Functionalized Polysaccharides Improve Sensitivity of Tyramide/Peroxidase Proximity Labeling Assays through Electrostatic Interactions.

High-throughput assays that efficiently link genotype and phenotype with high fidelity are key to successful enzyme engineering campaigns. Among these assays, the tyramide/peroxidase proximity labeling method converts the product of an enzymatic reaction of a surface expressed enzyme to a highly reactive fluorescent radical, which labels the cell surface. In this context, maintaining the proximity of the readout reagents to the cell surface is crucial to prevent crosstalk and ensure that short-lived radical species react before diffusing away. Here, we investigated improvements in tyramide/peroxidase proximity labeling for enzyme screening. We modified chitosan (Cs) chains with horseradish peroxidase (HRP) and evaluated the effects of these conjugates on the efficiency of proximity labeling reactions on yeast cells displaying d-amino acid oxidase. By tethering HRP to chitosan through different chemical approaches, we localized the auxiliary enzyme close to the cell surface and enhanced the sensitivity of tyramide-peroxidase labeling reactions. We found that immobilizing HRP onto chitosan through a 5 kDa PEG linker improved labeling sensitivity by over 3.5-fold for substrates processed with a low turnover rate (e.g., d-lysine), while the sensitivity of the labeling for high activity substrates (e.g., d-alanine) was enhanced by over 0.6-fold. Such improvements in labeling efficiency broaden the range of enzymes and conditions that can be studied and screened by tyramide/peroxidase proximity labeling.

Integrating Dynamic Network Analysis with AI for Enhanced Epitope Prediction in PD-L1:Affibody Interactions.

Understanding binding epitopes involved in protein-protein interactions and accurately determining their structure are long-standing goals with broad applicability in industry and biomedicine. Although various experimental methods for binding epitope determination exist, these approaches are typically low throughput and cost-intensive. Computational methods have potential to accelerate epitope predictions; however, recently developed artificial intelligence (AI)-based methods frequently fail to predict epitopes of synthetic binding domains with few natural homologues. Here we have developed an integrated method employing generalized-correlation-based dynamic network analysis on multiple molecular dynamics (MD) trajectories, initiated from AlphaFold2Multimer structures, to unravel the structure and binding epitope of the therapeutic PD-L1:Affibody complex. Both AlphaFold2 and conventional molecular dynamics trajectory analysis were ineffective in distinguishing between two proposed binding models, parallel and perpendicular. However, our integrated approach, utilizing dynamic network analysis, demonstrated that the perpendicular mode was significantly more stable. These predictions were validated using a suite of experimental epitope mapping protocols, including cross-linking mass spectrometry and next-generation sequencing-based deep mutational scanning. Conversely, AlphaFold3 failed to predict a structure bound in the perpendicular pose, highlighting the necessity for exploratory research in the search for binding epitopes and challenging the notion that AI-generated protein structures can be accepted without scrutiny. Our research underscores the potential of employing dynamic network analysis to enhance AI-based structure predictions for more accurate identification of protein-protein interaction interfaces.

Integrating Dynamic Network Analysis with AI for Enhanced Epitope Prediction in PD-L1:Affibody Interactions.

Understanding binding epitopes involved in protein-protein interactions and accurately determining their structure is a long standing goal with broad applicability in industry and biomedicine. Although various experimental methods for binding epitope determination exist, these approaches are typically low throughput and cost intensive. Computational methods have potential to accelerate epitope predictions, however, recently developed artificial intelligence (AI)-based methods frequently fail to predict epitopes of synthetic binding domains with few natural homologs. Here we have developed an integrated method employing generalized-correlation-based dynamic network analysis on multiple molecular dynamics (MD) trajectories, initiated from AlphaFold2 Multimer structures, to unravel the structure and binding epitope of the therapeutic PD-L1:Affibody complex. Both AlphaFold2 and conventional molecular dynamics trajectory analysis alone each proved ineffectual in differentiating between two putative binding models referred to as parallel and perpendicular. However, our integrated approach based on dynamic network analysis showed that the perpendicular mode was significantly more stable. These predictions were validated using a suite of experimental epitope mapping protocols including cross linking mass spectrometry and next-generation sequencing-based deep mutational scanning. Our research highlights the potential of deploying dynamic network analysis to refine AI-based structure predictions for precise predictions of protein-protein interaction interfaces.

Understanding activity-stability tradeoffs in biocatalysts by enzyme proximity sequencing.

Understanding the complex relationships between enzyme sequence, folding stability and catalytic activity is crucial for applications in industry and biomedicine. However, current enzyme assay technologies are limited by an inability to simultaneously resolve both stability and activity phenotypes and to couple these to gene sequences at large scale. Here we present the development of enzyme proximity sequencing, a deep mutational scanning method that leverages peroxidase-mediated radical labeling with single cell fidelity to dissect the effects of thousands of mutations on stability and catalytic activity of oxidoreductase enzymes in a single experiment. We use enzyme proximity sequencing to analyze how 6399 missense mutations influence folding stability and catalytic activity in a D-amino acid oxidase from Rhodotorula gracilis. The resulting datasets demonstrate activity-based constraints that limit folding stability during natural evolution, and identify hotspots distant from the active site as candidates for mutations that improve catalytic activity without sacrificing stability. Enzyme proximity sequencing can be extended to other enzyme classes and provides valuable insights into biophysical principles governing enzyme structure and function.

Directed evolution of Rhodotorula gracilisd-amino acid oxidase using single-cell hydrogel encapsulation and ultrahigh-throughput screening.

Engineering catalytic and biophysical properties of enzymes is an essential step en route to advanced biomedical and industrial applications. Here, we developed a high-throughput screening and directed evolution strategy relying on single-cell hydrogel encapsulation to enhance the performance of d-Amino acid oxidase from Rhodotorula gracilis (RgDAAOx), a candidate enzyme for cancer therapy. We used a cascade reaction between RgDAAOx variants surface displayed on yeast and horseradish peroxidase (HRP) in the bulk media to trigger enzyme-mediated crosslinking of phenol-bearing fluorescent alginate macromonomers, resulting in hydrogel formation around single yeast cells. The fluorescent hydrogel capsules served as an artificial phenotype and basis for pooled library screening by fluorescence activated cell sorting (FACS). We screened a RgDAAOx variant library containing ∼106 clones while lowering the d-Ala substrate concentration over three sorting rounds in order to isolate variants with low Km. After three rounds of FACS sorting and regrowth, we isolated and fully characterized four variants displayed on the yeast surface. We identified variants with a more than 5-fold lower Km than the parent sequence, with an apparent increase in substrate binding affinity. The mutations we identified were scattered across the RgDAAOx structure, demonstrating the difficulty in rationally predicting allosteric sites and highlighting the advantages of scalable library screening technologies for evolving catalytic enzymes.

Multiplexed on-yeast serological assay for immune escape screening of SARS-CoV-2 variants.

The emergence of the SARS-CoV-2 Omicron variant altered patient risk profiles and shifted the trajectory of the COVID-19 pandemic. Therefore, sensitive serological tests capable of analyzing patient IgG responses to multiple variants in parallel are highly desirable. Here, we present an adaptable serological test based on yeast surface display and serum biopanning that characterizes immune profiles against SARS-CoV-2 Wuhan (B lineage), Delta (B.1.617.2 lineage), and Omicron (B.1.1.529 lineage) receptor-binding domain (RBD) variants. We examined IgG titers from 30 serum samples from COVID-19-convalescent and vaccinated cohorts in Switzerland, and assessed the relative affinity of polyclonal serum IgG for RBD domains. We demonstrate that serum IgGs from patients recovered from severe COVID-19 between March-June 2021 bound tightly to both original Wuhan and Delta RBD variants, but failed to recognize Omicron RBDs, representing an affinity loss of >10- to 20-fold. Our yeast immunoassay is easily tailored, expandable and parallelized with newly emerging RBD variants.

Enzyme Cascade with Horseradish Peroxidase Readout for High-Throughput Screening and Engineering of Human Arginase-1.

We report an enzyme cascade with horseradish peroxidase-based readout for screening human arginase-1 (hArg1) activity. We combined the four enzymes hArg1, ornithine decarboxylase, putrescine oxidase, and horseradish peroxidase in a reaction cascade that generated colorimetric or fluorescent signals in response to hArg1 activity and used this cascade to assay wild-type and variant hArg1 sequences as soluble enzymes and displayed on the surface of Escherichia coli. We screened a curated 13-member hArg1 library covering mutations that modified the electrostatic environment surrounding catalytic residues D128 and H141, and identified the R21E variant with a 13% enhanced catalytic turnover rate compared to wild type. Our scalable one-pot single-step arginase assay with continuous kinetic readout is amenable to high-throughput screening and directed evolution of arginase libraries and testing drug candidates for arginase inhibition.

Titrating Avidity of Yeast-Displayed Proteins Using a Transcriptional Regulator.

Yeast surface display is a valuable tool for protein engineering and directed evolution; however, significant variability in the copy number (i.e., avidity) of displayed variants on the yeast cell wall complicates screening and selection campaigns. Here, we report an engineered titratable display platform that modulates the avidity of Aga2-fusion proteins on the yeast cell wall dependent on the concentration of the anhydrotetracycline (aTc) inducer. Our design is based on a genomic Aga1 gene copy and an episomal Aga2-fusion construct both under the control of an aTc-dependent transcriptional regulator that enables stoichiometric and titratable expression, secretion, and display of Aga2-fusion proteins. We demonstrate tunable display levels over 2-3 orders of magnitude for various model proteins, including glucose oxidase enzyme variants, mechanostable dockerin-binding domains, and anti-PDL1 affibody domains. By regulating the copy number of displayed proteins, we demonstrate the effects of titratable avidity levels on several specific phenotypic activities, including enzyme activity and cell adhesion to surfaces under shear flow. Finally, we show that titrating down the display level allows yeast-based binding affinity measurements to be performed in a regime that avoids ligand depletion effects while maintaining small sample volumes, avoiding a well-known artifact in yeast-based binding assays. The ability to titrate the multivalency of proteins on the yeast cell wall through simple inducer control will benefit protein engineering and directed evolution methodology relying on yeast display for broad classes of therapeutic and diagnostic proteins of interest.

Protein Engineering and High-Throughput Screening by Yeast Surface Display: Survey of Current Methods.

Yeast surface display (YSD) is a powerful tool in biotechnology that links genotype to phenotype. In this review, the latest advancements in protein engineering and high-throughput screening based on YSD are covered. The focus is on innovative methods for overcoming challenges in YSD in the context of biotherapeutic drug discovery and diagnostics. Topics ranging from titrating avidity in YSD using transcriptional control to the development of serological diagnostic assays relying on serum biopanning and mitigation of unspecific binding are covered. Screening techniques against nontraditional cellular antigens, such as cell lysates, membrane proteins, and extracellular matrices are summarized and techniques are further delved into for expansion of the chemical repertoire, considering protein-small molecule hybrids and noncanonical amino acid incorporation. Additionally, in vivo gene diversification and continuous evolution in yeast is discussed. Collectively, these techniques enhance the diversity and functionality of engineered proteins isolated via YSD, broadening the scope of applications that can be addressed. The review concludes with future perspectives and potential impact of these advancements on protein engineering. The goal is to provide a focused summary of recent progress in the field.

Correlating single-molecule rupture mechanics with cell population adhesion by yeast display.

Here, we present a method based on yeast surface display that allows for direct comparison between population-level cell adhesion strength and single-molecule receptor-ligand rupture mechanics. We developed a high-throughput yeast adhesion assay in which yeasts displaying monomeric streptavidin (mSA) or enhanced mutant mSA were adhered to a biotinylated coverglass submerged in fluid. After exposure to shear stress (20-1000 dyn/cm2) by rapid spinning of the coverglass, cells were imaged to quantify the midpoint detachment shear stress for the cell population. We then performed atomic force microscope single-molecule force spectroscopy (SMFS) on purified mSA variants and identified correlations between single-molecule rupture force distributions and cell population adhesion strength. Several features of yeast display were important for successful correlations of adhesion strength to be drawn, including covalent attachment of the receptor to the cell wall, a precisely defined molecular pulling geometry, repression of nonspecific adhesion, and control for multivalency. With these factors properly taken into account, we show that spinning disk cell adhesion assays can be correlated with SMFS and are capable of screening the mechanical strength of receptor-ligand complexes. These workflow enhancements will accelerate research on mechanostable receptor-ligand complexes and receptor-mediated cell adhesion.

High-throughput screening, next generation sequencing and machine learning: advanced methods in enzyme engineering.

Enzyme engineering is an important biotechnological process capable of generating tailored biocatalysts for applications in industrial chemical conversion and biopharma. Typical enhancements sought in enzyme engineering and in vitro evolution campaigns include improved folding stability, catalytic activity, and/or substrate specificity. Despite significant progress in recent years in the areas of high-throughput screening and DNA sequencing, our ability to explore the vast space of functional enzyme sequences remains severely limited. Here, we review the currently available suite of modern methods for enzyme engineering, with a focus on novel readout systems based on enzyme cascades, and new approaches to reaction compartmentalization including single-cell hydrogel encapsulation techniques to achieve a genotype-phenotype link. We further summarize systematic scanning mutagenesis approaches and their merger with deep mutational scanning and massively parallel next-generation DNA sequencing technologies to generate mutability landscapes. Finally, we discuss the implementation of machine learning models for computational prediction of enzyme phenotypic fitness from sequence. This broad overview of current state-of-the-art approaches for enzyme engineering and evolution will aid newcomers and experienced researchers alike in identifying the important challenges that should be addressed to move the field forward.

Enzyme-mediated hydrogel encapsulation of single cells for high-throughput screening and directed evolution of oxidoreductases.

Directed evolution of oxidoreductases to improve their catalytic properties is being ardently pursued in the industrial, biotechnological, and biopharma sectors. Hampering this pursuit are current enzyme screening methods that are limited in terms of throughput, cost, time, and complexity. We present a directed evolution strategy that allows for large-scale one-pot screening of glucose oxidase (GOx) enzyme libraries in well-mixed homogeneous solution. We used GOx variants displayed on the outer cell wall of yeasts to initiate a cascade reaction with horseradish peroxidase (HRP), resulting in peroxidase-mediated phenol cross-coupling and encapsulation of individual cells in well-defined fluorescent alginate hydrogel shells within ~10 min in mixed cell suspensions. Following application of denaturing stress to whole-cell GOx libraries, only cells displaying GOx variants with enhanced stability or catalytic activity were able to carry out the hydrogel encapsulation reaction. Fluorescence-activated cell sorting was then used to isolate the enhanced variants. We characterized three of the newly evolved Aspergillus niger GOx enzyme sequences and found up to ~5-fold higher specific activity, enhanced thermal stability, and differentiable glycosylation patterns. By coupling intracellular gene expression with the rapid formation of an extracellular hydrogel capsule, our system improves high-throughput screening for directed evolution of H 2 O 2 -producing enzymes many folds.

Bioorthogonal Elastin-like Polypeptide Scaffolds for Immunoassay Enhancement.

Artificial multiprotein complexes are sought after reagents for biomolecular engineering. A current limiting factor is the paucity of molecular scaffolds which allow for site-specific multicomponent assembly. Here, we address this limitation by synthesizing bioorthogonal elastin-like polypeptide (ELP) scaffolds containing periodic noncanonical l-azidohomoalanine amino acids in the guest residue position. The nine azide ELP guest residues served as conjugation sites for site-specific modification with dibenzocyclooctyne (DBCO)-functionalized single-domain antibodies (SdAbs) through strain-promoted alkyne-azide cycloaddition (SPAAC). Sortase A and ybbR tags at the C- and N-termini of the ELP scaffold provided two additional sites for derivatization with small molecules and peptides by Sortase A and 4'-phosphopantetheinyl transferase (Sfp), respectively. These functional groups are chemically bioorthogonal, mutually compatible, and highly efficient, thereby enabling synthesis of multi-antibody ELP complexes in a one-pot reaction. We demonstrate application of this material for enhancing the performance of sandwich immunoassays of the recombinant protein mCherry. In undiluted human plasma, surfaces modified with multi-antibody ELP complexes showed between 2.3- and 14.3-fold improvement in sensitivity and ∼30-40% lower limits of detection as compared with nonspecifically adsorbed antibodies. Dual-labeled multi-antibody ELP complexes were further used for cytometric labeling and analysis of live eukaryotic cells. These results demonstrate how multiple antibodies complexed onto bioorthogonal protein-based polymers can be used to enhance immunospecific binding interactions through multivalency effects.

Engineered Stochastic Adhesion Between Microbes as a Protection Mechanism Against Environmental Stress.

INTRODUCTION: Microbes aggregate when they display adhesive proteins on their outer membrane surfaces, which then form bridges between microbes. Aggregation protects the inner microbes from harsh environmental conditions such as high concentrations of antibiotics, high salt conditions, and fluctuations in pH. The protective effects of microbial aggregation make it an attractive target for improving the ability of probiotic strains to persist in the gut environment. However, it remains challenging to achieve synthetic microbial aggregation using natural adhesive proteins because these proteins frequently mediate microbial virulence. OBJECTIVES: Construction of synthetic proteins that mediate aggregation between microbes to enhance the survival of cells delivered to stressful environments. METHODS: We construct synthetic adhesins by fusing adhesive protein domains to surface display peptides. The resulting aggregated populations of bacteria are characterized using immunofluorescence, microscopy, flow cytometry, and quantification of colony forming units. RESULTS: We assemble a series of synthetic adhesins, demonstrate their display on the outer membrane of Escherichia coli, and show that they mediate bacterial aggregation. Further engineering of the size and motif composition of the adhesive domain shows that principles from natural adhesins can be applied to our synthetic adhesins. Finally, we show that aggregation allows E. coli cells to resist treatment with antimicrobial peptides and survive inside the gut of Caenorhabditis elegans. CONCLUSIONS: Our results demonstrate that synthetic aggregation can allow bacteria to resist biocidal environmental conditions. Synthetic adhesins may be used to facilitate microbial colonization of previously inaccessible environmental niches, either in remote natural environments or inside living organisms.

Magnetic Separation of Elastin-like Polypeptide Receptors for Enrichment of Cellular and Molecular Targets.

Protein-conjugated magnetic nanoparticles (mNPs) are promising tools for a variety of biomedical applications, from immunoassays and biosensors to theranostics and drug-delivery. In such applications, conjugation of affinity proteins (e.g., antibodies) to the nanoparticle surface many times compromises biological activity and specificity, leading to increased reagent consumption and decreased assay performance. To address this problem, we engineered a biomolecular magnetic separation system that eliminates the need to chemically modify nanoparticles with the capture biomolecules or synthetic polymers of any kind. The system consists of (i) thermoresponsive magnetic iron oxide nanoparticles displaying poly(N-isopropylacrylamide) (pNIPAm), and (ii) an elastin-like polypeptide (ELP) fused with the affinity protein Cohesin (Coh). Proper design of pNIPAm-mNPs and ELP-Coh allowed for efficient cross-aggregation of the two distinct nanoparticle types under collapsing stimuli, which enabled magnetic separation of ELP-Coh aggregates bound to target Dockerin (Doc) molecules. Selective resolubilization of the ELP-Coh/Doc complexes was achieved under intermediate conditions under which only the pNIPAm-mNPs remained aggregated. We show that ELP-Coh is capable of magnetically separating and purifying nanomolar quantities of Doc as well as eukaryotic whole cells displaying the complementary Doc domain from diluted human plasma. This modular system provides magnetic enrichment and purification of captured molecular targets and eliminates the requirement of biofunctionalization of magnetic nanoparticles to achieve bioseparations. Our streamlined and simplified approach is amenable for point-of-use applications and brings the advantages of ELP-fusion proteins to the realm of magnetic particle separation systems.

Yeast-based assays for screening 11ß-HSD1 inhibitors.

BACKGROUND: Intracellular metabolism of glucocorticoid hormones plays an important role in the pathogenesis of metabolic syndrome and regulates, among many physiological processes, collagen metabolism in skin. At the peripheral level the concentration of active glucocorticoids is mainly regulated by the 11ß-hydroxysteroid dehydrogenase 1 (11ß-HSD1) enzyme, involved in the conversion of cortisone into the biologically active hormone cortisol. Cortisol interacts with the glucocorticoid receptor and regulates the expression of different classes of genes within the nucleus. Due to its implication in glucocorticoid metabolism, the inhibition of 11ß-HSD1 activity has become a dominant strategy for the treatment of metabolic syndrome. Moreover, inhibitors of this target enzyme can be used for development of formulations to counteract skin ageing. Here we present the construction of two yeast cell based assays that can be used for the screening of novel 11ß-HSD1 inhibitors. RESULTS: The yeast Saccharomyces cerevisiae is used as a host organism for the expression of human 11ß-HSD1 as well as a genetically encoded assay system that allows intracellular screening of molecules with 11ß-HSD1 inhibitory activity. As proof of concept the correlation between 11ß-HSD1 inhibition and fluorescent output signals was successfully tested with increasing concentrations of carbenoxolone and tanshinone IIA, two known 11ß-HSD1 inhibitors. The first assay detects a decrease in fluorescence upon 11ß-HSD1 inhibition, whereas the second assay relies on stabilization of yEGFP upon inhibition of 11ß-HSD1, resulting in a positive read-out and thus minimizing the rate of false positives sometimes associated with read-outs based on loss of signals. Specific inhibition of the ABC transporter Pdr5p improves the sensitivity of the assay strains to cortisone concentrations by up to 60 times. CONCLUSIONS: Our yeast assay strains provide a cost-efficient and easy to handle alternative to other currently available assays for the screening of 11ß-HSD1 inhibitors. These assays are designed for an initial fast screening of large numbers of compounds and enable the selection of cell permeable molecules with target inhibitory activity, before proceeding to more advanced selection processes. Moreover, they can be employed in yeast synthetic biology platforms to reconstitute heterologous biosynthetic pathways of drug-relevant scaffolds for simultaneous synthesis and screening of 11ß-HSD1 inhibitors at intracellular level.