Protein Engineering Research Articles

Advancement of uridine diphosphate-dependent glycosyltransferases (UGTs) in the glycosylation modification of natural products and their protein engineering

Abstract Natural products have garnered significant attention due to their exceptional industrial and medicinal value. Glycosylation, a crucial structural modification in these products, is typically mediated by uridine diphosphate-dependent glycosyltransferases (UGTs). These enzymes not only enhance the physicochemical properties of natural products but also markedly increase their biological activity. This review provides an in-depth exploration of the characteristic features of UGTs and their diverse applications in the glycosylation modification of natural products, encompassing terpenoids, flavonoids, alkaloids, phenolic acids, and amide compounds. Special emphasis has been placed on the latest strategies in UGTs’ protein engineering and high-throughput screening methods, which provides insights into the study of UGTs' protein engineering and facilitate their application in the fields of food and medicine.

Toward the Prediction of Binding Events in Very Flexible, Allosteric, Multidomain Proteins.

Knowledge of the structures formed by proteins and small molecules is key to understand the molecular principles of chemotherapy and for designing new and more effective drugs. During the early stage of a drug discovery program, it is customary to predict ligand-protein complexes in silico, particularly when screening large compound databases. While virtual screening based on molecular docking is widely used for this purpose, it generally fails in mimicking binding events associated with large conformational changes in the protein, particularly when the latter involve multiple domains. In this work, we describe a new methodology to generate bound-like conformations of very flexible and allosteric proteins bearing multiple binding sites by exploiting only information on the unbound structure and the putative binding sites. The protocol is validated on the paradigm enzyme adenylate kinase, for which we generated a significant fraction of bound-like structures. A fraction of these conformations, employed in ensemble-docking calculations, allowed to find native-like poses of substrates and inhibitors (binding to the active form of the enzyme), as well as catalytically incompetent analogs (binding the inactive form). Our protocol provides a general framework for the generation of bound-like conformations of challenging drug targets that are suitable to host different ligands, demonstrating high sensitivity to the fine chemical details that regulate protein's activity. We foresee applications in virtual screening, in the prediction of the impact of amino acid mutations on structure and dynamics, and in protein engineering.

Proteins for Hyperelastic Materials.

Meticulous engineering and the yielded hyperelastic performance of structural proteins represent a new frontier in developing next-generation functional biomaterials. These materials exhibit outstanding and programmable mechanical properties, including elasticity, resilience, toughness, and active biological characteristics, such as degradability and tissue repairability, compared with their chemically synthetic counterparts. However, there are several critical issues regarding the preparation approaches of hyperelastic protein-based materials: limited natural sequence modules, non-hierarchical assembly, and imbalance between compressive and tensile elasticity, leading to unmet demands. Therefore, it is pivotal to develop an alternative strategy for biofabricating hyperelastic materials. Herein, the molecular design, engineering, and property regulation of hyperelastic structural proteins are overviewed. First, methodologies for deeper exploration of mechanical modules, including machine learning-aided de novo design, random mutations of natural sequences, and multiblock fusion techniques, are actively introduced. These methodologies facilitate the generation of elastomeric protein modules and demonstrate enhanced structural and functional versatility. Subsequently, assembly tactics of hyperelastic proteins (i.e., physical modulation, genetic adaptations, and chemical modifications) are reviewed, yielding hierarchically ordered structures. Finally, advances in biophysical techniques for more nuanced characterization of protein ensembles are discussed, unveiling the tuning mechanisms of protein elasticity across scales. Future developments in structural hyperelastic protein-based biomaterials are also envisioned.

Protein Engineering for Industrial Biocatalysis: Principles, Approaches, and Lessons from Engineered PETases

Protein engineering has emerged as a transformative field in industrial biotechnology, enabling the optimization of enzymes to meet stringent industrial demands for stability, specificity, and efficiency. This review explores the principles and methodologies of protein engineering, emphasizing rational design, directed evolution, semi-rational approaches, and the recent integration of machine learning. These strategies have significantly enhanced enzyme performance, even rendering engineered PETase industrially relevant. Insights from engineered PETases underscore the potential of protein engineering to tackle environmental challenges, such as advancing sustainable plastic recycling, paving the way for innovative solutions in industrial biocatalysis. Future directions point to interdisciplinary collaborations and the integration of emerging machine learning technologies to revolutionize enzyme design.

Recent Advances in Protein Engineering and Synthetic Applications of Amino Acid Transaminases

Amino acid transaminases (ATs) have garnered considerable attention in recent years as promising biocatalysts for the synthesis of high‐value chiral chemicals, including both natural and non‐canonical amino acids. These enzymes catalyze the transfer of amino groups from amino acids to keto acids, playing a pivotal role in various biological processes and industrial applications. Characterized by their high turnover rates, remarkable enantioselectivity, and broad substrate specificity, ATs exhibit exceptional versatility and potential. This review presents a comprehensive overview of the classification, reaction mechanisms, and activity assays of ATs. More crucially, we delve into the recent advancements in protein engineering of ATs through directed evolution and rational/semi‐rational design strategies, which have been instrumental in addressing limitations such as low catalytic efficiency and stability. Furthermore, we survey the recent synthetic applications of ATs in the production of aliphatic and aromatic amino acids, highlighting smart amino donors and coupling methods that effectively shift the equilibrium of transamination reactions, as well as enzyme cascades that further expand the scope of reactions. By bridging gaps in research on ATs, this review aims to provide valuable insights and guide for future developments in the field of biocatalysis, ultimately fostering their continued utilization and advancement.

Droplet microfluidic screening to engineer angiotensin-converting enzyme 2 (ACE2) catalytic activity

BackgroundAngiotensin-Converting Enzyme 2 (ACE2) is a crucial peptidase in human peptide hormone signaling, catalyzing the conversion of Angiotensin-II to Angiotensin-(1–7), which activates the Mas receptor and elicits vasodilation, increased blood flow, reduced inflammation, and decreased pathological tissue remodeling. This study leverages protein engineering to enhance ACE2’s therapeutic potential for treating conditions such as respiratory viral infections, acute respiratory distress syndrome, and diabetes. Surrogate substrates used in traditional high-throughput screening methods for peptidases often fail to accurately mimic native substrates, leading to less effective enzyme variants. Here, we developed an ultra-high-throughput droplet microfluidic platform to screen peptidases on native peptide substrates. Our assay detects substrate cleavage via free amino acid release, providing a precise measurement of biologically relevant peptidase activity.ResultsUsing this new platform, we screened a large library of ACE2 variants, identifying position 187 as a hotspot for enhancing enzyme activity. Further focused screening revealed the K187T variant, which exhibited a fourfold increase in catalytic efficiency (kcat/KM) over wild-type ACE2.ConclusionsThis work demonstrates the potential of droplet microfluidics for therapeutic peptidase engineering, offering a robust and accessible method to optimize enzyme properties for clinical applications.

Monitoring Dynamic Conformations of a Single Fluorescent Molecule Inside a Protein Cavity.

Fluorescence nanoscopy and single-molecule methods are entering the realm of structural biology, breaking new ground for dynamic structural measurements at room temperature and liquid environments. Here, single-molecule localization microscopy, polarization-dependent single-molecule excitation, and protein engineering are combined to determine the orientation of a fluorophore forming hydrogen bonds inside a protein cavity. The observed conformations are in good agreement with molecular dynamics simulations, enabling a new, more realistic interplay between experiments and simulations to identify stable conformations and the key interactions involved. Furthermore, jumps between conformations can be monitored with a precision of 3° and a time resolution of a few seconds, confirming the potential of this methodology for retrieving dynamic structural information of nanoscopic biological systems under physiologically compatible conditions.

Modern Approaches to Protein Constructions: A Comprehensive Review of Computational Tools and Databases for De Novo Protein Design and Engineering

ABSTRACTThe field of protein engineering has witnessed transformative advancements, with computational tools and databases driving novel innovations in de novo protein design. This review consolidates and critiques a comprehensive range of modern computational resources, offering a unique focus on their applications across diverse domains, including protein stability prediction, posttranslational modification analysis, and mutation effect evaluation. Key contributions include a detailed examination of tools integrating machine learning and artificial intelligence to enhance predictive accuracy and streamline protein engineering workflows. By highlighting underexplored tools and novel methodologies, such as advanced protein–ligand interaction predictors and neural network–based stability assessment models, this study establishes itself as a unique reference for researchers aiming to develop tailored proteins for therapeutic, industrial, and biomedical applications.

Altering the mechanical properties of self-assembled filaments through engineering of EspA bacterial protein.

Protein-based biomaterials are in high demand due to their high biocompatibility, non-toxicity, and biodegradability. In this study, we explore the bacterial E. coli secreted protein A (EspA), which self-assembles into long extracellular filaments, as a potential building block for new protein-based biomaterials. We investigated the morphological and mechanical properties of EspA filaments and how protein engineering can modify them. Our study include three types of filaments: natural EspA filaments, full-length recombinant EspA filaments, and truncated recombinant EspA filaments lacking a third of the original codon region. The recombinant EspA proteins formed curly, thin filaments with higher longitudinal elasticity (shorter persistence length) compared to the natural, linear filaments. Additionally, the recombinant filaments had a radial elastic modulus about an order of magnitude lower than the natural filaments. The truncated recombinant filaments had a higher radial modulus than the full-length ones, and unlike the purely elastic natural filaments, recombinant filaments were less compliant with the applied force that penetrated them. These differences underscore the potential to modulate EspA filament properties through protein sequence mutations. Our findings suggest EspA as a fundamental element for developing a new biomaterial with a hierarchical structure, enabling the fabrication of macroscopic substances from self-assembled EspA-modulated filaments.

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.

Compromising the immunogenicity of diphtheria toxin-based immunotoxins through epitope engineering: An in silico approach.

Immunotoxins are genetically engineered recombinant proteins consisting of a targeting moiety, such as an antibody, and a cytotoxic toxin moiety of microbial origin. Pseudomonas exotoxin A and diphtheria toxin (DT) have been abundantly used in immunotoxins, with the latter applied as the toxin moiety of the FDA-approved drug Denileukin diftitox (ONTAK®). However, the use of immunotoxins provokes an adverse immune response in the host body against the toxin moiety, limiting their efficacy. In silico approaches have received increasing attention in protein engineering. In this study, the epitopes responsible for immunogenicity were identified through multiple platforms. By subtracting conserved and ligand-binding residues, K33, T111, and E112 were identified as common epitopes across all platforms. Substitution analysis evaluated alternative residues regarding their impact on protein stability, considering 19 different amino acid substitutions. Among the mutants explored, the T111A-E112G mutant exhibited the most destabilizing substitution for DT, thereby reducing immunogenicity. Finally, a 3D model of the mutant was generated and verified. The model was then docked with its native ligand NADH, and the complex's molecular behavior was simulated using molecular dynamics.

Surface engineering of amine transaminases to control their region-selective immobilization.

The industrial use of enzymes often requires their immobilization to facilitate downstream processing and enable reuse. However, controlling enzyme orientation during immobilization is challenging and typically restricted to the N- and C-terminal regions. In this work, we propose a strategy to immobilize more active and stable amine transaminases (ATAs) by combining protein engineering with immobilization techniques. Our approach involves the structure-guided insertion of histidine clusters (His-clusters) at flexible regions of ATA subunit interfaces, enabling immobilization on cobalt-chelated carriers. By screening multiple ATAs from various microbial sources and testing different His-clusters for each, we identified the most active and stable heterogeneous biocatalysts. Notably, the immobilized H2A variant of Chromobacterium violaceum ATA (CvATA-2HA) exhibited the highest activity per mass of biocatalyst (4 U g-1). Meanwhile, the H3 variant of Pseudomonas fluorescens ATA (PfATA-H3) showed enhanced thermostability and DMSO resistance, being approximately 2.5 times more stable than its free counterpart. Overall, our findings highlight the impact of enzyme surface engineering on immobilization efficiency. The strategic placement of His-clusters enabled region-directed immobilization, improving both the activity and stability of specific ATA variants.

Stable: A protein engineering platform for enhancing stability and delivery of alpha-galactosidase A for Fabry disease therapy

Stable: A protein engineering platform for enhancing stability and delivery of alpha-galactosidase A for Fabry disease therapy

Design and Engineering of Silver Nanomushroom Arrays as a Universal Solid-State SERS Platform for the Label-Free, Sensitive, and Quantitative Detection of Trace Proteins.

Surface-enhanced Raman scattering (SERS) is an ultrasensitive optical technique that is critical for protein detection and essential for identifying protein structure and concentrations in various biomedical and diagnostic applications. However, achieving highly sensitive and reproducible SERS signals for label-free proteins remains challenging due to their weak Raman signals and structural complexity. In this study, silver nanomushroom arrays (Ag NMAs) as SERS substrates were readily prepared and surface-engineered using a facile template-assisted micro- and nanofabrication approach. The surface of the substrate exhibits nanoscale roughness, long-range order, and hydrophilicity, enabling rapid and uniform dispersion of protein molecules. These molecules are anchored through Ag-S bonds, resulting in ultrasensitive Raman signals driven by strong electromagnetic enhancement effects. The highly ordered array structure improves signal repeatability, achieving a relative standard deviation of as low as 4.32%. Additionally, utilizing the silicon characteristic peak of the SERS substrate as an internal standard significantly reduces measurement errors, allowing for reliable and precise quantitative detection of protein molecules, with a linear correlation coefficient (R2) exceeding 0.96. Ultrasensitive SERS detection and effective protein discrimination via principal component analysis further validate the Ag NMA substrate's potential for universal trace protein detection. This study presents an advanced SERS platform for the sensitive and rapid detection of trace proteins, showcasing significant potential in pharmaceutical research, metabolic studies, diagnostic medicine, and protein engineering.

Biocatalytic enantioselective formation and ring-opening of oxetanes

Although biocatalysis offers complementary or alternative approaches to traditional synthetic methods, the limited range of available enzymatic reactions currently poses challenges in synthesizing a diverse array of desired compounds. Consequently, there is a significant demand for developing novel biocatalytic processes to enable reactions that were previously unattainable. Herein, we report the discovery and subsequent protein engineering of a unique halohydrin dehalogenase to develop a biocatalytic platform for enantioselective formation and ring-opening of oxetanes. This biocatalytic platform, exhibiting high efficiency, excellent enantioselectivity, and broad scopes, facilitates the preparative-scale synthesis of chiral oxetanes and a variety of chiral γ-substituted alcohols. Additionally, both the enantioselective oxetane formation and ring-opening processes are proven scalable for large-scale transformations at high substrate concentrations, and can be integrated efficiently in a one-pot, one-catalyst cascade system. This work expands the enzymatic toolbox for non-natural reactions and will promote further exploration of the catalytic repertoire of halohydrin dehalogenases in synthetic and pharmaceutical chemistry.

Engineering synthetic signaling receptors to enable erythropoietin-free erythropoiesis

Blood transfusion plays a vital role in modern medicine, but frequent shortages occur. Ex vivo manufacturing of red blood cells (RBCs) from universal donor cells offers a potential solution, yet the high cost of recombinant cytokines remains a barrier. Erythropoietin (EPO) signaling is crucial for RBC development, and EPO is among the most expensive media components. To address this challenge, we develop highly optimized small molecule-inducible synthetic EPO receptors (synEPORs) using design-build-test cycles and genome editing. By integrating synEPOR at the endogenous EPOR locus in O-negative induced pluripotent stem cells, we achieve equivalent erythroid differentiation, transcriptomic changes, and hemoglobin production using small molecules compared to EPO-supplemented cultures. This approach dramatically reduces culture media costs. Our strategy not only addresses RBC production challenges but also demonstrates how protein and genome engineering can introduce precisely regulated cellular behaviors, potentially improving scalable manufacturing of a wide range of clinically relevant cell types.

Isolation of PCSK9-specific nanobodies from synthetic libraries using a combined protein selection strategy

Nanobodies (Nbs) hold great potential to replace conventional antibodies in various biomedical applications. However, conventional methods for their discovery can be time-consuming and expensive. We have developed a reliable protein selection strategy that combines magnetic activated cell sorting (MACS)-based screening of yeast surface display (YSD) libraries and functional ligand-binding identification by Tat-based recognition of associating proteins (FLI-TRAP) to isolate antigen-specific Nbs from synthetic libraries. This combined process enabled isolation of three unique Nb clones (NbT15, NbT21, and NbT22) that all bound specifically to a target antigen, namely proprotein convertase subtilisin/kexin type 9 (PCSK9) as well as a gain-of-function PCSK9 mutant (D374Y). All three clones bound to PCSK9 and blocked the interaction between the low-density lipoprotein receptor (LDLR) and either wild-type PCSK9 or the D374Y mutant. Overall, our combined protein selection method enables rapid and straightforward identification of potent antigen-specific Nbs in a manner that can be executed in a basic laboratory setting without the need for specialized equipment. We anticipate that our strategy will be a valuable addition to the protein engineering toolkit, allowing development of Nbs or virtually any other synthetic binding protein for a wide range of applications.

Protein biochemistry and engineering drive the development of a carbonic anhydrase-based carbon dioxide sequestration strategy.

The sequestration of carbon dioxide using carbonic anhydrase (CA) is one of the most effective methods for mitigating global warming. The burning of fossil fuels releases large quantities of flue gas; because of its high temperature and of the alkaline conditions required for CaCO3 precipitation in the mineralization process, thermo-alkali-stable CAs are needed. In this context, Manyumwa etal. conducted a biochemical characterization of three CAs derived from thermophilic bacteria. They then employed a rational design approach to enhance the specific activity and stability of the enzyme from the hydrothermal vent species Persephonella sp. KM09-Lau-8.

Screening a 681-membered yeast collection for the secretion of proteins with antifungal activity.

Fungal pathogens pose a threat to human health and food security. Few antifungals are available and resistance to all has been reported. Novel strategies to control plant and human pathogens as well as food spoilers are urgently required. Environmental yeasts provide a functionally diverse, yet underexploited potential for fungal control based on their natural competition via the secretion of proteins and other small molecules such as iron chelators, volatile organic compounds or biosurfactants. However, there is a lack of standardized workflows to systematically access application-relevant yeast-based compounds and understand their molecular functioning. Towards this goal, we developed a workflow to identify and characterize yeast isolates that are active against spoilage yeasts and relevant human and plant pathogens, herein focusing on discovering yeasts that secrete antifungal proteins. The workflow includes the classification of the secreted molecules and cross-comparison of their antifungal capacity using an independent synthetic calibrant. Our workflow delivered a collection of 681 yeasts of which 212 isolates (31%) displayed antagonism against at least one target strain. While 57.5% of the active yeasts showed iron-depended antagonism, likely due to pulcherrimin-like iron chelators, 31.7% secreted antifungal proteins. Those yeast candidates clustered within twelve OTUs, showed narrow and broad target spectra, and several showed a broad pH and temperature activity profile. Given the tools for yeast biotechnology and protein engineering available, our collection can serve as a rich starting point for genetic and molecular characterization of the various antifungal phenotypes, their mode of action and their future exploitation.

Semirational Protein Engineering of a Decarboxylative Aldolase for the Regiodivergent and Stereodivergent Synthesis of Cyclic Imino Acids

AbstractAldolases are powerful C−C bond‐forming enzymes for asymmetric organic synthesis because of their supreme stereoselectivity, compatibility with diverse electrophiles and nucleophiles, and promising scalability. Stereodivergent engineering of aldolases to tune the selectivity for the synthesis of stereoisomers of chiral molecules is highly desirable but has rarely been reported. Herein we report the semirational engineering of the decarboxylative aldolase UstD with a focused rational iterative site‐specific mutagenesis (FRISM) strategy to perform a C−C bond‐forming reaction with dione electrophiles. The variant obtained from a small mutant library showed divergent regioselectivity and diastereoselectivity compared to the wild‐type enzyme, resulting in the production of 30 cyclic imino acids with stereocenters at the α and γ positions. Molecular dynamics simulation and kinetic data revealed the basis of selectivity.