcare

Microbial enzymes have an indispensable role in producing foods, pharmaceuticals, and other commercial goods. Many novel enzymes have been reported from all domains of life, such as plants, microbes, and animals. Nonetheless, industrially desirable enzymes of microbial origin are limited. This review article discusses the classifications, applications, sources, and challenges of most demanded industrial enzymes such as pectinases, cellulase, lipase, and protease. In addition, the production of novel enzymes through protein engineering technologies such as directed evolution, rational, and de novo design, for the improvement of existing industrial enzymes is also explored. We have also explored the role of metagenomics, nanotechnology, OMICs, and machine learning approaches in the bioprospecting of novel enzymes. Overall, this review covers the basics of biocatalysts in industrial and healthcare applications and provides an overview of existing microbial enzyme optimization tools. KEY POINTS: • Microbial bioactive molecules are vital for therapeutic and industrial applications. • High-throughput OMIC is the most proficient approach for novel enzyme discovery. • Comprehensive databases and efficient machine learning models are the need of the hour to fast forward de novo enzyme design and discovery.

How much metagenome data is needed for protein structure prediction: The advantages of targeted approach from the ecological and evolutionary perspectives

It has been proven that three-dimensional protein structures could be modeled by supplementing homologous sequences with metagenome sequences. Even though a large volume of metagenome data is utilized for such purposes, a significant proportion of proteins remain unsolved. In this review, we focus on identifying ecological and evolutionary patterns in metagenome data, decoding the complicated relationships of these patterns with protein structures, and investigating how these patterns can be effectively used to improve protein structure prediction. First, we proposed the metagenome utilization efficiency and marginal effect model to quantify the divergent distribution of homologous sequences for the protein family. Second, we proposed that the targeted approach effectively identifies homologous sequences from specified biomes compared with the untargeted approach's blind search. Finally, we determined the lower bound for metagenome data required for predicting all the protein structures in the Pfam database and showed that the present metagenome data is insufficient for this purpose. In summary, we discovered ecological and evolutionary patterns in the metagenome data that may be used to predict protein structures effectively. The targeted approach is promising in terms of effectively extracting homologous sequences and predicting protein structures using these patterns.

Juggling with fluorescent proteins: Spectrum and structural changes of the mCardinal2 variants

mCardinal2 is a red fluorescent protein developed through the designs of mKate, mNeptune and mCardinal. Fluorescence spectrums of mCardinal2 and its five mutants (T143C, T143G, C158A, C158D and M160E) were measured with their quantum yields. C158A and C158D increased brightness with slight changes in fluorescence spectrums while T143C, T143G and M160E decreased brightness with blue shift in fluorescence spectrums, which resulted in green, cyan and green fluorescent proteins respectively. Crystal structures of all six variants were analyzed and compared together with those of mKate, LSS-mKate1, LSS-mKate2 and mCardinal. Around the Cα-Cβ bond of Tyr64 in the MYG chromophores, only C158A and C158D were in the trans conformation while all others were mostly in the cis conformation. Blue-shift brightness-decreased variants (T143C, T143G and M160E) showed the diminished hydrogen bonds while large-Stoke-shift brightness-increased variant C158D showed the enhanced hydrogen bonds around the chromophore.

A backbone-centred energy function of neural networks for protein design

A protein backbone structure is designable if a substantial number of amino acid sequences exist that autonomously fold into it^1,2. It has been suggested that the designability of backbones is governed mainly by side chain-independent or side chain type-insensitive molecular interactions^3-5, indicating an approach for designing new backbones (ready for amino acid selection) based on continuous sampling and optimization of the backbone-centred energy surface. However, a sufficiently comprehensive and precise energy function has yet to be established for this purpose. Here we show that this goal is met by a statistical model named SCUBA (for Side Chain-Unknown Backbone Arrangement) that uses neural network-form energy terms. These terms are learned with a two-step approach that comprises kernel density estimation followed by neural network training and can analytically represent multidimensional, high-order correlations in known protein structures. We report the crystal structures of nine de novo proteins whose backbones were designed to high precision using SCUBA, four of which have novel, non-natural overall architectures. By eschewing use of fragments from existing protein structures, SCUBA-driven structure design facilitates far-reaching exploration of the designable backbone space, thus extending the novelty and diversity of the proteins amenable to de novo 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.

Biological nanopores for sensing applications

Biological nanopores are proteins with transmembrane pore that can be embedded in lipid bilayer. With the development of single-channel current measurement technologies, biological nanopores have been reconstituted into planar lipid bilayer and used for single-molecule sensing of various analytes and events such as single-molecule DNA sensing and sequencing. To improve the sensitivity for specific analytes, various engineered nanopore proteins and strategies are deployed. Here, we introduce the origin and principle of nanopore sensing technology as well as the structure and associated properties of frequently used protein nanopores. Furthermore, sensing strategies for different applications are reviewed, with focus on the alteration of buffer condition, protein engineering, and deployment of accessory proteins and adapter-assisted sensing. Finally, outlooks for de novo design of nanopore and nanopore beyond sensing are discussed.

The Energy Landscape Perspective: Encoding Structure and Function for Biomolecules

The energy landscape perspective is outlined with particular reference to biomolecules that perform multiple functions. We associate these multifunctional molecules with multifunnel energy landscapes, illustrated by some selected examples, where understanding the organisation of the landscape has provided new insight into function. Conformational selection and induced fit may provide alternative routes to realisation of multifunctionality, exploiting the possibility of environmental control and distinct binding modes.

Genetically engineered materials: Proteins and beyond

Information-rich molecules provide opportunities for evolution. Genetically engineered materials are superior in that their properties are coded within genetic sequences and could be fine-tuned. In this review, we elaborate the concept of genetically engineered materials (GEMs) using examples ranging from engineered protein materials to engineered living materials. Protein-based materials are the materials of choice by nature. Recent progress in protein engineering has led to opportunities to tune their sequences for optimal material performance. Proteins also play a central role in living materials where they act in concert with other biological components as well as nonbiological cofactors, giving rise to living features. While the existing GEMs are often limited to those constructed by building blocks of biological origin, being genetically engineerable does not preclude nonbiologic or synthetic materials, the latter of which have yet to be fully explored.