An enzyme-based biosensor for monitoring and engineering protein stability in vivo

Optimization of a High-Throughput Screen for Monitoring Disease-Associated Protein Misfolding and Aggregation in Bacteria

Protein misfolding and aggregation are central features of a wide range of diseases, including neurodegenerative disorders, systemic amyloidoses, and cancer. The identification of compounds that can modulate protein folding and aggregation is a key step toward developing effective therapies. High-throughput screening methods are essential for efficiently identifying such compounds. In this study, we optimized a previously developed high-throughput genetic screen for monitoring protein misfolding and aggregation in bacteria. This system is based on monitoring the fluorescence of Escherichia coli cells expressing fusions of human misfolding-prone and disease-related proteins (MisPs) with the green fluorescent protein. We systematically tested a variety of experimental conditions, such as overexpression conditions and MisP-GFP fusion formats, to identify key parameters that affect the sensitivity and dynamic range of the assay. Using misfolding-prone, cancer-associated variants of human p53 as a model system, we found that strong overexpression conditions, such as high copy number vectors, strong promoters, high inducer concentrations, and high overexpression temperatures, can yield optimal assay performance. These optimized assay conditions were also validated with additional MisPs, such as the Alzheimer's disease-associated amyloid-β peptide and variants of superoxide dismutase 1 associated with amyotrophic lateral sclerosis. At the same time, we observed that certain conditions, such as inducer concentrations and overexpression temperature, may need to be precisely fine-tuned for each new MisP target to yield optimal assay performance. Our findings provide a framework for standardizing MisP-GFP screening assays, facilitating their broad application in the discovery of therapeutic agents targeting protein misfolding and aggregation.

Precise Engineering and Efficient Biosynthesis of Robust and High-Activity Human Haemoglobin for Artificial Oxygen Carriers

Recombinant human haemoglobin (rHb) is a tetramer protein with heme as cofactors, which have extensive applications in the fields of biomaterials and biomedical therapeutics. However, due to the poor structural stability, the dissociation of heme, weak oxygen transport efficiency, and lower activity, the utilisation of rHb is severely limited in artificial oxygen carriers. Herein, based on the novel developed high-throughput screening strategies and semi-rational design, the engineered rHb mutant with strong stability and heme-binding ability was obtained. In addition, through the homology alignment and rational design, the oxygen transport capacity of rHb was significantly enhanced. Furthermore, the bottlenecks of heme supply were overcome by applying the fine-tuned heme synthesis in Escherichia coli. Finally, the robust and high-activity rHb mutant was synthesised and can be used as a new generation of artificial oxygen carriers.

AggNet: Advancing protein aggregation analysis through deep learning and protein language model

Protein aggregation is critical to various biological and pathological processes. Besides, it is also an important property in biotherapeutic development. However, experimental methods to profile protein aggregation are costly and labor-intensive, driving the need for more efficient computational alternatives. In this study, we introduce "AggNet," a novel deep learning framework based on the protein language model ESM2 and AlphaFold2, which utilizes physicochemical, evolutionary, and structural information to discriminate amyloid and non-amyloid peptides and identify aggregation-prone regions (APRs) in diverse proteins. Benchmark comparisons show that AggNet outperforms existing methods and achieves state-of-the-art performance on protein aggregation prediction. Also, the predictive ability of AggNet is stable across proteins with different secondary structures. Feature analysis and visualizations prove that the model effectively captures peptides' physicochemical properties effectively, thereby offering enhanced interpretability. Further validation through a case study on MEDI1912 confirms AggNet's practical utility in analyzing protein aggregation and guiding mutation for aggregation mitigation. This study enhances computational tools for predicting protein aggregation and highlights the potential of AggNet in protein engineering. Finally, to improve the accessibility of AggNet, the source code can be accessed at: https://github.com/Hill-Wenka/AggNet.

Current status and emerging frontiers in enzyme engineering: An industrial perspective

Protein engineering mechanisms can be an efficient approach to enhance the biochemical properties of various biocatalysts. Immobilization of biocatalysts and the introduction of new-to-nature chemical reactivities are also possible through the same mechanism. Discovering new protocols that enhance the catalytic active protein that possesses novelty in terms of being stable, active, and, stereoselectivity with functions could be identified as essential areas in terms of concurrent bioorganic chemistry (synergistic relationship between organic chemistry and biochemistry in the context of enzyme engineering). However, with our current level of knowledge about protein folding and its correlation with protein conformation and activities, it is almost impossible to design proteins with specific biological and physical properties. Hence, contemporary protein engineering typically involves reprogramming existing enzymes by mutagenesis to generate new phenotypes with desired properties. These processes ensure that limitations of naturally occurring enzymes are not encountered. For example, researchers have engineered cellulases and hemicellulases to withstand harsh conditions encountered during biomass pretreatment, such as high temperatures and acidic environments. By enhancing the activity and robustness of these enzymes, biofuel production becomes more economically viable and environmentally sustainable. Recent trends in enzyme engineering have enabled the development of tailored biocatalysts for pharmaceutical applications. For instance, researchers have engineered enzymes such as cytochrome P450s and amine oxidases to catalyze challenging reactions involved in drug synthesis. In addition to conventional methods, there has been an increasing application of machine learning techniques to identify patterns in data. These patterns are then used to predict protein structures, enhance enzyme solubility, stability, and function, forecast substrate specificity, and assist in rational protein design. In this review, we discussed recent trends in enzyme engineering to optimize the biochemical properties of various biocatalysts. Using examples relevant to biotechnology in engineering enzymes, we try to expatiate the significance of enzyme engineering with how these methods could be applied to optimize the biochemical properties of a naturally occurring enzyme.

State-of-the-art in engineering small molecule biosensors and their applications in metabolic engineering

Genetically encoded biosensors are crucial for enhancing our understanding of how molecules regulate biological systems. Small molecule biosensors, in particular, help us understand the interaction between chemicals and biological processes. They also accelerate metabolic engineering by increasing screening throughput and eliminating the need for sample preparation through traditional chemical analysis. Additionally, they offer significantly higher spatial and temporal resolution in cellular analyte measurements. In this review, we discuss recent progress in in vivo biosensors and control systems-biosensor-based controllers-for metabolic engineering. We also specifically explore protein-based biosensors that utilize less commonly exploited signaling mechanisms, such as protein stability and induced degradation, compared to more prevalent transcription factor and allosteric regulation mechanism. We propose that these lesser-used mechanisms will be significant for engineering eukaryotic systems and slower-growing prokaryotic systems where protein turnover may facilitate more rapid and reliable measurement and regulation of the current cellular state. Lastly, we emphasize the utilization of cutting-edge and state-of-the-art techniques in the development of protein-based biosensors, achieved through rational design, directed evolution, and collaborative approaches.

An intein-based biosensor to measure protein stability in vivo

Biosensors to measure protein stability in vivo are valuable tools for a variety of applications. Previous work has demonstrated that a tripartite design, whereby a protein of interest (POI) is inserted within a reporter, can link POI stability to reporter activity. Inteins are translated within other proteins and excised in a self-mediated protein splicing reaction. Here, we developed a novel folding biosensor where a POI is inserted within an intein, which is subsequently translated within an antibiotic resistance marker. We showed that protein splicing is required for antibiotic resistance and that housing a stable POI within the intein, compared to an unstable variant, results in a 100,000-fold difference in survival. Further, using a fluorescent protein that matures slowly as the POI, we developed a reporter with two simultaneous readouts for protein folding. Finally, we showed that co-expression of GroEL can significantly increase the activity of both reporters, further verifying that protein folding factors can act on the POI in the biosensor. As a whole, our work provides a new twist on the traditional tripartite approach to measuring protein stability in vivo.

High-Throughput Screening Techniques for the Selection of Thermostable Enzymes

The acquisition of a thermostable enzyme is an indispensable prerequisite for its successful implementation in industrial applications and the development of novel functionalities. Various protein engineering approaches, including rational design, semirational design, and directed evolution, have been employed to enhance thermostability. However, all of these approaches require sensitive and reliable high-throughput screening (HTS) technologies to efficiently and rapidly identify variants with improved properties. While numerous reviews focus on modification strategies for enhancing enzyme thermostability, there is a dearth of literature reviewing HTS methods specifically aimed at this objective. Herein, we present a comprehensive overview of various HTS methods utilized for modifying enzyme thermostability across different screening platforms. Additionally, we highlight significant recent examples that demonstrate the successful application of these methods. Furthermore, we address the technical challenges associated with HTS technologies used for screening thermostable enzyme variants and discuss valuable perspectives to promote further advancements in this field. This review serves as an authoritative reference source offering theoretical support for selecting appropriate screening strategies tailored to specific enzymes with the aim of improving their thermostability.

Enzymatic Degradation of Deoxynivalenol with the Engineered Detoxification Enzyme Fhb7

In the era of global climate change, the increasingly severe Fusarium head blight (FHB) and deoxynivalenol (DON) contamination have caused economic losses and brought food and feed safety concerns. Recently, an FHB resistance gene Fhb7 coding a glutathione-S transferase (GST) to degrade DON by opening the critical toxic epoxide moiety was identified and opened a new window for wheat breeding and DON detoxification. However, the poor stability of Fhb7 and the elusiveness of the catalytic mechanism hinder its practical application. Herein, we report the first structure of Fhb7 at 2.41 Å and reveal a unique catalytic mechanism of epoxide opening transformation in GST family proteins. Furthermore, variants V29P and M10 showed that 5.5-fold and 266.7-fold longer half-life time than wild-type, respectively, were identified. These variants offer broad substrate scope, and the engineered biosafe Bacillus subtilis overexpressing the variants shows excellent DON degradation performance, exhibiting potential at bacterium engineering to achieve DON detoxification in the feed and biomedicine industry. This work provides a profound mechanistic insight into the enzymatic activities of Fhb7 and paves the way for further utilizing Fhb7-related enzymes in crop breeding and DON detoxification by synthetic biology.

Atomic-level design of metalloenzyme-like active pockets in metal-organic frameworks for bioinspired catalysis

Natural metalloenzymes with astonishing reaction activity and specificity underpin essential life transformations. Nevertheless, enzymes only operate under mild conditions to keep sophisticated structures active, limiting their potential applications. Artificial metalloenzymes that recapitulate the catalytic activity of enzymes can not only circumvent the enzymatic fragility but also bring versatile functions into practice. Among them, metal-organic frameworks (MOFs) featuring diverse and site-isolated metal sites and supramolecular structures have emerged as promising candidates for metalloenzymes to move toward unparalleled properties and behaviour of enzymes. In this review, we systematically summarize the significant advances in MOF-based metalloenzyme mimics with a special emphasis on active pocket engineering at the atomic level, including primary catalytic sites and secondary coordination spheres. Then, the deep understanding of catalytic mechanisms and their advanced applications are discussed. Finally, a perspective on this emerging frontier research is provided to advance bioinspired catalysis.

MEnTaT: A machine-learning approach for the identification of mutations to increase protein stability

Enhancing protein thermal stability is important for biomedical and industrial applications as well as in the research laboratory. Here, we describe a simple machine-learning method which identifies amino acid substitutions that contribute to thermal stability based on comparison of the amino acid sequences of homologous proteins derived from bacteria that grow at different temperatures. A key feature of the method is that it compares the sequences based not simply on the amino acid identity, but rather on the structural and physicochemical properties of the side chain. The method accurately identified stabilizing substitutions in three well-studied systems and was validated prospectively by experimentally testing predicted stabilizing substitutions in a polyamine oxidase. In each case, the method outperformed the widely used bioinformatic consensus approach. The method can also provide insight into fundamental aspects of protein structure, for example, by identifying how many sequence positions in a given protein are relevant to temperature adaptation.

Biosensor-guided discovery and engineering of metabolic enzymes

A variety of chemicals have been produced through metabolic engineering approaches, and enhancing biosynthesis performance can be achieved by using enzymes with high catalytic efficiency. Accordingly, a number of efforts have been made to discover enzymes in nature for various applications. In addition, enzyme engineering approaches have been attempted to suit specific industrial purposes. However, a significant challenge in enzyme discovery and engineering is the efficient screening of enzymes with the desired phenotype from extensive enzyme libraries. To overcome this bottleneck, genetically encoded biosensors have been developed to specifically detect target molecules produced by enzyme activity at the intracellular level. Especially, the biosensors facilitate high-throughput screening (HTS) of targeted enzymes, expanding enzyme discovery and engineering strategies with advances in systems and synthetic biology. This review examines biosensor-guided HTS systems and highlights studies that have utilized these tools to discover enzymes in diverse areas and engineer enzymes to enhance their properties, such as catalytic efficiency, specificity, and stability.

Advances in ultrahigh-throughput screening technologies for protein evolution

Inspired by natural evolution, directed evolution randomly mutates the gene of interest through artificial evolution conditions with variants being screened for the required properties. Directed evolution is vital to the enhancement of protein properties and comprises the construction of libraries with considerable diversity as well as screening methods with sufficient efficiency as key steps. Owing to the various characteristics of proteins, specific methods are urgently needed for library screening, which is one of the main limiting factors in accelerating evolution. This review initially organizes the principles of ultrahigh-throughput screening from the perspective of protein properties. It then provides a comprehensive introduction to the latest progress and future trends in ultrahigh-throughput screening technologies for directed evolution.

A New Fluorescent Probe Tool: ERNathG

β-d-Glucuronidase (GUS) plays a pivotal role in both clinical treatment assessment and environmental monitoring. Existing tools for GUS detection suffer from (1) poor continuity due to a gap between the optimal pH of the probes and the enzyme and (2) diffusion from the detection site due to lack of an anchoring structure. Here we report a novel GUS pH-matching and endoplasmic reticulum-anchoring strategy for GUS recognition. The new fluorescent probe tool was termed ERNathG, which was designed and synthesized with β-d-glucuronic acid as the GUS-specific recognition site and 4-hydroxy-1,8-naphthalimide as a fluorescence reporting group, with a p-toluene sulfonyl as an anchoring group. This probe enabled the continuous and anchored detection of GUS without pH-adjustment for the related assessment of common cancer cell lines and gut bacteria. The probe's properties are far superior to those of commonly used commercial molecules.

Protein Stability: Enhancement and Measurement

This chapter defines protein stability, emphasizes its importance, and surveys the field of protein stabilization, with summary reference to a selection of 2014-2021 publications. One can enhance stability, particularly by protein engineering strategies but also by chemical modification and by other means. General protocols are set out on how to measure a given protein's (i) kinetic thermal stability and (ii) oxidative stability and (iii) how to undertake chemical modification of a protein in solution.

Increasing protein stability by inferring substitution effects from high-throughput experiments

We apply a computational model, global multi-mutant analysis (GMMA), to inform on effects of most amino acid substitutions from a randomly mutated gene library. Using a high mutation frequency, the method can determine mutations that increase the stability of even very stable proteins for which conventional selection systems have reached their limit. As a demonstration of this, we screened a mutant library of a highly stable and computationally redesigned model protein using an in vivo genetic sensor for folding and assigned a stability effect to 374 of 912 possible single amino acid substitutions. Combining the top 9 substitutions increased the unfolding energy 47 to 69 kJ/mol in a single engineering step. Crystal structures of stabilized variants showed small perturbations in helices 1 and 2, which rendered them closer in structure to the redesign template. This case study illustrates the capability of the method, which is applicable to any screen for protein function.

Computational Design of Extraordinarily Stable Peptide Structures through Side-Chain-Locked Knots

Extraordinarily stable protein and peptide structures are critically demanded in many applications. Typical approaches to enhance protein and peptide stability are strengthening certain interactions. Here, we develop a very different approach: stabilizing peptide structures through side-chain-locked knots. More specifically, a peptide core consists of a knot, which is prevented from unknotting and unfolding by large side chains of amino acids at knot boundaries. These side chains impose free energy barriers for unknotting. The free energy barriers are quantified using all-atom and coarse-grained simulations. The barriers become infinitely high for large side chains and tight knot cores, resulting in stable peptide structures, which never unfold unless one chemical bond is broken. The extraordinary stability is essentially kinetic stability. Our new approach lifts the thermodynamic restriction in designing peptide structures, provides extra freedom in selecting sequence and structural motifs that are thermodynamically unstable, and should expand the functionality of peptides. This work also provides a bottom-up understanding of how knotting enhances protein stability.

Traceless enzymatic protein synthesis without ligation sites constraint

Protein synthesis and semisynthesis offer immense promise for life sciences and have impacted pharmaceutical innovation. The absence of a generally applicable method for traceless peptide conjugation with a flexible choice of junction sites remains a bottleneck for accessing many important synthetic targets, however. Here we introduce the PALME (protein activation and ligation with multiple enzymes) platform designed for sequence-unconstrained synthesis and modification of biomacromolecules. The upstream activating modules accept and process easily accessible synthetic peptides and recombinant proteins, avoiding the challenges associated with preparation and manipulation of activated peptide substrates. Cooperatively, the downstream coupling module provides comprehensive solutions for sequential peptide condensation, cyclization and protein N/C-terminal or internal functionalization. The practical utility of this methodology is demonstrated by synthesizing a series of bioactive targets ranging from pharmaceutical ingredients to synthetically challenging proteins. The modular PALME platform exhibits unprecedentedly broad accessibility for traceless protein synthesis and functionalization, and holds enormous potential to extend the scope of protein chemistry and synthetic biology.

Electrochemical nanobiosensors equipped with peptides: a review

Recent research in the field of electrochemical biosensors equipped with peptides and nanomaterials have been categorized, reviewed, and critically analyzed. Indeed, using these innovative biosensors can revolutionize biomedical diagnostics in the future. Saving lives, time, and money in this field will be considered as some main benefits of this type of diagnosis. Here, these biosensors have been categorized and evaluated in four main sections. In the first section, the focus is on investigating the types of electrochemical peptide-based nanobiosensors applied to detect pathogenic microorganisms, microbial toxins, and viruses. In the second section, due to the importance of rapid diagnosis and prognosis of various cancers, the electrochemical peptide-based nanobiosensors designed to detect cancer biomarkers have been reviewed and analyzed. In the third section, the electrochemical peptide-based nanobiosensors, which were applied to detect the essential and effective biomolecules in the various diseases, and health control, including enzymes, hormones, biomarkers, and other biomolecules, have been considered. Finally, using a comprehensive analysis, all the used elements in these biosensors have been presented as conceptual diagrams that can effectively guide researchers in future developments. The essential factors in evaluating and analyzing these electrochemical peptide-based nanobiosensors such as analyte, peptide sequence, functional groups interacted between the peptide sequences and other biosensing components, the applied nanomaterials, diagnostic techniques, detection range, and limit of detection have also been included. Other analyzable items such as the type of used redox marker and the location of the peptide sequence against the signal transducer were also considered.

Photoenzymatic Activity of Artificial-Natural Bienzyme Applied in Biodegradation of Methylene Blue and Accelerating Polymerization of Dopamine

Enzymes as biocatalysts have attracted extensive attention. In addition to immobilizing or encapsulating various enzymes for combating the easy loss of enzymatic activity, strengthening the enzymatic activity upon light irradiation is a challenge. To the best of our knowledge, the work of spatiotemporally modulating the catalytic activity of artificial-natural bienzymes with a near-infrared light irradiation has not been reported. Inspired by immobilized enzymes and nanozymes, herein a platinum nanozyme was synthesized; subsequently, the platinum nanozyme was grafted on the body of laccase, thus successfully obtaining the artificial-natural bienzyme. The three-dimensional structure of the artificial-natural bienzyme was greatly different from that of the immobilized enzyme or the encapsulated enzyme. The platinum nanozyme possessed excellent laccase-like activity, which was 3.7 times higher than that of laccase. Meanwhile, the coordination between the platinum nanozyme and laccase was proved. Besides, the cascaded catalysis of artificial-natural bienzyme was verified with hydrogen peroxide as a mediator. The enzymatic activities of artificial-natural bienzyme with and without near-infrared light irradiation were, respectively, 46.2 and 29.5% higher than that of free laccase. Moreover, the reversible catalytic activity of the coupled enzyme could be manipulated with and without a near-infrared light at 808 nm. As a result, the degradation rates of methylene blue catalyzed by the coupled enzyme and the platinum nanozyme were higher than that of laccase. Furthermore, accelerating polymerization of the dopamine was also demonstrated. Briefly, this facile strategy may provide a universal approach to control the catalytic activity of other natural enzymes.

A dual-reporter system for investigating and optimizing protein translation and folding in E. coli

Strategies for investigating and optimizing the expression and folding of proteins for biotechnological and pharmaceutical purposes are in high demand. Here, we describe a dual-reporter biosensor system that simultaneously assesses in vivo protein translation and protein folding, thereby enabling rapid screening of mutant libraries. We have validated the dual-reporter system on five different proteins and find an excellent correlation between reporter signals and the levels of protein expression and solubility of the proteins. We further demonstrate the applicability of the dual-reporter system as a screening assay for deep mutational scanning experiments. The system enables high throughput selection of protein variants with high expression levels and altered protein stability. Next generation sequencing analysis of the resulting libraries of protein variants show a good correlation between computationally predicted and experimentally determined protein stabilities. We furthermore show that the mutational experimental data obtained using this system may be useful for protein structure calculations.

Computational and Experimental Assessment of Backbone Templates for Computational Redesign of the Thioredoxin Fold

Computational protein design has taken big strides in recent years; however, the tools available are still not at a state where a sequence can be designed to fold into a given protein structure at will and with high probability. We have applied here a recent release of Rosetta Design to redesign a set of structurally very similar proteins belonging to the thioredoxin fold. We used a genetic screening tool to estimate solubility/folding of the designed proteins in E. coli and to select the best hits from this for further biochemical characterization. We have previously used this set of template proteins for redesign and found that success was highly dependent on template structure, a trait which was also found in this study. Nevertheless, state-of-the-art design software is now able to predict the best template, most likely due to the introduction of an energy term that reports on stress in covalent bond lengths and angles. The template that led to the greatest fraction of successful designs was the same (a thioredoxin from spinach) as that identified in our previous study. Our previously described redesign of thioredoxin, which also used the spinach protein as a template, however also performed well as a template. In the present study, both of these templates yielded proteins with compact folded structures and enforced the conclusion that any design project must carefully consider different design templates. Fortunately, selecting designs based on energies appears to correctly identify such templates.