Radical-relay C(sp3)-H azidation catalyzed by an engineered nonheme iron enzyme.

Nonheme iron enzymes are versatile biocatalysts for a broad range of unique and powerful transformations, such as hydroxylation, chlorination, and epimerization as well as cyclization/ring-opening of organic molecules. Beyond their native biological functions, these enzymes are robust for engineering due to their structural diversity and high evolvability. Based on enzyme promiscuity and directed evolution as well as inspired by synthetic organic chemistry, nonheme iron enzymes can be repurposed to catalyze reactions previously only accessible with synthetic catalysts. To this end, our group has engineered a series of nonheme iron enzymes to employ non-natural radical-relay mechanisms for new-to-nature radical transformations. In particular, we have demonstrated that a nonheme iron enzyme, (4-hydroxyphenyl)pyruvate dioxygenase from streptomyces avermitilis (SavHppD), can be repurposed to enable abiological radical-relay process to access C(sp3)-H azidation products. This represents the first known instance of enzymatic radical relay azidation reactions. In this chapter, we describe the detailed experimental protocol to convert promiscuous nonheme iron enzymes into efficient and selective biocatalyst for radical relay azidation reactions. One round of directed evolution is described in detail, which includes the generation and handling of site-saturation mutagenesis, protein expression and whole-cell reactions screening in a 96-well plate. These protocol details might be useful to engineer various nonheme iron enzymes for other applications.

Resistance-based directed evolution of nanobodies for higher affinity in prokaryotes.

A prokaryotic resistance-based directed evolution system leveraging protein-fragment complementation assay (PCA) was devised, and its proficiency in detecting protein-protein interactions and discriminating varying degrees of binding affinity was demonstrated by two well-characterized protein pairs. Furthermore, we constructed a random mutant library based on the GBPR36K/E45K mutant, characterized by almost no affinity towards EGFP. This library was subjected to PCA-based prokaryotic directed evolution, resulting in the isolation of back-mutated variants. In summary, we have established an expedited, cost-effective, and structural information-independent PCA-based prokaryotic directed evolution platform for nanobody affinity maturation, featuring tunable screening stringency via modulation of antibiotic concentrations.

Unlocking Lignin's Potential: Engineered Bacterial Laccases to Produce Biologically Active Molecules.

Laccases are biocatalysts with immense potential in lignocellulose biorefineries to valorize emerging lignin monomers for sustainable chemicals. Despite reduced costs over the past two decades, enzymes remain a major expense in biorefining. Protein engineering can enhance enzyme properties and lower costs further. In this study, we used enzyme engineering tools to improve > 400-fold the catalytic efficiency (kcat/Km) of a hyperthermostable bacterial laccase for 2,6-dimethoxyphenol, a lignin-related phenolic compound. Furthermore, this evolved variant showed improved activity at neutral to alkaline pH for hydroxycinnamyl alcohols, hydrocinnamic acids, phenylpropanoid and vanillyl derivatives. We optimized conditions for the synthesis of syringaresinol, dehydrodiconiferyl alcohol, thomasidioic acid, biseugenol, dehydrodiisoeugenol, and diapocynin, detailing the pH, catalyst concentration, reaction time, temperature, and oxygenation of the reaction mixtures. Our biocatalytic system offers several advantages, including being free of organic solvents, achieving faster reaction times, using lower amounts of enzymes and delivering excellent yields (up to 100%) than reported methods. Additionally, we provide insights that advance the state-of-the-art in lignin combinatory chemistry. This progress marks a significant step forward in valorizing the lignin chemicals platform, enabling high yields of dimeric compounds with structural scaffolds that can be exploited in various biotechnological areas, such as medicinal chemistry and polymer synthesis.

Characterizing and Tailoring the Substrate Profile of a γ-Glutamyltransferase Variant.

Xenobiology is an emerging field that focuses on the extension and redesign of biological systems through the use of laboratory-derived xenomolecules, which are molecules that are new to the metabolism of the cell. Despite the enormous potential of using xenomolecules in living organisms, most noncanonical building blocks still need to be supplied externally, and often poor uptake into cells limits wider applicability. To improve the cytosolic availability of noncanonical molecules, a synthetic transport system based on portage transport was developed, in which molecules of interest "cargo" are linked to a synthetic transport vector that enables piggyback transport through the alkylsulfonate transporter (SsuABC) of Escherichia coli. Upon cytosolic delivery, the vector-cargo conjugate is enzymatically cleaved by GGTxe, leading to the release of the cargo molecule. To deepen our understanding of the synthetic transport system, we focused on the characterization and further development of the enzymatic cargo release step. Hence, the substrate scope of GGTxe was characterized using a library of structurally diverse vector-cargo conjugates and MS/MS-based quantification of hydrolysis products in a kinetic manner. The resulting substrate tolerance characterization revealed that vector-amino acid conjugates were significantly unfavored. To overcome this shortcoming, a selection system based on metabolic auxotrophy complementation and directed evolution of GGTxe was established. In a directed evolution campaign, we improved the enzymatic activity of GGTxe for vector-amino acid conjugates and revealed the importance of residue D386 in the cargo unloading step.

CAPE: a deep learning framework with Chaos-Attention net for Promoter Evolution.

Predicting the strength of promoters and guiding their directed evolution is a crucial task in synthetic biology. This approach significantly reduces the experimental costs in conventional promoter engineering. Previous studies employing machine learning or deep learning methods have shown some success in this task, but their outcomes were not satisfactory enough, primarily due to the neglect of evolutionary information. In this paper, we introduce the Chaos-Attention net for Promoter Evolution (CAPE) to address the limitations of existing methods. We comprehensively extract evolutionary information within promoters using merged chaos game representation and process the overall information with modified DenseNet and Transformer structures. Our model achieves state-of-the-art results on two kinds of distinct tasks related to prokaryotic promoter strength prediction. The incorporation of evolutionary information enhances the model's accuracy, with transfer learning further extending its adaptability. Furthermore, experimental results confirm CAPE's efficacy in simulating in silico directed evolution of promoters, marking a significant advancement in predictive modeling for prokaryotic promoter strength. Our paper also presents a user-friendly website for the practical implementation of in silico directed evolution on promoters. The source code implemented in this study and the instructions on accessing the website can be found in our GitHub repository https://github.com/BobYHY/CAPE.

Screening of protein-based inhibitors for the intracellular domain of epidermal growth factor receptor by directed evolution using the yeast Gγ recruitment system.

Protein-based therapeutics, including antibodies and antibody-like-proteins, have increasingly attracted attention due to their high specificity compared to small-molecular drugs. The Gγ recruitment system, one of the in vivo yeast two-hybrid systems for detecting protein-protein interactions, has been previously developed using yeast signal transduction machinery. In this study, we modified the Gγ recruitment system to screen the protein mutants that efficiently bind to the intracellular domain of the epidermal growth factor receptor L858R mutant (cytoEGFRL858R). Using the modified platform, we performed in vivo directed evolution for growth factor receptor-bound protein 2 (Grb2) and its truncated variant containing only the Src-homology 2 (SH2) domain, successfully identifying several mutants that more strongly bound to cytoEGFRL858R than their parental proteins. Some of them contained novel beneficial mutations (F108Y and Q144H) and specifically bound to the recombinant cytosolic phosphorylated EGFR in vitro, highlighting the utility of the evolutionary platform.

Adeno-Associated Virus Engineering and Load Strategy for Tropism Modification, Immune Evasion and Enhanced Transgene Expression.

Gene therapy aims to add, replace or turn off genes to help treat disease. To date, the US Food and Drug Administration (FDA) has approved 14 gene therapy products. With the increasing interest in gene therapy, feasible gene delivery vectors are necessary for inserting new genes into cells. There are different kinds of gene delivery vectors including viral vectors like lentivirus, adenovirus, retrovirus, adeno-associated virus et al, and non-viral vectors like naked DNA, lipid vectors, polymer nanoparticles, exosomes et al, with viruses being the most commonly used. Among them, the most concerned vector is adeno-associated virus (AAV) because of its safety, natural ability to efficiently deliver gene into cells and sustained transgene expression in multiple tissues. In addition, the AAV genome can be engineered to generate recombinant AAV (rAAV) containing transgene sequences of interest and has been proven to be a safe gene vector. Recently, rAAV vectors have been approved for the treatment of various rare diseases. Despite these approvals, some major limitations of rAAV remain, namely nonspecific tissue targeting and host immune response. Additional problems include neutralizing antibodies that block transgene delivery, a finite transgene packaging capacity, high viral titer used for per dose and high cost. To deal with these challenges, several techniques have been developed. Based on differences in engineering methods, this review proposes three strategies: gene engineering-based capsid modification (capsid modification), capsid surface tethering through chemical conjugation (surface tethering), and other formulations loaded with AAV (virus load). In addition, the major advantages and limitations encountered in rAAV engineering strategies are summarized.

Protein multi-level structure feature-integrated deep learning method for mutational effect prediction.

Through iterative rounds of mutation and selection, proteins can be engineered to enhance their desired biological functions. Nevertheless, identifying optimal mutation sites for directed evolution remains challenging due to the vastness of the protein sequence landscape and the epistatic mutational effects across residues. To address this challenge, we introduce MLSmut, a deep learning-based approach that leverages multi-level structural features of proteins. MLSmut extracts salient information from protein co-evolution, sequence semantics, and geometric features to predict the mutational effect. Extensive benchmark evaluations on 10 single-site and two multi-site deep mutation scanning datasets demonstrate that MLSmut surpasses existing methods in predicting mutational outcomes. To overcome the limited training data availability, we employ a two-stage training strategy: initial coarse-tuning on a large corpus of unlabeled protein data followed by fine-tuning on a curated dataset of 40-100 experimental measurements. This approach enables our model to achieve satisfactory performance on downstream protein prediction tasks. Importantly, our model holds the potential to predict the mutational effects of any protein sequence. Collectively, these findings suggest that our approach can substantially reduce the reliance on laborious wet lab experiments and deepen our understanding of the intricate relationships between mutations and protein function.

Facilitating secretory expression of apple seed ß-glucosidase in Komagataella phaffii for the efficient preparation of salidroside.

Plant-derived ß-glucosidases hold promise for glycoside biosynthesis via reverse hydrolysis because of their excellent glucose tolerance and robust stability. However, their poor heterologous expression hinders the development of large-scale production and applications. In this study, we overexpressed apple seed ß-glucosidase (ASG II) in Komagataella phaffii and enhanced its production from 289 to 4322 U L-1 through expression cassette engineering and protein engineering. Upon scaling up to a 5-L high cell-density fermentation, the resultant mutant ASG IIV80A achieved a maximum protein concentration and activity in the secreted supernatant of 2.3 g L-1 and 41.4 kU L-1, respectively. The preparative biosynthesis of salidroside by ASG IIV80A exhibited a high space-time yield of 33.1 g L-1 d-1, which is so far the highest level by plant-derived ß-glucosidase. Our work addresses the long-standing challenge of the heterologous expression of plant-derived ß-glucosidase in microorganisms and presents new avenues for the efficient production of salidroside and other natural glycosides.

Evolving dual-trait EPSP synthase variants using a synthetic yeast selection system.

The enzyme 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) functions in the shikimate pathway which is responsible for the production of aromatic amino acids and precursors of other essential secondary metabolites in all plant species. EPSPS is also the molecular target of the herbicide glyphosate. While some plant EPSPS variants have been characterized with reduced glyphosate sensitivity and have been used in biotechnology, the glyphosate insensitivity typically comes with a cost to catalytic efficiency. Thus, there exists a need to generate additional EPSPS variants that maintain both high catalytic efficiency and high glyphosate tolerance. Here, we create a synthetic yeast system to rapidly study and evolve heterologous EPSP synthases for these dual traits. Using known EPSPS variants, we first validate that our synthetic yeast system is capable of recapitulating growth characteristics observed in plants grown in varying levels of glyphosate. Next, we demonstrate that variants from mutagenesis libraries with distinct phenotypic traits can be isolated depending on the selection criteria applied. By applying strong dual-trait selection pressure, we identify a notable EPSPS mutant after just a single round of evolution that displays robust glyphosate tolerance (Ki of nearly 1 mM) and improved enzymatic efficiency over the starting point (~2.5 fold). Finally, we show the crystal structure of corn EPSPS and the top resulting mutants and demonstrate that certain mutants have the potential to outperform previously reported glyphosate-resistant EPSPS mutants, such as T102I and P106S (denoted as TIPS), in whole-plant testing. Altogether, this platform helps explore the trade-off between glyphosate resistance and enzymatic efficiency.

Comprehensive Analysis of Allulose Production: A Review and Update.

Advancements in D-allulose production have seen significant strides in recent years, focusing on enzymatic conversion methods. Key developments include traditional immobilization techniques, the discovery of novel enzymes, directed evolution studies, and biosynthesis through metabolic pathway modification. Enzymatic conversion, particularly utilizing D-allulose 3-epimerase, remains fundamental for industrial-scale production. Innovative immobilization strategies, such as functionalized nano-beads and magnetic MOF nanoparticles, have significantly enhanced enzyme stability and reusability. Directed evolution has led to improved enzyme thermostability and catalytic efficiency, while synthetic biology methods, including phosphorylation-driven and thermodynamics-driven pathways, have optimized production processes. High-throughput screening methods have been crucial in identifying and refining enzyme variants for industrial applications. Collectively, these advancements not only enhance production efficiency and cost-effectiveness but also adhere to sustainable and economically viable manufacturing practices. The past five years have witnessed critical developments with significant potential impact on the commercial viability and global demand for allulose.

MutaT7GDE: A Single Chimera for the Targeted, Balanced, Efficient, and Processive Installation of All Possible Transition Mutations In Vivo.

Deaminase-T7 RNA polymerase fusion (MutaT7) proteins are a growing class of synthetic biology tools used to diversify target genes during in vivo laboratory evolution. To date, MutaT7 chimeras comprise either a deoxyadenosine or deoxycytidine deaminase fused to a T7 RNA polymerase. Their expression drives targeted deoxyadenosine-to-deoxyguanosine or deoxycytidine-to-deoxythymidine mutagenesis, respectively. Here, we repurpose recently engineered substrate-promiscuous general deaminases (GDEs) to establish a substantially simplified system based on a single chimeric enzyme capable of targeting both deoxyadenosine and deoxycytidine. We assess on- and off-target mutagenesis, strand and context preference, and parity of deamination for four different MutaT7GDE constructs. We identify a single chimera that installs all possible transition mutations more efficiently than preexisting, more cumbersome MutaT7 tools. The optimized MutaT7GDE chimera reported herein is a next-generation hypermutator capable of mediating efficient and uniform target-gene diversification during in vivo directed evolution.

In vitro evolution driven by epistasis reveals alternative cholesterol-specific binding motifs of perfringolysin O.

The crucial molecular factors that shape the interfaces of lipid-binding proteins with their target ligands and surfaces remain unknown due to the complex makeup of biological membranes. Cholesterol, the major modulator of bilayer structure in mammalian cell membranes, is recognized by various proteins, including the well-studied cholesterol-dependent cytolysins. Here, we use in vitro evolution to investigate the molecular adaptations that preserve the cholesterol specificity of perfringolysin O, the prototypical cholesterol-dependent cytolysin from Clostridium perfringens. We identify variants with altered membrane-binding interfaces whose cholesterol-specific activity exceeds that of the wild-type perfringolysin O. These novel variants represent alternative evolutionary outcomes and have mutations at conserved positions that can only accumulate when epistatic constraints are alleviated. Our results improve the current understanding of the biochemical malleability of the surface of a lipid-binding protein.

Efficient and green production of flavone-5-O-glycosides by glycosyltransferases in Escherichia coli.

O-Glycosylflavonoids exhibit diverse biological activities but their low content in plants is difficult to extract and isolate, and chemical synthesis steps are cumbersome, which are harmful to the environment. Therefore, the biosynthesis of O-glycosylflavonoids represents a green and sustainable alternative strategy, with glycosyltransferases playing a crucial role in this process. However, there are few studies on flavone 5-O-glycosyltransferases, which limits the synthesis of rare flavone 5-O glycosides by microorganisms. In this study, we characterized a highly regioselectivity flavone 5-O glycosyltransferase from Panicum hallii. Site-directed mutagenesis at residue P141 switches glucosylation to xylosylation. Using a combinatorial strategy of metabolic engineering, we generated a series of Escherichia coli recombinant strains to biocatalyze glycosylation of the typical flavone apigenin. Ultimately, further optimization of transformation conditions, apigenin-5-O-glucoside and apigenin-5-O-xyloside were biosynthesized for the first time so far, and the yields were 1490 mg/L and 1210 mg/L, respectively. This study provides a biotechnological component for the biosynthesis of flavone-5-O-glycosides, and established a green and sustainable approach for the industrial production of high-value O-glycosylflavones by engineering, which lays a foundation for their further development and application in food and pharmaceutical fields.

Thermostability and activity improvement in l-threonine aldolase through targeted mutations in V-shaped subunit.

l-threonine aldolase (LTA) catalyzes the synthesis of ß-hydroxy-α-amino acids, which are important chiral intermediates widely used in the fields of pharmaceuticals and pesticides. However, the limited thermostability of LTA hinders its industrial application. Furthermore, the trade-off between thermostability and activity presents a challenge in the thermostability engineering of this enzyme. This study proposes a strategy to regulate the rigidity of LTA's V-shaped subunit by modifying its opening and hinge regions, distant from the active center, aiming to mitigate the trade-off. With LTA from Bacillus nealsonii as targeted enzyme, a total of 25 residues in these two regions were investigated by directed evolution. Finally, mutant G85A/M207L/A12C was obtained, showing significantly enhanced thermostability with a 20 °C increase in T5060 to 66 °C, and specific activity elevated by 34 % at the optimum temperature. Molecular dynamics simulations showed that the newly formed hydrophobicity and hydrogen bonds improved the thermostability and boosted proton transfer efficiency. This work enhances the thermostability of LTA while preventing the loss of activity. It opens new avenues for the thermostability engineering of other industrially relevant enzymes with active center located at the interface of subunits or domains.

Deep-learning-based design of synthetic orthologs of SH3 signaling domains.

Evolution-based deep generative models represent an exciting direction in understanding and designing proteins. An open question is whether such models can learn specialized functional constraints that control fitness in specific biological contexts. Here, we examine the ability of generative models to produce synthetic versions of Src-homology 3 (SH3) domains that mediate signaling in the Sho1 osmotic stress response pathway of yeast. We show that a variational autoencoder (VAE) model produces artificial sequences that experimentally recapitulate the function of natural SH3 domains. More generally, the model organizes all fungal SH3 domains such that locality in the model latent space (but not simply locality in sequence space) enriches the design of synthetic orthologs and exposes non-obvious amino acid constraints distributed near and far from the SH3 ligand-binding site. The ability of generative models to design ortholog-like functions in vivo opens new avenues for engineering protein function in specific cellular contexts and environments.

Targeted enhancement of bacteriophage activity against antibiotic-resistant Staphylococcus aureus biofilms through an evolutionary assay.

Staphylococcus aureus´ biofilm-forming ability and rapid resistance development pose a significant challenge to successful treatment, particularly in postoperative complications, emphasizing the need for enhanced therapeutic strategies. Bacteriophage (phage) therapy has reemerged as a promising and safe option to combat multidrug-resistant bacteria. However, questions regarding the efficacy of phages against biofilms and the development of phage resistance require further evaluation. Expanding on the adaptable and evolutionary characteristics of phages, we introduce an evolutionary approach to enhance the activity of S. aureus phages against biofilms. Unlike other in vitro directed evolution methods performed in planktonic cultures, we employed pre-stablished biofilms to do a serial-passage assay to evolve phages monitored by real-time isothermal microcalorimetry (IMC). The evolved phages demonstrated an expanded host range, with the CUB_MRSA-COL_R9 phage infecting 83% of strains in the collection (n = 72), surpassing the ISP phage, which represented the widest host range (44%) among the ancestral phages. In terms of antimicrobial efficacy, IMC data revealed superior suppression of bacterial growth by the evolved phages compared to the ancestral CUB-M and/or ISP phages against the respective bacterial strain. The phage cocktail exhibited higher efficacy, achieving over 90% suppression relative to the growth control even after 72 h of monitoring. Biofilm cell-counts, determined by RT-qPCR, confirmed the enhanced antibiofilm performance of evolved phages with no biofilm regrowth up to 48 h in treated MRSA15 and MRSA-COL strains. Overall, our results underscore the potential of biofilm-adapted phage cocktails to improve clinical outcomes in biofilm-associated infections, minimizing the emergence of resistance and lowering the risk of infection relapse. However, further investigation is necessary to evaluate the translatability of our results from in vitro to in vivo models, especially in the context of combination therapy with the current standard of care treatment.

Herbicide-resistant HPPD variants identified via directed evolution.

Herbicides play a crucial role in boosting crop yields, yet the emergence of herbicide-resistant weeds and the susceptibility of crops to herbicides have posed significant challenges to their efficacy. ß-triketone herbicides specifically target the enzyme 4-Hydroxyphenylpyruvate dioxygenase (HPPD) essential for plant growth. Remarkably, few resistant weeds have been identified against these herbicides. In this study, we aimed to identify mutations within the cotton HPPD gene that confer resistance to mesotrione, a widely used triketone herbicide. Through the establishment of a high-throughput mutant screening system in E. coli, we identified four single nucleotide changes leading to amino acid substitutions in HPPD, resulting in mesotrione resistance while preserving native enzymatic activity. Various combinations of these mutations displayed synergistic effects on herbicide resistance. Additionally, the HPPD variants were able to complement the Arabidopsis athppd mutant, indicating their retention of sufficient native activity crucial for plant growth and development. Expression of these cotton HPPD variants in Arabidopsis resulted in heightened herbicide resistance. These findings offer critical insights into the target amino acids of HPPD for gene editing, paving the way for the development of herbicide-resistant cotton in the future.

Advances in evolved T7 RNA polymerases for expanding the frontiers of enzymatic nucleic acid synthesis.

In vitro RNA synthesis technologies are crucial in developing therapeutic RNA drugs, such as mRNA vaccines and RNA interference (RNAi) therapies. Enzymatic RNA synthesis, recognized for its sustainability and efficiency, enables the production of extensive RNA sequences under mild conditions. Among the enzymes utilized, T7 RNA polymerase is distinguished by its exceptional catalytic efficiency, enabling the precise and rapid transcription of RNA from DNA templates by recognizing the specific T7 promoter sequence. With the advancement in clinical applications of RNA-based drugs, there is an increasing demand for the synthesis of chemically modified RNAs that are stable and resistant to nuclease degradation. To this end, researchers have applied directed evolution to broaden the enzyme's substrate scope, enhancing its compatibility with non-canonical substrates and reducing the formation of by-products. This review summarizes the progress in engineering T7 RNA polymerase for these purposes and explores prospective developments in the field.

A combinatorially complete epistatic fitness landscape in an enzyme active site.

Protein engineering often targets binding pockets or active sites which are enriched in epistasis-nonadditive interactions between amino acid substitutions-and where the combined effects of multiple single substitutions are difficult to predict. Few existing sequence-fitness datasets capture epistasis at large scale, especially for enzyme catalysis, limiting the development and assessment of model-guided enzyme engineering approaches. We present here a combinatorially complete, 160,000-variant fitness landscape across four residues in the active site of an enzyme. Assaying the native reaction of a thermostable ß-subunit of tryptophan synthase (TrpB) in a nonnative environment yielded a landscape characterized by significant epistasis and many local optima. These effects prevent simulated directed evolution approaches from efficiently reaching the global optimum. There is nonetheless wide variability in the effectiveness of different directed evolution approaches, which together provide experimental benchmarks for computational and machine learning workflows. The most-fit TrpB variants contain a substitution that is nearly absent in natural TrpB sequences-a result that conservation-based predictions would not capture. Thus, although fitness prediction using evolutionary data can enrich in more-active variants, these approaches struggle to identify and differentiate among the most-active variants, even for this near-native function. Overall, this work presents a large-scale testing ground for model-guided enzyme engineering and suggests that efficient navigation of epistatic fitness landscapes can be improved by advances in both machine learning and physical modeling.