YESS 2.0, a Tunable Platform for Enzyme Evolution, Yields Highly Active TEV Protease Variants

High-Resolution Substrate Specificity Profiling of SARS-CoV-2 Mpro; Comparison to SARS-CoV Mpro

The SARS-CoV-2 Mpro protease from COVID-19 cleaves the pp1a and pp2b polyproteins at 11 sites during viral maturation and is the target of Nirmatrelvir, one of the two components of the frontline treatment sold as Paxlovid. We used the YESS 2.0 platform, combining protease and substrate expression in the yeast endoplasmic reticulum with fluorescence-activated cell sorting and next-generation sequencing, to carry out the high-resolution substrate specificity profiling of SARS-CoV-2 Mpro as well as the related SARS-CoV Mpro from SARS 2003. Even at such a high level of resolution, the substrate specificity profiles of both enzymes are essentially identical. The population of cleaved substrates isolated in our sorts is so deep, the relative catalytic efficiencies of the different cleavage sites on the SARS-CoV-2 polyproteins pp1a and pp2b are qualitatively predicted. These results not only demonstrated the precise and reproducible nature of the YESS 2.0/NGS approach to protease substrate specificity profiling but also should be useful in the design of next generation SARS-CoV-2 Mpro inhibitors, and by analogy, SARS-CoV Mpro inhibitors as well.

A combinatorial strategy for HRV 3C protease engineering to achieve the N-terminal free cleavage

Human rhinovirus 3C protease (HRV 3CP) has a high specificity against the substrate of LEVLFQ↓G at P1' site, which plays an important role in biotechnology and academia as a fusion tag removal tool. However, a non-ignorable limitation is that an extra residue of Gly would remain at the N terminus of the recombinant target protein after cleavage with HRV 3CP, thus potentially causing protein mis-functionality or immunogenicity. Here, we developed a combinatorial strategy by integrating structure-guided library design and high-throughput screening of eYESS approach for HRV 3CP engineering to expand its P1' specificity. Finally, a C3 variant was obtained, exhibiting a broad substrate P1' specificity to recognize 20 different amino acids with the highest activity against LEVLFQ↓M (kcat/KM = 3.72 ± 0.04 mM-1∙s-1). Further biochemical and NGS-mediated substrate profiling analysis showed that C3 variant still kept its substrate stringency at P1 site and good residue tolerance at P2' site, but with an expanded P1' specificity. Structural simulation of C3 indicated a reconstructed S1' binding pocket as well as new interactions with the substrates. Overall, our studies here prompt not only the practical applications and understanding of substrate recognition mechanisms of HRV 3CP, also provide new tools for other enzyme engineering.

Advances in recombinant protease production: current state and perspectives

Proteases, enzymes that catalyze the hydrolysis of peptide bonds in proteins, are important in the food industry, biotechnology, and medical fields. With increasing demand for proteases, there is a growing emphasis on enhancing their expression and production through microbial systems. However, proteases' native hosts often fall short in high-level expression and compatibility with downstream applications. As a result, the recombinant production of proteases has become a significant focus, offering a solution to these challenges. This review presents an overview of the current state of protease production in prokaryotic and eukaryotic expression systems, highlighting key findings and trends. In prokaryotic systems, the Bacillus spp. is the predominant host for proteinase expression. Yeasts are commonly used in eukaryotic systems. Recent advancements in protease engineering over the past five years, including rational design and directed evolution, are also highlighted. By exploring the progress in both expression systems and engineering techniques, this review provides a detailed understanding of the current landscape of recombinant protease research and its prospects for future advancements.

Leveraging yeast sequestration to study and engineer posttranslational modification enzymes

Enzymes that catalyze posttranslational modifications (PTMs) of peptides and proteins (PTM-enzymes)-proteases, protein ligases, oxidoreductases, kinases, and other transferases-are foundational to our understanding of health and disease and empower applications in chemical biology, synthetic biology, and biomedicine. To fully harness the potential of PTM-enzymes, there is a critical need to decipher their enzymatic and biological mechanisms, develop molecules that can probe and modulate them, and endow them with improved and novel functions. These objectives are contingent upon implementation of high-throughput functional screens and selections that interrogate large sequence libraries to isolate desired PTM-enzyme properties. This review discusses the principles of Saccharomyces cerevisiae organelle sequestration to study and engineer PTM-enzymes. These include outer membrane sequestration, specifically methods that modify yeast surface display, and cytoplasmic sequestration based on enzyme-mediated transcription activation. Furthermore, we present a detailed discussion of yeast endoplasmic reticulum sequestration for the first time. Where appropriate, we highlight the major features and limitations of different systems, specifically how they can measure and control enzyme catalytic efficiencies. Taken together, yeast-based high-throughput sequestration approaches significantly lower the barrier to understanding how PTM-enzymes function and how to reprogram them.

Mechanism of Mutation-Induced Effects on the Catalytic Function of TEV Protease: A Molecular Dynamics Study

Tobacco etch virus protease (TEVp) is wildly exploited for various biotechnological applications. These applications take advantage of TEVp's ability to cleave specific substrate sequences to study protein function and interactions. A major limitation of this enzyme is its relatively slow catalytic rate. In this study, MD simulations were conducted on TEV enzymes and known highly active mutants (eTEV and uTEV3) to explore the relationship between mutation, conformation, and catalytic function. The results suggest that mutations distant from the active site can influence the substrate-binding pocket through interaction networks. MD analysis of eTEV demonstrates that, by stabilizing the orientation of the substrate at the catalytic site, mutations that appropriately enlarge the substrate-binding pocket will be beneficial for Kcat, enhancing the catalytic efficiency of the enzyme. On the contrary, mutations in uTEV3 reduced the flexibility of the active pocket and increased the hydrogen bonding between the substrate and enzyme, resulting in higher affinity. At the same time, the MD simulation demonstrates that mutations outside of the active site residues could affect the dynamic movement of the binding pocket by altering residue networks and communication pathways, thereby having a profound impact on reactivity. These findings not only provide a molecular mechanistic explanation for the excellent mutants, but also serve as a guiding framework for rational computational design.

The lipid-binding D4 domain of perfringolysin O facilitates the active loading of exogenous cargo into extracellular vesicles

Whereas extracellular vesicles (EVs) have been engineered for cargo loading, innovative strategies for it can still be developed. Here, we describe domain 4 (D4), a cholesterol-binding domain derived from perfringolysin O, as a viable candidate for EV cargo loading. D4 and its mutants localized to the plasma membrane and the membranes of different vesicular structures in the cytoplasm, and facilitate the transport of proteins of interest (POIs) into EVs. D4-EVs were internalized by recipient cells analogous to EVs engineered with CD9. Intracellular cargo discharge from D4-EVs was successfully detected with the assistance of vesicular stomatitis virus glycoprotein. This study presents a novel strategy for recruiting POIs into EVs via a lipid-binding domain that ensures content release in recipient cells.

Modular and integrative activity reporters enhance biochemical studies in the yeast ER

The yeast endoplasmic reticulum sequestration and screening (YESS) system is a broadly applicable platform to perform high-throughput biochemical studies of post-translational modification enzymes (PTM-enzymes). This system enables researchers to profile and engineer the activity and substrate specificity of PTM-enzymes and to discover inhibitor-resistant enzyme mutants. In this study, we expand the capabilities of YESS by transferring its functional components to integrative plasmids. The YESS integrative system yields uniform protein expression and protease activities in various configurations, allows one to integrate activity reporters at two independent loci and to split the system between integrative and centromeric plasmids. We characterize these integrative reporters with two viral proteases, Tobacco etch virus (TEVp) and 3-chymotrypsin like protease (3CLpro), in terms of coefficient of variance, signal-to-noise ratio and fold-activation. Overall, we provide a framework for chromosomal-based studies that is modular, enabling rigorous high-throughput assays of PTM-enzymes in yeast.

A high-throughput screening platform for enzymes active on mucin-type O-glycoproteins

Mucin-type O-glycosylation is a post-translational modification present at the interface between cells where it has important roles in cellular communication. However, deciphering the function of O-glycoproteins and O-glycans can be challenging, especially as few enzymes are available for their assembly or selective degradation. Here, to address this deficiency, we developed a genetically encoded screening methodology for the discovery and engineering of the diverse classes of enzymes that act on O-glycoproteins. The method uses Escherichia coli that have been engineered to produce an O-glycosylated fluorescence resonance energy transfer probe that can be used to screen for O-glycopeptidase activity. Subsequent cleavage of the substrate by O-glycopeptidases provides a read-out of the glycosylation state of the probe, allowing the method to also be used to assay glycosidases and glycosyltransferases. We further show the potential of this methodology in the first ultrahigh-throughput-directed evolution of an O-glycopeptidase.

Prediction and design of protease enzyme specificity using a structure-aware graph convolutional network

Site-specific proteolysis by the enzymatic cleavage of small linear sequence motifs is a key posttranslational modification involved in physiology and disease. The ability to robustly and rapidly predict protease-substrate specificity would also enable targeted proteolytic cleavage by designed proteases. Current methods for predicting protease specificity are limited to sequence pattern recognition in experimentally derived cleavage data obtained for libraries of potential substrates and generated separately for each protease variant. We reasoned that a more semantically rich and robust model of protease specificity could be developed by incorporating the energetics of molecular interactions between protease and substrates into machine learning workflows. We present Protein Graph Convolutional Network (PGCN), which develops a physically grounded, structure-based molecular interaction graph representation that describes molecular topology and interaction energetics to predict enzyme specificity. We show that PGCN accurately predicts the specificity landscapes of several variants of two model proteases. Node and edge ablation tests identified key graph elements for specificity prediction, some of which are consistent with known biochemical constraints for protease:substrate recognition. We used a pretrained PGCN model to guide the design of protease libraries for cleaving two noncanonical substrates, and found good agreement with experimental cleavage results. Importantly, the model can accurately assess designs featuring diversity at positions not present in the training data. The described methodology should enable the structure-based prediction of specificity landscapes of a wide variety of proteases and the construction of tailor-made protease editors for site-selectively and irreversibly modifying chosen target proteins.

Modular and integrative activity reporters enhance biochemical studies in the yeast ER

The yeast endoplasmic reticulum sequestration and screening (YESS) system is a generalizable platform that has become highly useful to investigate post-translational modification enzymes (PTM-enzymes). This system enables researchers to profile and engineer the activity and substrate specificity of PTM-enzymes and to discover inhibitor-resistant enzyme mutants. In this study, we expand the capabilities of YESS by transferring its functional components to integrative plasmids. The YESS integrative system yields uniform protein expression and protease activities in various configurations, allows one to integrate activity reporters at two independent loci and to split the system between integrative and centromeric plasmids. We characterize these integrative reporters with two viral proteases, Tobacco etch virus (TEVp) and 3-chymotrypsin like protease (3CL pro ), in terms of coefficient of variance, signal-to-noise ratio and fold-activation. Overall, we provide a framework for chromosomal-based studies that is modular, enabling rigorous high-throughput assays of PTM-enzymes in yeast.

Antibody-guided proteases enable selective and catalytic degradation of challenging therapeutic targets

The exquisite specificity, natural biological functions, and favorable development properties of antibodies make them highly effective agents as drugs. Monoclonal antibodies are particularly strong as inhibitors of systemically accessible targets where trough-level concentrations can sustain full target occupancy. Yet beyond this pharmacologic wheelhouse, antibodies perform suboptimally for targets of high abundance and those not easily accessible from circulation. Fundamentally, this restraint on broader application is due largely to the stoichiometric nature of their activity - one drug molecule is generally able to inhibit a maximum of two target molecules at a time. Enzymes in contrast are able to catalytically turnover multiple substrates, making them a natural sub-stoichiometric solution for targets of high abundance or in poorly accessible sites of action. However, enzymes have their own limitations as drugs, including, in particular the polypharmacology and broad specificity often seen with native enzymes. In this study, we introduce antibody-guided proteolytic enzymes to enable selective sub-stoichiometric turnover of therapeutic targets. We demonstrate that antibody-mediated substrate targeting can enhance enzyme activity and specificity, with proof of concept for two challenging target proteins, amyloid-β (Aβ) and immunoglobulin G (IgG). This work advances a new biotherapeutic platform that combines the favorable properties of antibodies and proteolytic enzymes to more effectively suppress high-bar therapeutic targets.

Prediction and Design of Protease Enzyme Specificity Using a Structure-Aware Graph Convolutional Network

Site-specific proteolysis by the enzymatic cleavage of small linear sequence motifs is a key post-translational modification involved in physiology and disease. The ability to robustly and rapidly predict protease substrate specificity would also enable targeted proteolytic cleavage - editing - of a target protein by designed proteases. Current methods for predicting protease specificity are limited to sequence pattern recognition in experimentally-derived cleavage data obtained for libraries of potential substrates and generated separately for each protease variant. We reasoned that a more semantically rich and robust model of protease specificity could be developed by incorporating the three-dimensional structure and energetics of molecular interactions between protease and substrates into machine learning workflows. We present Protein Graph Convolutional Network (PGCN), which develops a physically-grounded, structure-based molecular interaction graph representation that describes molecular topology and interaction energetics to predict enzyme specificity. We show that PGCN accurately predicts the specificity landscapes of several variants of two model proteases: the NS3/4 protease from the Hepatitis C virus (HCV) and the Tobacco Etch Virus (TEV) proteases. Node and edge ablation tests identified key graph elements for specificity prediction, some of which are consistent with known biochemical constraints for protease:substrate recognition. We used a pre-trained PGCN model to guide the design of TEV protease libraries for cleaving two non-canonical substrates, and found good agreement with experimental cleavage results. Importantly, the model can accurately assess designs featuring diversity at positions not present in the training data. The described methodology should enable the structure-based prediction of specificity landscapes of a wide variety of proteases and the construction of tailor-made protease editors for site-selectively and irreversibly modifying chosen target proteins.

A comprehensive survey of coronaviral main protease active site diversity in 3D: Identifying and analyzing drug discovery targets in search of broad specificity inhibitors for the next coronavirus pandemic

Although the rapid development of therapeutic responses to combat SARS-CoV-2 represents a great human achievement, it also demonstrates untapped potential for advanced pandemic preparedness. Cross-species efficacy against multiple human coronaviruses by the main protease (MPro) inhibitor nirmatrelvir raises the question of its breadth of inhibition and our preparedness against future coronaviral threats. Herein, we describe sequence and structural analyses of 346 unique MPro enzymes from all coronaviruses represented in the NCBI Virus database. Cognate substrates of these representative proteases were inferred from their polyprotein sequences. We clustered MPro sequences based on sequence identity and AlphaFold2-predicted structures, showing approximate correspondence with known viral subspecies. Predicted structures of five representative MPros bound to their inferred cognate substrates showed high conservation in protease:substrate interaction modes, with some notable differences. Yeast-based proteolysis assays of the five representatives were able to confirm activity of three on inferred cognate substrates, and demonstrated that of the three, only one was effectively inhibited by nirmatrelvir. Our findings suggest that comprehensive preparedness against future potential coronaviral threats will require continued inhibitor development. Our methods may be applied to candidate coronaviral MPro inhibitors to evaluate in advance the breadth of their inhibition and identify target coronaviruses potentially meriting advanced development of alternative countermeasures.

Strategies for enriching and characterizing proteins with inhibitory properties on the yeast surface

Display technologies are powerful tools for discovering binding proteins against a broad range of biological targets. However, it remains challenging to adapt display technologies for the discovery of proteins that inhibit the enzymatic activities of targets. Here, we investigate approaches for discovering and characterizing inhibitory antibodies in yeast display format using a well-defined series of constructs and the target matrix metalloproteinase-9. Three previously reported antibodies were used to create model libraries consisting of inhibitory, non-inhibitory, and non-binding constructs. Conditions that preferentially enrich for inhibitory clones were identified for both magnetic bead-based enrichments and fluorescence-activated cell sorting. Half maximal inhibitory concentration (IC50) was obtained through yeast titration assays. The IC50 of the inhibitory antibody obtained in yeast display format falls within the confidence interval of the IC50 value determined in soluble form. Overall, this study identifies strategies for the discovery and characterization of inhibitory clones directly in yeast display format.

Tyrosine Phosphorylation Screening on the Yeast Surface by Magnetic Bead Selection and FACS

The ability to understand and characterize phosphorylation is important to the study of cell signaling and to synthetic biology approaches. Current methods for characterizing kinase-substrate interactions are limited by their inherently low throughput and the heterogeneity of samples analyzed. Recent advances in yeast surface display techniques provide new opportunities for studying individual kinase-substrate interactions in a stimulus-independent fashion. Here, we describe techniques for building substrate libraries into full-length domains of interest that, when co-localized intracellularly with individual kinases, result in the display of phosphorylated domains on the yeast surface, as well as fluorescence-activated cell sorting and magnetic bead selection techniques for enriching from these libraries based on phosphorylation state.

High throughput mutagenesis and screening for yeast engineering

The eukaryotic yeast Saccharomyces cerevisiae is a model host utilized for whole cell biocatalytic conversions, protein evolution, and scientific inquiries into the pathogenesis of human disease. Over the past decade, the scale and pace of such studies has drastically increased alongside the advent of novel tools for both genome-wide studies and targeted genetic mutagenesis. In this review, we will detail past and present (e.g., CRISPR/Cas) genome-scale screening platforms, typically employed in the context of growth-based selections for improved whole cell phenotype or for mechanistic interrogations. We will further highlight recent advances that enable the rapid and often continuous evolution of biomolecules with improved function. Additionally, we will detail the corresponding advances in high throughput selection and screening strategies that are essential for assessing or isolating cellular and protein improvements. Finally, we will describe how future developments can continue to advance yeast high throughput engineering.

Between Protein Fold and Nucleophile Identity: Multiscale Modeling of the TEV Protease Enzyme-Substrate Complex

The cysteine protease from the tobacco etch virus (TEVp) is a well-known and widely utilized enzyme. TEVp's chymotrypsin-like fold is generally associated with serine catalytic triads that differ in terms of a reaction mechanism from the most well-studied papain-like cysteine proteases. The question of what dominates the TEVp mechanism, nucleophile identity, or structural composition has never been previously addressed. Here, we use enhanced sampling multiscale modeling to uncover that TEVp combines the features of two worlds in such a way that potentially hampers its activity. We show that TEVp cysteine is strictly in the anionic form in a free enzyme similar to papain. Peptide binding shifts the equilibrium toward the nucleophile's protonated form, characteristic of chymotrypsin-like proteases, although the cysteinyl anion form is still present and interconversion is rapid. This way cysteine protonation generates enzyme states that are a diversion from the most effective course of action, with only 13.2% of Michaelis complex sub-states able to initiate the reaction. As a result, we propose an updated view on the reaction mechanism catalyzed by TEVp. We also demonstrate that AlphaFold is able to construct protease-substrate complexes with high accuracy. We propose that our findings open a way for its industrious use in enzymological tasks. Unique features of TEVp discovered in this work open a discussion on the evolutionary history and trade-offs of optimizing serine triad-associated folds to cysteine as a nucleophile.

Reprogramming the Cleavage Specificity of Botulinum Neurotoxin Serotype B1

Proteases with reprogrammed specificity for nonnative substrates are highly desired in synthetic biology and biomedicine. However, generating reprogrammed proteases that are orthogonal and highly specific for a new target has been a major challenge. In this work, we sought to expand the versatility of protease systems by engineering an orthogonal botulinum neurotoxin serotype B (BoNT/B) protease that recognizes an orthogonal substrate. We designed and validated an orthogonal BoNT/B protease system in mammalian cells, combining mutations in the protease with compensatory mutations in the protease substrate and incorporating a truncated target sequence and then demonstrated use of this orthogonal BoNT/B protease-substrate combination to regulate complex transcriptional circuitry in mammalian cells. Transposing this platform into yeast, we demonstrated utility of this approach for in vivo protease evolution. We tested this platform with the newly designed orthogonal protease and then used it in a high-throughput screen to identify novel orthogonal protease/protease substrate combinations. While carrying out this work, we also generated new cleavage reporters that could be used to report botulinum toxin protease activity in mammalian cells using simple fluorescent readouts. We envision that these approaches will expand the applications of botulinum protease in new directions and aid in the development of new reprogrammed proteases.

Engineering Proteins Containing Noncanonical Amino Acids on the Yeast Surface

Yeast display has been used to advance many critical research areas, including the discovery of unique protein binders and biological therapeutics. In parallel, noncanonical amino acids (ncAAs) have been used to tailor antibody-drug conjugates and enable discovery of therapeutic leads. Together, these two technologies have allowed for generation of synthetic antibody libraries, where the introduction of ncAAs in yeast-displayed proteins allows for library screening for therapeutically relevant targets. The combination of yeast display with genetically encoded ncAAs increases the available chemistry in proteins and advances applications that require high-throughput strategies. In this chapter, we discuss methods for displaying proteins containing ncAAs on the yeast surface, generating and screening libraries of proteins containing ncAAs, preparing bioconjugates on the yeast surface in large scale, generating and screening libraries of aminoacyl-tRNA synthetases (aaRSs) for encoding ncAAs by using reporter constructs, and characterizing ncAA-containing proteins secreted from yeast. The experimental designs laid out in this chapter are generalizable for discovery of protein binders to a variety of targets and aaRS evolution to continue expanding the genetic code beyond what is currently available in yeast.

Yeast Surface Display: New Opportunities for a Time-Tested Protein Engineering System

Yeast surface display has proven to be a powerful tool for the discovery of antibodies and other novel binding proteins and for engineering the affinity and selectivity of existing proteins for their targets. In the decades since the first demonstrations of the approach, the range of yeast display applications has greatly expanded to include many different protein targets and has grown to encompass methods for rapid protein characterization. Here, we briefly summarize the development of yeast display methodologies and highlight several selected examples of recent applications to timely and challenging protein engineering and characterization problems.

Construction, Investigation and Application of TEV Protease Variants with Improved Oxidative Stability

Tobacco etch virus protease (TEVp) is a useful tool for removing fusion tags, but wild-type TEVp is less stable under oxidized redox state. In this work, we introduced and combined C19S, C110S and C130S into TEVp variants containing T17S, L56V, N68D, I77V and S135G to improve protein solubility, and S219V to inhibit self-proteolysis. The solubility and cleavage activity of the constructed variants in Escherichia coli strains including BL21(DE3), BL21(DE3)pLys, Rossetta(DE3) and Origami(DE3) under the same induction conditions were analyzed and compared. The desirable soluble amounts, activity, and oxidative stability were identified to be reluctantly favored in the TEVp. Unlike C19S, C110S and C130S hardly impacted on decreasing protein solubility in the BL21(DE3), but they contributed to improved tolerance to the oxidative redox state in vivo and in vitro. After two fusion proteins were cleaved by purified TEVp protein containing double mutations under the oxidized redox state, the refolded disulfide-rich bovine enterokinase catalytic domain or maize peroxidase with enhanced yields were released from the regenerated amorphous cellulose via affinity absorption of the cellulose-binding module as the affinity tag.

Directed Evolution Methods for Enzyme Engineering

Enzymes underpin the processes required for most biotransformations. However, natural enzymes are often not optimal for biotechnological uses and must be engineered for improved activity, specificity and stability. A rich and growing variety of wet-lab methods have been developed by researchers over decades to accomplish this goal. In this review such methods and their specific attributes are examined.