Engineering bio-brick protein scaffolds for organizing enzyme assemblies.

Enzyme scaffolding is an emerging approach for enhancing the catalytic efficiency of multi-enzymatic cascades by controlling their spatial organization and stoichiometry. This study introduces a novel family of engineered SCAffolding Bricks, named SCABs, utilizing the consensus tetratricopeptide repeat (CTPR) domain for organized multi-enzyme systems. Two SCAB systems are developed, one employing head-to-tail interactions with reversible covalent disulfide bonds, the other relying on non-covalent metal-driven assembly via engineered metal coordinating interfaces. Enzymes are directly fused to SCAB modules, triggering assembly in a non-reducing environment or by metal presence. A proof-of-concept with formate dehydrogenase (FDH) and L-alanine dehydrogenase (AlaDH) shows enhanced specific productivity by 3.6-fold compared to free enzymes, with the covalent stapling outperforming the metal-driven assembly. This enhancement likely stems from higher-order supramolecular assembly and improved NADH cofactor regeneration, resulting in more efficient cascades. This study underscores the potential of protein engineering to tailor scaffolds, leveraging supramolecular spatial-organizing tools, for more efficient enzymatic cascade reactions.

Graphormer supervised de novo protein design method and function validation.

Protein design is central to nearly all protein engineering problems, as it can enable the creation of proteins with new biological functions, such as improving the catalytic efficiency of enzymes. One key facet of protein design, fixed-backbone protein sequence design, seeks to design new sequences that will conform to a prescribed protein backbone structure. Nonetheless, existing sequence design methods present limitations, such as low sequence diversity and shortcomings in experimental validation of the designed functional proteins. These inadequacies obstruct the goal of functional protein design. To improve these limitations, we initially developed the Graphormer-based Protein Design (GPD) model. This model utilizes the Transformer on a graph-based representation of three-dimensional protein structures and incorporates Gaussian noise and a sequence random masks to node features, thereby enhancing sequence recovery and diversity. The performance of the GPD model was significantly better than that of the state-of-the-art ProteinMPNN model on multiple independent tests, especially for sequence diversity. We employed GPD to design CalB hydrolase and generated nine artificially designed CalB proteins. The results show a 1.7-fold increase in catalytic activity compared to that of the wild-type CalB and strong substrate selectivity on p-nitrophenyl acetate with different carbon chain lengths (C2-C16). Thus, the GPD method could be used for the de novo design of industrial enzymes and protein drugs. The code was released at https://github.com/decodermu/GPD.

Diverse models of cavity engineering in enzyme modification: Creation, filling, and reshaping.

Most enzyme modification strategies focus on designing the active sites or their surrounding structures. Interestingly, a large portion of the enzymes (60%) feature active sites located within spacious cavities. Despite recent discoveries, cavity-mediated enzyme engineering remains crucial for enhancing enzyme properties and unraveling folding-unfolding mechanisms. Cavity engineering influences enzyme stability, catalytic activity, specificity, substrate recognition, and docking. This article provides a comprehensive review of various cavity engineering models for enzyme modification, including cavity creation, filling, and reshaping. Additionally, it also discusses feasible tools for geometric analysis, functional assessment, and modification of cavities, and explores potential future research directions in this field. Furthermore, a promising universal modification strategy for cavity engineering that leverages state-of-the-art technologies and methodologies to tailor cavities according to the specific requirements of industrial production conditions is proposed.

Excelzyme: A Swiss University-Industry Collaboration for Accelerated Biocatalyst Development.

Excelzyme, an enzyme engineering platform located at the Zurich University of Applied Sciences, is dedicated to accelerating the development of tailored biocatalysts for large-scale industrial applications. Leveraging automation and advanced computational techniques, including machine learning, efficient biocatalysts can be generated in short timeframes. Toward this goal, Excelzyme systematically selects suitable protein scaffolds as the foundation for constructing complex enzyme libraries, thereby enhancing sequence and structural biocatalyst diversity. Here, we describe applied workflows and technologies as well as an industrial case study that exemplifies the successful application of the workflow.

Fc engineered anti-virus therapeutic human IgG1 expressed in plants with altered binding to the neonatal Fc receptor.

Production of therapeutic monoclonal antibody (mAb) in transgenic plants has several advantages such as large-scale production and the absence of pathogenic animal contaminants. However, mAb with high mannose (HM) type glycans has shown a faster clearance compared to antibodies produced in animal cells. The neonatal Fc receptor (FcRn) regulates the persistence of immunoglobulin G (IgG) by the FcRn-mediated recycling pathway, which salvages IgG from lysosomal degradation within cells. In this study, Fc-engineering of antirabies virus therapeutic mAb SO57 with the endoplasmic reticulum (ER)-retention peptide signal (Lys-Asp-Glu-Leu; KDEL) (mAbpK SO57) in plant cell was conducted to enhance its binding activity to human neonatal Fc receptor (hFcRn), consequently improve its serum half-life. Enzyme-linked immunosorbent assay (ELISA) and Surface plasmon resonance assay showed altered binding affinity of the Fc region of three different mAbpK SO57 variants [M252Y/S254T/T256E (MST), M428L/N434S (MN), H433K/N434F (HN)] to hFcRn compared to wild type (WT) of mAbpK SO57. Molecular modeling data visualized the structural alterations in these mAbpK SO57. All of the mAbpK SO57 variants had HM type glycan structures similar to the WT mAbpK SO57. In addition, the neutralizing activity of the three variants against the rabies virus CVS-11 was effective as the WT mAbpK SO57. These results indicate that the binding affinity of mAbpK SO57 variants to hFcRn can be modified without alteration of N-glycan structure and neutralization activity. Taken together, this study suggests that Fc-engineering of antirabies virus mAb can be applied to enhance the efficacy of therapeutic mAbs in plant expression systems.

Machine learning to predict continuous protein properties from binary cell sorting data and map unseen sequence space.

Proteins are a diverse class of biomolecules responsible for wide-ranging cellular functions, from catalyzing reactions to recognizing pathogens. The ability to evolve proteins rapidly and inexpensively toward improved properties is a common objective for protein engineers. Powerful high-throughput methods like fluorescent activated cell sorting and next-generation sequencing have dramatically improved directed evolution experiments. However, it is unclear how to best leverage these data to characterize protein fitness landscapes more completely and identify lead candidates. In this work, we develop a simple yet powerful framework to improve protein optimization by predicting continuous protein properties from simple directed evolution experiments using interpretable, linear machine learning models. Importantly, we find that these models, which use data from simple but imprecise experimental estimates of protein fitness, have predictive capabilities that approach more precise but expensive data. Evaluated across five diverse protein engineering tasks, continuous properties are consistently predicted from readily available deep sequencing data, demonstrating that protein fitness space can be reasonably well modeled by linear relationships among sequence mutations. To prospectively test the utility of this approach, we generated a library of stapled peptides and applied the framework to predict affinity and specificity from simple cell sorting data. We then coupled integer linear programming, a method to optimize protein fitness from linear weights, with mutation scores from machine learning to identify variants in unseen sequence space that have improved and co-optimal properties. This approach represents a versatile tool for improved analysis and identification of protein variants across many domains of protein engineering.

Practical Approaches to Genetic Code Expansion with Aminoacyl-tRNA Synthetase/tRNA Pairs.

Genetic code expansion (GCE) can enable the site-selective incorporation of non-canonical amino acids (ncAAs) into proteins. GCE has advanced tremendously in the last decade and can be used to create biorthogonal handles, monitor and control proteins inside cells, study post-translational modifications, and engineer new protein functions. Since establishing our laboratory, our research has focused on applications of GCE in protein and enzyme engineering using aminoacyl-tRNA synthetase/tRNA (aaRS/tRNA) pairs. This topic has been reviewed extensively, leaving little doubt that GCE is a powerful tool for engineering proteins and enzymes. Therefore, for this young faculty issue, we wanted to provide a more technical look into the methods we use and the challenges we think about in our laboratory. Since starting the laboratory, we have successfully engineered over a dozen novel aaRS/tRNA pairs tailored for various GCE applications. However, we acknowledge that the field can pose challenges even for experts. Thus, herein, we provide a review of methodologies in ncAA incorporation with some practical commentary and a focus on challenges, emerging solutions, and exciting developments.

Directed evolution of the fluorescent protein CGP with in situ biosynthesized noncanonical amino acids.

The incorporation of noncanonical amino acids (ncAAs) into proteins can enhance their function beyond the abilities of canonical amino acids and even generate new functions. However, the ncAAs used for such research are usually chemically synthesized, which is expensive and hinders their application on large industrial scales. We believe that the biosynthesis of ncAAs using metabolic engineering and their employment in situ in target protein engineering with genetic code expansion could overcome these limitations. As a proof of principle, we biosynthesized four ncAAs, O-L-methyltyrosine, 3,4-dihydroxy-L-phenylalanine, 5-hydroxytryptophan, and 5-chloro-L-tryptophan using metabolic engineering and directly evolved the fluorescent consensus green protein (CGP) by combination with nine other exogenous ncAAs in Escherichia coli. After screening a TAG scanning library expressing 13 ncAAs, several variants with enhanced fluorescence and stability were identified. The variants CGPV3pMeoF/K190pMeoF and CGPG20pMeoF/K190pMeoF expressed with biosynthetic O-L-methyltyrosine showed an approximately 1.4-fold improvement in fluorescence compared to the original level, and a 2.5-fold improvement in residual fluorescence after heat treatment. Our results demonstrated the feasibility of integrating metabolic engineering, genetic code expansion, and directed evolution in engineered cells to employ biosynthetic ncAAs in protein engineering. These results could further promote the application of ncAAs in protein engineering and enzyme evolution. IMPORTANCE: Noncanonical amino acids (ncAAs) have shown great potential in protein engineering and enzyme evolution through genetic code expansion. However, in most cases, ncAAs must be provided exogenously during protein expression, which hinders their application, especially when they are expensive or have poor cell membrane penetration. Engineering cells with artificial metabolic pathways to biosynthesize ncAAs and employing them in situ for protein engineering and enzyme evolution could facilitate their application and reduce costs. Here, we attempted to evolve the fluorescent consensus green protein (CGP) with biosynthesized ncAAs. Our results demonstrated the feasibility of using biosynthesized ncAAs in protein engineering, which could further stimulate the application of ncAAs in bioengineering and biomedicine.

Engineering of glycoside hydrolase family 7 cellobiohydrolases directed by natural diversity screening.

Protein engineering and screening of processive fungal cellobiohydrolases (CBHs) remain challenging due to limited expression hosts, synergy-dependency, and recalcitrant substrates. In particular, glycoside hydrolase family 7 (GH7) CBHs are critically important for the bioeconomy and typically difficult to engineer. Here, we target the discovery of highly active natural GH7 CBHs and engineering of variants with improved activity. Using experimentally assayed activities of genome mined CBHs, we applied sequence and structural alignments to top performers to identify key point mutations linked to improved activity. From ∼1500 known GH7 sequences, an evolutionarily diverse subset of 57 GH7 CBH genes was expressed in Trichoderma reesei and screened using a multiplexed activity screening assay. Ten catalytically enhanced natural variants were identified, produced, purified, and tested for efficacy using industrially relevant conditions and substrates. Three key amino acids in CBHs with performance comparable or superior to Penicillium funiculosum Cel7A were identified and combinatorially engineered into P. funiculosum cel7a, expressed in T. reesei, and assayed on lignocellulosic biomass. The top performer generated using this combined approach of natural diversity genome mining, experimental assays, and computational modeling produced a 41% increase in conversion extent over native P. funiculosum Cel7A, a 55% increase over the current industrial standard T. reesei Cel7A, and 10% improvement over Aspergillus oryzae Cel7C, the best natural GH7 CBH previously identified in our laboratory.

Structure- and machine learning-guided engineering demonstrate that a non-canonical disulfide in an anti-PD-1 rabbit antibody does not impede antibody developability.

Rabbits produce robust antibody responses and have unique features in their antibody repertoire that make them an attractive alternative to rodents for in vivo discovery. However, the frequent occurrence of a non-canonical disulfide bond between complementarity-determining region (CDR) H1 (C35a) and CDRH2 (C50) is often seen as a liability for therapeutic antibody development, despite limited reports of its effect on antibody binding, function, and stability. Here, we describe the discovery and humanization of a human-mouse cross-reactive anti-programmed cell death 1 (PD-1) monoclonal rabbit antibody, termed h1340.CC, which possesses this non-canonical disulfide bond. Initial removal of the non-canonical disulfide resulted in a loss of PD-1 affinity and cross-reactivity, which led us to explore protein engineering approaches to recover these. First, guided by the sequence of a related clone and the crystal structure of h1340.CC in complex with PD-1, we generated variant h1340.SA.LV with a potency and cross-reactivity similar to h1340.CC, but only partially recovered affinity. Side-by-side developability assessment of both h1340.CC and h1340.SA.LV indicate that they possess similar, favorable properties. Next, and prompted by recent developments in machine learning (ML)-guided protein engineering, we used an unbiased ML- and structure-guided approach to rapidly and efficiently generate a different variant with recovered affinity. Our case study thus indicates that, while the non-canonical inter-CDR disulfide bond found in rabbit antibodies does not necessarily constitute an obstacle to therapeutic antibody development, combining structure- and ML-guided approaches can provide a fast and efficient way to improve antibody properties and remove potential liabilities.

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.

Continuous Fluorescence Assay for In Vitro Translation Compatible with Noncanonical Amino Acids.

The tolerance of the translation apparatus toward noncanonical amino acids (ncAAs) has enabled the creation of diverse natural-product-like peptide libraries using mRNA display for use in drug discovery. Typical experiments testing for ribosomal ncAA incorporation involve radioactive end point assays to measure yield alongside mass spectrometry experiments to validate incorporation. These end point assays require significant postexperimental manipulation for analysis and prevent higher throughput analysis and optimization experiments. Continuous assays for in vitro translation involve the synthesis of fluorescent proteins which require the full complement of canonical AAs for function and are therefore of limited utility for testing of ncAAs. Here, we describe a new, continuous fluorescence assay for in vitro translation based on detection of a short peptide tag using an affinity clamp protein, which exhibits changes in its fluorescent properties upon binding. Using this assay in a 384-well format, we were able to validate the incorporation of a variety of ncAAs and also quickly test for the codon reading specificities of a variety of Escherichia coli tRNAs. This assay enables rapid assessment of ncAAs and optimization of translation components and is therefore expected to advance the engineering of the translation apparatus for drug discovery and synthetic biology.

Irreversible light-activated SpyLigation mediates split-protein assembly in 4D.

The conditional assembly of split-protein pairs to modulate biological activity is commonly achieved by fusing split-protein fragments to dimerizing components that bring inactive pairs into close proximity in response to an exogenous trigger. However, current methods lack full spatial and temporal control over reconstitution, require sustained activation and lack specificity. Here light-activated SpyLigation (LASL), based on the photoregulation of the covalent SpyTag (ST)/SpyCatcher (SC) peptide-protein reaction, assembles nonfunctional split fragment pairs rapidly and irreversibly in solution, in engineered biomaterials and intracellularly. LASL introduces an ortho-nitrobenzyl(oNB)-caged lysine into SC's reactive site to generate a photoactivatable SC (pSC). Split-protein pairs of interest fused to pSC and ST are conditionally assembled via near-ultraviolet or pulsed near-infrared irradiation, as the uncaged SC can react with ST to ligate appended fragments. We describe procedures for the efficient synthesis of the photocaged amino acid that is incorporated within pSC (<5 days) as well as the design and cloning of LASL plasmids (1-4 days) for recombinant protein expression in either Escherichia coli (5-6 days) or mammalian cells (4-6 days), which require some prior expertise in protein engineering. We provide a chemoenzymatic scheme for appending bioorthogonal reactive handles onto E. coli-purified pSC protein (<4 days) that permits LASL component incorporation and patterned protein activation within many common biomaterial platforms. Given that LASL is irreversible, the photolithographic patterning procedures are fast and do not require sustained light exposure. Overall, LASL can be used to interrogate and modulate cell signaling in various settings.

Enhancing the Catalytic Efficiency of D-lactonohydrolase through the Synergy of Tunnel Engineering, Evolutionary Analysis, and Force-Field Calculations.

Computational design advances enzyme evolution and their use in biocatalysis in a faster and more efficient manner. In this study, a synergistic approach integrating tunnel engineering, evolutionary analysis, and force-field calculations has been employed to enhance the catalytic activity of D-lactonohydrolase (D-Lac), which is a pivotal enzyme involved in the resolution of racemic pantolactone during the production of vitamin B5. The best mutant, N96S/A271E/F274Y/F308G (M3), was obtained and its catalytic efficiency (kcat/KM) was nearly 23-fold higher than that of the wild-type. The M3 whole-cell converted 20 % of DL-pantolactone into D-pantoic acid (D-PA, >99 % e.e.) with a conversion rate of 47 % and space-time yield of 107.1 g L-1 h-1, demonstrating its great potential for industrial-scale D-pantothenic acid production. Molecular dynamics (MD) simulations revealed that the reduction in the steric hindrance within the substrate tunnel and conformational reconstruction of the distal loop resulted in a more favourable"catalytic" conformation, making it easier for the substrate and enzyme to enter their pre-reaction state. This study illustrates the potential of the distal residue on the pivotal loop at the entrance of the D-Lac substrate tunnel as a novel modification hotspot capable of reshaping energy patterns and consequently influencing the enzymatic activity.

Data-Driven Protein Engineering for Improving Catalytic Activity and Selectivity.

Protein engineering is essential for altering the substrate scope, catalytic activity and selectivity of enzymes for applications in biocatalysis. However, traditional approaches, such as directed evolution and rational design, encounter the challenge in dealing with the experimental screening process of a large protein mutation space. Machine learning methods allow the approximation of protein fitness landscapes and the identification of catalytic patterns using limited experimental data, thus providing a new avenue to guide protein engineering campaigns. In this concept article, we review machine learning models that have been developed to assess enzyme-substrate-catalysis performance relationships aiming to improve enzymes through data-driven protein engineering. Furthermore, we prospect the future development of this field to provide additional strategies and tools for achieving desired activities and selectivities.

Becoming fluent in proteins.

Large language models have emerged as a new compass for navigating the complex landscapes of protein engineering. This issue of Cell Systems features ProGen2 and IgLM-two protein language models (PLMs) that use subtly different approaches to design proteins.

Displaying a Protein Cage on a Protein Crystal by In-Cell Crystal Engineering.

The development of solid biomaterials has rapidly progressed in recent years in applications in bionanotechnology. The immobilization of proteins, such as enzymes, within protein crystals is being used to develop solid catalysts and functionalized materials. However, an efficient method for encapsulating protein assemblies has not yet been established. This work presents a novel approach to displaying protein cages onto a crystalline protein scaffold using in-cell protein crystal engineering. The polyhedra crystal (PhC) scaffold, which displays a ferritin cage, was produced by coexpression of polyhedrin monomer (PhM) and H1-ferritin (H1-Fr) monomer in Escherichia coli. The H1-tag is derived from the H1-helix of PhM. Our technique represents a unique strategy for immobilizing protein assemblies onto in-cell protein crystals and is expected to contribute to various applications in bionanotechnology.

Protein engineering using mutability landscapes: Controlling site-selectivity of P450-catalyzed steroid hydroxylation.

Directed evolution and rational design have been used widely in engineering enzymes for their application in synthetic organic chemistry and biotechnology. With stereoselectivity playing a crucial role in catalysis for the synthesis of valuable chemical and pharmaceutical compounds, rational design has not achieved such wide success in this specific area compared to directed evolution. Nevertheless, one bottleneck of directed evolution is the laborious screening efforts and the observed trade-offs in catalytic profiles. This has motivated researchers to develop more efficient protein engineering methods. As a prime approach, mutability landscaping avoids such trade-offs by providing more information of sequence-function relationships. Here, we describe an application of this efficient protein engineering method to improve the regio-/stereoselectivity and activity of P450BM3 for steroid hydroxylation, while keeping the mutagenesis libraries small so that they will require only minimal screening.

The colors of peroxygenase activity: Colorimetric high-throughput screening assays for directed evolution.

Fungal unspecific peroxygenases (UPOs) are arising as versatile biocatalysts for C-H oxyfunctionalization reactions. In recent years, several directed evolution studies have been conducted to design improved UPO variants. An essential part of this protein engineering strategy is the design of reliable colorimetric high-throughput screening (HTS) assays for mutant library exploration. Here, we present a palette of 12 colorimetric HTS assays along with their step-by-step protocols, which have been validated for directed UPO evolution campaigns. This array of colorimetric assays will pave the way for the discovery and design of new UPO variants.

Molecular analysis of the type III interferon complex and its applications in protein engineering.

Type III interferons (IFNλs) are cytokines with critical roles in the immune system and are attractive therapeutic candidates due to their tissue-specific activity. Despite entering several clinical trials, results have demonstrated limited efficacy and potency, partially attributed to low-affinity protein-protein interactions (PPIs) responsible for receptor complex formation. Subsequently, structural studies of the native IFNλ signaling complexes remain inaccessible. While protein engineering can overcome affinity limitations, tools to investigate low-affinity systems like these remain limited. To provide insights into previous efforts to strengthen the PPIs within this complex, we perform a molecular analysis of the extracellular ternary complexes of IFNλ3 using both computational and experimental approaches. We first use molecular simulations and modeling to quantify differences in PPIs and residue strain fluctuations, generate detailed free energy landscapes, and reveal structural differences between an engineered, high-affinity complex, and a model of the wild-type, low-affinity complex. This analysis illuminates distinct behaviors of these ligands, yielding mechanistic insights into IFNλ complex formation. We then apply these computational techniques in protein engineering and design by utilizing simulation data to identify hotspots of interaction to rationally engineer the native cytokine-receptor complex for increased stability. These simulations are then validated by experimental techniques, showing that a single mutation at a computationally predicted site of interaction between the two receptors increases PPIs and improves complex formation for all IFNλs. This study highlights the power of molecular dynamics simulations for protein engineering and design as applied to the IFNλ family but also presents a potential tool for analysis and engineering of other systems with low-affinity PPIs.