Differential sensing with arrays of de novo designed peptide assemblies

Rationally seeded computational protein design of ɑ-helical barrels

Computational protein design is advancing rapidly. Here we describe efficient routes starting from validated parallel and antiparallel peptide assemblies to design two families of α-helical barrel proteins with central channels that bind small molecules. Computational designs are seeded by the sequences and structures of defined de novo oligomeric barrel-forming peptides, and adjacent helices are connected by loop building. For targets with antiparallel helices, short loops are sufficient. However, targets with parallel helices require longer connectors; namely, an outer layer of helix-turn-helix-turn-helix motifs that are packed onto the barrels. Throughout these computational pipelines, residues that define open states of the barrels are maintained. This minimizes sequence sampling, accelerating the design process. For each of six targets, just two to six synthetic genes are made for expression in Escherichia coli. On average, 70% of these genes express to give soluble monomeric proteins that are fully characterized, including high-resolution structures for most targets that match the design models with high accuracy.

Opportunities and challenges in design and optimization of protein function

The field of protein design has made remarkable progress over the past decade. Historically, the low reliability of purely structure-based design methods limited their application, but recent strategies that combine structure-based and sequence-based calculations, as well as machine learning tools, have dramatically improved protein engineering and design. In this Review, we discuss how these methods have enabled the design of increasingly complex structures and therapeutically relevant activities. Additionally, protein optimization methods have improved the stability and activity of complex eukaryotic proteins. Thanks to their increased reliability, computational design methods have been applied to improve therapeutics and enzymes for green chemistry and have generated vaccine antigens, antivirals and drug-delivery nano-vehicles. Moreover, the high success of design methods reflects an increased understanding of basic rules that govern the relationships among protein sequence, structure and function. However, de novo design is still limited mostly to α-helix bundles, restricting its potential to generate sophisticated enzymes and diverse protein and small-molecule binders. Designing complex protein structures is a challenging but necessary next step if we are to realize our objective of generating new-to-nature activities.

Accelerating Genetic Sensor Development, Scale-up, and Deployment Using Synthetic Biology

Living cells are exquisitely tuned to sense and respond to changes in their environment. Repurposing these systems to create engineered biosensors has seen growing interest in the field of synthetic biology and provides a foundation for many innovative applications spanning environmental monitoring to improved biobased production. In this review, we present a detailed overview of currently available biosensors and the methods that have supported their development, scale-up, and deployment. We focus on genetic sensors in living cells whose outputs affect gene expression. We find that emerging high-throughput experimental assays and evolutionary approaches combined with advanced bioinformatics and machine learning are establishing pipelines to produce genetic sensors for virtually any small molecule, protein, or nucleic acid. However, more complex sensing tasks based on classifying compositions of many stimuli and the reliable deployment of these systems into real-world settings remain challenges. We suggest that recent advances in our ability to precisely modify nonmodel organisms and the integration of proven control engineering principles (e.g., feedback) into the broader design of genetic sensing systems will be necessary to overcome these hurdles and realize the immense potential of the field.

Machine Learning-Assistant Colorimetric Sensor Arrays for Intelligent and Rapid Diagnosis of Urinary Tract Infection

Urinary tract infections (UTIs), which can lead to pyelonephritis, urosepsis, and even death, are among the most prevalent infectious diseases worldwide, with a notable increase in treatment costs due to the emergence of drug-resistant pathogens. Current diagnostic strategies for UTIs, such as urine culture and flow cytometry, require time-consuming protocols and expensive equipment. We present here a machine learning-assisted colorimetric sensor array based on recognition of ligand-functionalized Fe single-atom nanozymes (SANs) for the identification of microorganisms at the order, genus, and species levels. Colorimetric sensor arrays are built from the SAN Fe1-NC functionalized with four types of recognition ligands, generating unique microbial identification fingerprints. By integrating the colorimetric sensor arrays with a trained computational classification model, the platform can identify more than 10 microorganisms in UTI urine samples within 1 h. Diagnostic accuracy of up to 97% was achieved in 60 UTI clinical samples, holding great potential for translation into clinical practice applications.

Food for Thought: Optical Sensor Arrays and Machine Learning for the Food and Beverage Industry

Arrays of cross-reactive sensors, combined with statistical or machine learning analysis of their multivariate outputs, have enabled the holistic analysis of complex samples in biomedicine, environmental science, and consumer products. Comparisons are frequently made to the mammalian nose or tongue and this perspective examines the role of sensing arrays in analyzing food and beverages for quality, veracity, and safety. I focus on optical sensor arrays as low-cost, easy-to-measure tools for use in the field, on the factory floor, or even by the consumer. Novel materials and approaches are highlighted and challenges in the research field are discussed, including sample processing/handling and access to significant sample sets to train and test arrays to tackle real issues in the industry. Finally, I examine whether the comparison of sensing arrays to noses and tongues is helpful in an industry defined by human taste.

Design of Functional Globular β-Sheet Miniproteins

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

Characterization of amyloid-like metal-amino acid assemblies with remarkable catalytic activity

While enzymes are potentially useful in various applications, their limited operational stability and production costs have led to an extensive search for stable catalytic agents that will retain the efficiency, specificity, and environmental-friendliness of natural enzymes. Despite extensive efforts, there is still an unmet need for improved enzyme mimics and novel concepts to discover and optimize such agents. Inspired by the catalytic activity of amyloids and the formation of amyloid-like assemblies by metabolites, our group pioneered the development of novel metabolite-metal co-assemblies (bio-nanozymes) that produce nanomaterials mimicking the catalytic function of common metalloenzymes that are being used for various technological applications. In addition to their notable activity, bio-nanozymes are remarkably safe as they are purely composed of amino acids and minerals that are harmless to the environment. The bio-nanozymes exhibit high efficiency and exceptional robustness, even under extreme conditions of temperature, pH, and salinity that are impractical for enzymes. Our group has recently also demonstrated the formation of ordered amino acid co-assemblies showing selective and preferential interactions comparable to the organization of residues in folded proteins. The identified bio-nanozymes can be used in various applications including environmental remediation, synthesis of new materials, and green energy.

ProteinMPNN Recovers Complex Sequence Properties of Transmembrane β-barrels

Recent deep-learning (DL) protein design methods have been successfully applied to a range of protein design problems, including the de novo design of novel folds, protein binders, and enzymes. However, DL methods have yet to meet the challenge of de novo membrane protein (MP) and the design of complex β-sheet folds. We performed a comprehensive benchmark of one DL protein sequence design method, ProteinMPNN, using transmembrane and water-soluble β-barrel folds as a model, and compared the performance of ProteinMPNN to the new membrane-specific Rosetta Franklin2023 energy function. We tested the effect of input backbone refinement on ProteinMPNN performance and found that given refined and well-defined inputs, ProteinMPNN more accurately captures global sequence properties despite complex folding biophysics. It generates more diverse TMB sequences than Franklin2023 in pore-facing positions. In addition, ProteinMPNN generated TMB sequences that passed state-of-the-art in silico filters for experimental validation, suggesting that the model could be used in de novo design tasks of diverse nanopores for single-molecule sensing and sequencing. Lastly, our results indicate that the low success rate of ProteinMPNN for the design of β-sheet proteins stems from backbone input accuracy rather than software limitations.

Metal-Promoted Higher-Order Assembly of Disulfide-Stapled Helical Barrels

Peptide-based helical barrels are a noteworthy building block for hierarchical assembly, with a hydrophobic cavity that can serve as a host for cargo. In this study, disulfide-stapled helical barrels were synthesized containing ligands for metal ions on the hydrophilic face of each amphiphilic peptide helix. The major product of the disulfide-stapling reaction was found to be composed of five amphiphilic peptides, thereby going from a 16-amino-acid peptide to a stapled 80-residue protein in one step. The structure of this pentamer, 5HB1, was optimized in silico, indicating a significant hydrophobic cavity of ~6 Å within a helical barrel. Metal-ion-promoted assembly of the helical barrel building blocks generated higher order assemblies with a three-dimensional (3D) matrix morphology. The matrix was decorated with hydrophobic dyes and His-tagged proteins both before and after assembly, taking advantage of the hydrophobic pocket within the helical barrels and coordination sites within the metal ion-peptide framework. As such, this peptide-based biomaterial has potential for a number of biotechnology applications, including supplying small molecule and protein growth factors during cell and tissue growth within the matrix.

Peptide Self-Assembly Controlled Photoligation of Polymers

Highly efficient chemical ligations that operate in water under mild conditions are the foundation of bioorthogonal chemistry. However, the toolbox of suitable reactions is limited. Conventional approaches to expand this toolbox aim at altering the inherent reactivity of functional groups to design new reactions that meet the required benchmarks. Inspired by controlled reaction environments that enzymes provide, we report a fundamentally different approach that makes inefficient reactions highly efficient within defined local environments. Contrasting enzymatically catalyzed reactions, the reactivity controlling self-assembled environment is brought about by the ligation targets themselves─avoiding the use of a catalyst. Targeting [2 + 2] photocycloadditions, which are inefficient at low concentrations and readily quenched by oxygen, short β-sheet encoded peptide sequences are inserted between a hydrophobic photoreactive styrylpyrene unit and a hydrophilic polymer. In water, electrostatic repulsion of deprotonated amino acid residues governs the formation of small self-assembled structures, which enable a highly efficient photoligation of the polymer, reaching ∼90% ligation within 2 min (0.034 mM). Upon protonation at low pH, the self-assembly changes into 1D fibers, altering photophysical properties and shutting down the photocycloaddition reaction. Using the reversible morphology change, it is possible to switch the photoligation "ON" or "OFF" under constant irradiation simply by varying the pH. Importantly, in dimethylformamide, the photoligation reaction did not occur even at 10-fold higher concentrations (0.34 mM). The self-assembly into a specific architecture, encoded into the polymer ligation target, enables a highly efficient ligation that overcomes the concentration limitations and high oxygen sensitivity of [2 + 2] photocycloadditions.

Understanding a protein fold: the physics, chemistry, and biology of α-helical coiled coils

Protein science is being transformed by powerful computational methods for structure prediction and design: AlphaFold2 can predict many natural protein structures from sequence, and other AI methods are enabling the de novo design of new structures. This raises a question: how much do we understand the underlying sequence-to-structure/function relationships being captured by these methods? This perspective presents our current understanding of one class of protein assembly, the α-helical coiled coils. At first sight, these are straightforward: sequence repeats of hydrophobic (h) and polar (p) residues, (hpphppp)_n, direct the folding and assembly of amphipathic α helices into bundles. However, many different bundles are possible: they can have two or more helices (different oligomers); the helices can have parallel, antiparallel or mixed arrangements (different topologies); and the helical sequences can be the same (homomers) or different (heteromers). Thus, sequence-to-structure relationships must be present within the hpphppp repeats to distinguish these states. I discuss the current understanding of this problem at three levels: First, physics gives a parametric framework to generate the many possible coiled-coil backbone structures. Second, chemistry provides a means to explore and deliver sequence-to-structure relationships. Third, biology shows how coiled coils are adapted and functionalized in nature, inspiring applications of coiled coils in synthetic biology. I argue that the chemistry is largely understood; the physics is partly solved, though the considerable challenge of predicting even relative stabilities of different coiled-coil states remains; but there is much more to explore in the biology and synthetic biology of coiled coils.