Addressing the Numbers Problem in Directed Evolution

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

l-threonine aldolase (LTA) catalyzes the synthesis of β-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.

Rational enzyme design by reducing the number of hotspots and library size

Biocatalysts that are eco-friendly, sustainable, and highly specific have great potential for applications in the production of fine chemicals, food, detergents, biofuels, pharmaceuticals, and more. However, due to factors such as low activity, narrow substrate scope, poor thermostability, or incorrect selectivity, most natural enzymes cannot be directly used for large-scale production of the desired products. To overcome these obstacles, protein engineering methods have been developed over decades and have become powerful and versatile tools for adapting enzymes with improved catalytic properties or new functions. The vastness of the protein sequence space makes screening a bottleneck in obtaining advantageous mutated enzymes in traditional directed evolution. In the realm of mathematics, there are two major constraints in the protein sequence space: (1) the number of residue substitutions (M); and (2) the number of codons encoding amino acids as building blocks (N). This feature review highlights protein engineering strategies to reduce screening efforts from two dimensions by reducing the numbers M and N, and also discusses representative seminal studies of rationally engineered natural enzymes to deliver new catalytic functions.

Efficient and easible biocatalysts: Strategies for enzyme improvement. A review

Owing to the environmental friendliness and vast advantages that enzymes offer in the biotechnology and industry fields, biocatalysts are a prolific investigation field. However, the low catalytic activity, stability, and specific selectivity of the enzyme limit the range of the reaction enzymes involved in. A comprehensive understanding of the protein structure and dynamics in terms of molecular details enables us to tackle these limitations effectively and enhance the catalytic activity by enzyme engineering or modifying the supports and solvents. Along with different strategies including computational, enzyme engineering based on DNA recombination, enzyme immobilization, additives, chemical modification, and physicochemical modification approaches can be promising for the wide spread of industrial enzyme usage. This is attributed to the successful application of biocatalysts in industrial and synthetic processes requires a system that exhibits stability, activity, and reusability in a continuous flow process, thereby reducing the production cost. The main goal of this review is to display relevant approaches for improving enzyme characteristics to overcome their industrial application.

Enhanced Sequence-Activity Mapping and Evolution of Artificial Metalloenzymes by Active Learning

Tailored enzymes are crucial for the transition to a sustainable bioeconomy. However, enzyme engineering is laborious and failure-prone due to its reliance on serendipity. The efficiency and success rates of engineering campaigns may be improved by applying machine learning to map the sequence-activity landscape based on small experimental data sets. Yet, it often proves challenging to reliably model large sequence spaces while keeping the experimental effort tractable. To address this challenge, we present an integrated pipeline combining large-scale screening with active machine learning, which we applied to engineer an artificial metalloenzyme (ArM) catalyzing a new-to-nature hydroamination reaction. Combining lab automation and next-generation sequencing, we acquired sequence-activity data for several thousand ArM variants. We then used Gaussian process regression to model the activity landscape and guide further screening rounds. Critical characteristics of our pipeline include the cost-effective generation of information-rich data sets, the integration of an explorative round to improve the model's performance, and the inclusion of experimental noise. Our approach led to an order-of-magnitude boost in the hit rate while making efficient use of experimental resources. Search strategies like this should find broad utility in enzyme engineering and accelerate the development of novel biocatalysts.

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.

Rational evolution for altering the ligand preference of estrogen receptor alpha

Estrogen receptor α is commonly used in synthetic biology to control the activity of genome editing tools. The activating ligands, estrogens, however, interfere with various cellular processes, thereby limiting the applicability of this receptor. Altering its ligand preference to chemicals of choice solves this hurdle but requires adaptation of unspecified ligand-interacting residues. Here, we provide a solution by combining rational protein design with multi-site-directed mutagenesis and directed evolution of stably integrated variants in Saccharomyces cerevisiae. This method yielded an estrogen receptor variant, named TERRA, that lost its estrogen responsiveness and became activated by tamoxifen, an anti-estrogenic drug used for breast cancer treatment. This tamoxifen preference of TERRA was maintained in mammalian cells and mice, even when fused to Cre recombinase, expanding the mammalian synthetic biology toolbox. Not only is our platform transferable to engineer ligand preference of any steroid receptor, it can also profile drug-resistance landscapes for steroid receptor-targeted therapies.

Using directed evolution to improve hydrogen production in chimeric hydrogenases from algal species

Algae generate hydrogen from sunlight and water utilizing high-energy electrons generated during photosynthesis. The amount of hydrogen produced in heterologous expression of the wild-type hydrogenase is currently insufficient for industrial applications. One approach to improve hydrogen yields is through directed evolution of the DNA of the native hydrogenase. Here, we created 113 chimeric algal hydrogenase gene variants derived from combining segments of three parent hydrogenases, two from Chlamydomonas reinhardtii (CrHydA1 and CrHydA2) and one from Scenedesmus obliquus (HydA1). To generate chimeras, there were seven segments into which each of the parent hydrogenase genes was divided and recombined in a variety of combinations. The chimeric and parental hydrogenase sequences were cloned for heterologous expression in Escherichia coli, and 40 of the resultant enzymes expressed were assayed for H2 production. Chimeric clones that resulted in equal or greater production obtained with the cloned CrHydA1 parent hydrogenase were those comprised of CrHydA1 sequence in segments #1, 2, 3, and/or 4. These best-performing chimeras all contained one common region, segment #2, the part of the sequence known to contain important amino acids involved in proton transfer or hydrogen cluster coordination. The amino acid sequence distances among all chimeric clones to that of the CrHydA1 parent were determined, and the relationship between sequence distances and experimentally-derived H2 production was evaluated. An additional model determined the correlation between electrostatic potential energy surface area ratios and H2 production. The model yielded several algal mutants with predicted hydrogen productions in a range of two to three times that of the wild-type hydrogenase. The mutant data and the model can now be used to predict which specific mutant sequences may result in even higher hydrogen yields. Overall, results provide more precise details in planning future directed evolution to functionally improve algal hydrogenases.

Intermediate product control in cascade reaction for one-pot production of ε-caprolactone by Escherichia coli

ε-Caprolactone is an important non-toxic compound for polymer synthesis like polycaprolactone which has been widely used in drug delivery and degradable plastics. To meet the demand for a green economy, a bi-enzymatic cascade, consisting of an alcohol dehydrogenase (ADH) and a cyclohexanone monooxygenase (CHMO), was designed and introduced into Escherichia coli to synthesize ε-caprolactone from cyclohexanol with a self-sufficient NADPH-cofactor regeneration system. To further improve the catalytic efficiency, a carbonyl group-dependent colorimetric method using inexpensive 2,4-dinitrophenylhydrazine (DNPH) was developed for assay of cyclohexanone, an intermediate production of cascade reaction. It can be used to screen mutant strains with high catalytic efficiency from high-throughput library by detecting the absorbance value in microtiter plates (MTP) instead of gas chromatography (GC) analysis. Moreover, an RBS combinatorial library was constructed for balancing the expression of ADH and CHMO from two independent transcriptional units. After the high-throughput screening based on intermediate product control, an optimal variant with higher substrate tolerance and long-term stability was obtained from RBS combinatorial library. Through a fed-batch process, ε-caprolactone production reached 148.2 mM after 70 h of reaction under the optimized conditions, which was the highest yield achieved to date.

Enzymatic Photodecarboxylation on Secondary and Tertiary Carboxylic Acids

Photoenzymatic decarboxylation of bulky secondary and tertiary carboxylic acids catalyzed by engineered Chlorella variabilis fatty acid photodecarboxylase (CvFAP) is reported. Rational design and directed evolution of wild-type CvFAP are used to improve the reactivity and expand potential applications. Moreover, engineered CvFAP can catalyze light-driven kinetic resolution of α-substituted carboxylic acid. Our work sheds light on the production of chiral building blocks and bioactive molecules from bulky carboxylic acids via the photoenzymatic way.

Sequence- and Structure-Based Mining of Thermostable D-Allulose 3-Epimerase and Computer-Guided Protein Engineering To Improve Enzyme Activity

D-Allulose, a functional sweetener, can be synthesized from fructose using D-allulose 3-epimerase (DAEase). Nevertheless, a majority of the reported DAEases have inadequate stability under harsh industrial reaction conditions, which greatly limits their practical applications. In this study, big data mining combined with a computer-guided free energy calculation strategy was employed to discover a novel DAEase with excellent thermostability. Consensus sequence analysis of flexible regions and comparison of binding energies after substrate docking were performed using phylogeny-guided big data analyses. TtDAE from Thermogutta terrifontis was the most thermostable among 358 candidate enzymes, with a half-life of 32 h at 70 °C. Subsequently, structure-guided virtual screening and a customized strategy based on a combinatorial active-site saturation test/iterative saturation mutagenesis were utilized to engineer TtDAE. Finally, the catalytic activity of the M4 variant (P105A/L14C/T63G/I65A) was increased by 5.12-fold. Steered molecular dynamics simulations indicated that M4 had an enlarged substrate-binding pocket, which enhanced the fit between the enzyme and the substrate. The approach presented here, combining DAEases mining with further rational modification, provides guidance for obtaining promising catalysts for industrial-scale production.

Optogenetic Microwell Array Screening System: A High-Throughput Engineering Platform for Genetically Encoded Fluorescent Indicators

Genetically encoded fluorescent indicators (GEFIs) are protein-based optogenetic tools that change their fluorescence intensity when binding specific ligands in cells and tissues. GEFI encoding DNA can be expressed in cell subtypes while monitoring cellular physiological responses. However, engineering GEFIs with physiological sensitivity and pharmacological specificity often requires iterative optimization through trial-and-error mutagenesis while assessing their biophysical function in vitro one by one. Here, the vast mutational landscape of proteins constitutes a significant obstacle that slows GEFI development, particularly for sensors that rely on mammalian host systems for testing. To overcome these obstacles, we developed a multiplexed high-throughput engineering platform called the optogenetic microwell array screening system (Opto-MASS) that functionally tests thousands of GEFI variants in parallel in mammalian cells. Opto-MASS represents the next step for engineering optogenetic tools as it can screen large variant libraries orders of magnitude faster than current methods. We showcase this system by testing over 13,000 dopamine and 21,000 opioid sensor variants. We generated a new dopamine sensor, dMASS1, with a >6-fold signal increase to 100 nM dopamine exposure compared to its parent construct. Our new opioid sensor, μMASS1, has a ∼4.6-fold signal increase over its parent scaffold's response to 500 nM DAMGO. Thus, Opto-MASS can rapidly engineer new sensors while significantly shortening the optimization time for new sensors with distinct biophysical properties.

New Mitogenomes of the Green Lacewing Tribe Ankylopterygini (Neuroptera: Chrysopidae: Chrysopinae) and Phylogenetic Implications of Chrysopidae

Chrysopidae (green lacewings) are a cosmopolitan and species-rich family of Neuroptera, with remarkable significance of biological control against various agricultural and forestry pests. However, the phylogenetic position of Chrysopidae in Neuroptera and the internal relationships within the family remain equivocal among previous studies based on different types of data and sampling. Here we sequenced the mitochondrial genomes (mitogenomes) of two species of the genus Ankylopteryx in the chrysopine tribe Ankylopterygini for the first time. The characteristics of these mitogenomes were analyzed in comparison with other green lacewing mitogenomes published to date. In the phylogeny herein reconstructed based on mitogenomes, Chrysopinae were recovered as the sister group to Apochrysinae + Nothochrysinae. Within the subfamily of Chrysopinae, Nothancylini were recovered as the sister group to (Leucochrysini + Belonopterygini) + (Ankylopterygini + Chrysopini). The divergence time estimation suggested an Early Cretaceous initial divergence within the extant Chrysopidae. Within Chrysopinae, the four tribes except Nothancylini diverged around mid-Cretaceous.

Accelerating Biocatalysis Discovery with Machine Learning: A Paradigm Shift in Enzyme Engineering, Discovery, and Design

Emerging computational tools promise to revolutionize protein engineering for biocatalytic applications and accelerate the development timelines previously needed to optimize an enzyme to its more efficient variant. For over a decade, the benefits of predictive algorithms have helped scientists and engineers navigate the complexity of functional protein sequence space. More recently, spurred by dramatic advances in underlying computational tools, the promise of faster, cheaper, and more accurate enzyme identification, characterization, and engineering has catapulted terms such as artificial intelligence and machine learning to the must-have vocabulary in the field. This Perspective aims to showcase the current status of applications in pharmaceutical industry and also to discuss and celebrate the innovative approaches in protein science by highlighting their potential in selected recent developments and offering thoughts on future opportunities for biocatalysis. It also critically assesses the technology's limitations, unanswered questions, and unmet challenges.

Synthesis of epitope-targeting nanobody based on native protein-protein interactions for FtsZ filamentation suppressor

Phage display and biopanning are powerful tools for generating binding molecules for a specific target. However, the selection process based only on binding affinity provides no assurance for the antibody's affinity to the target epitope. In this study, we propose a molecular-evolution approach guided by native protein-protein interactions to generate epitope-targeting antibodies. The binding-site sequence in a native protein was grafted into a complementarity-determining region (CDR) in the nanobody, and a nonrelated CDR loop (in the grafted nanobody) was randomized to create a phage display library. In this construction of nanobodies by integrating graft and evolution technology (CAnIGET method), suitable grafting of the functional sequence added functionality to the nanobody, and the molecular-evolution approach enhanced the binding function to inhibit the native protein-protein interactions. To apply for biological tool with growth screening, model nanobodies with an affinity for filamenting temperature-sensitive mutant Z (FtsZ) from Staphylococcus aureus were constructed and completely inhibited the polymerization of FtsZ as a function. Consequently, the expression of these nanobodies drastically decreased the cell division rate. We demonstrate the potential of the CAnIGET method with the use of native protein-protein interactions for steady epitope-specific evolutionary engineering.

Fishing for Catalysis: Experimental Approaches to Narrowing Search Space in Directed Evolution of Enzymes

Directed evolution has transformed protein engineering offering a path to rapid improvement of protein properties. Yet, in practice it is limited by the hyper-astronomic protein sequence search space, and approaches to identify mutagenic hot spots, i.e., locations where mutations are most likely to have a productive impact, are needed. In this perspective, we categorize and discuss recent progress in the experimental approaches (broadly defined as structural, bioinformatic, and dynamic) to hot spot identification. Recent successes in harnessing protein dynamics and machine learning approaches provide new opportunities for the field and will undoubtedly help directed evolution reach its full potential.

LibGENiE - A bioinformatic pipeline for the design of information-enriched enzyme libraries

Enzymes are potent catalysts with high specificity and selectivity. To leverage nature's synthetic potential for industrial applications, various protein engineering techniques have emerged which allow to tailor the catalytic, biophysical, and molecular recognition properties of enzymes. However, the many possible ways a protein can be altered forces researchers to carefully balance between the exhaustiveness of an enzyme screening campaign and the required resources. Consequently, the optimal engineering strategy is often defined on a case-by-case basis. Strikingly, while predicting mutations that lead to an improved target function is challenging, here we show that the prediction and exclusion of deleterious mutations is a much more straightforward task as analyzed for an engineered carbonic acid anhydrase, a transaminase, a squalene-hopene cyclase and a Kemp eliminase. Combining such a pre-selection of allowed residues with advanced gene synthesis methods opens a path toward an efficient and generalizable library construction approach for protein engineering. To give researchers easy access to this methodology, we provide the website LibGENiE containing the bioinformatic tools for the library design workflow.

Context-dependence of T-loop Mediated Long-range RNA Tertiary Interactions

The architecture and folding of complex RNAs is governed by a limited set of highly recurrent structural motifs that form long-range tertiary interactions. One of these motifs is the T-loop, which was first identified in tRNA but is broadly distributed across biological RNAs. While the T-loop has been examined in detail in different biological contexts, the various receptors that it interacts with are not as well defined. In this study, we use a cell-based genetic screen in concert with bioinformatic analysis to examine three different, but related, T-loop receptor motifs found in the flavin mononucleotide (FMN) and cobalamin (Cbl) riboswitches. As a host for different T-loop receptors, we employed the env8 class-II Cbl riboswitch, an RNA that uses two T-loop motifs for both folding and supporting the ligand binding pocket. A set of libraries was created in which select nucleotides that participate in the T-loop/T-loop receptor (TL/TLR) interaction were fully randomized. Library members were screened for their ability to support Cbl-dependent expression of a reporter gene. While T-loops appear to be variable in sequence, we find that the functional sequence space is more restricted in the Cbl riboswitch, suggesting that TL/TLR interactions are context dependent. Our data reveal clear sequence signatures for the different types of receptor motifs that align with phylogenic analysis of these motifs in the FMN and Cbl riboswitches. Finally, our data suggest the functional contribution of various nucleobase-mediated long-range interactions within the riboswitch subclass of TL/TLR interactions that are distinct from those found in other RNAs.

Inside Out Computational Redesign of Cavities for Improving Thermostability and Catalytic Activity of Rhizomucor Miehei Lipase

Cavities are created by hydrophobic interactions between residue side chain atoms during the folding of enzymes. Redesigning cavities can improve the thermostability and catalytic activity of the enzyme; however, the synergistic effect of cavities remains unclear. In this study, Rhizomucor miehei lipase (RML) was used as a model to explore volume fluctuation and spatial distribution changes of the internal cavities, which could reveal the roles of internal cavities in the thermostability and catalytic activity. We present an inside out cavity engineering (CE) strategy based on computational techniques to explore how changes in the volumes and spatial distribution of cavities affect the thermostability and catalytic activity of the enzyme. We obtained 12 single-point mutants, among which the melting temperatures (Tm) of 8 mutants showed an increase of more than 2°C. Sixteen multipoint mutations were further designed by spatial distribution rearrangement of internal cavities. The Tm of the most stable triple variant, with mutations including T21V (a change of T to V at position 21), S27A, and T198L (T21V/S27A/T198L), was elevated by 11.0°C, together with a 28.7-fold increase in the half-life at 65°C and a specific activity increase of 9.9-fold (up to 5,828 U mg-1), one of the highest lipase activities reported. The possible mechanism of decreased volumes and spatial rearrangement of the internal cavities improved the stability of the enzyme, optimizing the outer substrate tunnel to improve the catalytic efficiency. Overall, the inside out computational redesign of cavities method could help to deeply understand the effect of cavities on enzymatic stability and activity, which would be beneficial for protein engineering efforts to optimize natural enzymes. IMPORTANCE In the present study, R. miehei lipase, which is widely used in various industries, provides an opportunity to explore the effects of internal cavities on the thermostability and catalytic activity of enzymes. Here, we execute high hydrostatic pressure molecular dynamics (HP-MD) simulations to screen the critical internal cavity and reshape the internal cavities through site-directed mutation. We show that as the global internal cavity volume decreases, cavity rearrangement can improve the stability of the protein while optimizing the substrate channel to improve the catalytic efficiency. Our results provide significant insights into understanding the mechanism of action of the internal cavity. Our strategy is expected to be applied to other enzymes to promote increases in thermostability and catalytic activity.

Mutation Hotspot for Changing the Substrate Specificity of β-N-Acetylhexosaminidase: A Library of GlcNAcases

β-N-Acetylhexosaminidase from Talaromyces flavus (TfHex; EC 3.2.1.52) is an exo-glycosidase with dual activity for cleaving N-acetylglucosamine (GlcNAc) and N-acetylgalactosamine (GalNAc) units from carbohydrates. By targeting a mutation hotspot of the active site residue Glu332, we prepared a library of ten mutant variants with their substrate specificity significantly shifted towards GlcNAcase activity. Suitable mutations were identified by in silico methods. We optimized a microtiter plate screening method in the yeast Pichia pastoris expression system, which is required for the correct folding of tetrameric fungal β-N-acetylhexosaminidases. While the wild-type TfHex is promiscuous with its GalNAcase/GlcNAcase activity ratio of 1.2, the best single mutant variant Glu332His featured an 8-fold increase in selectivity toward GlcNAc compared with the wild-type. Several prepared variants, in particular Glu332Thr TfHex, had significantly stronger transglycosylation capabilities than the wild-type, affording longer chitooligomers - they behaved like transglycosidases. This study demonstrates the potential of mutagenesis to alter the substrate specificity of glycosidases.

Insertions and deletions in protein evolution and engineering

Protein evolution or engineering studies are traditionally focused on amino acid substitutions and the way these contribute to fitness. Meanwhile, the insertion and deletion of amino acids is often overlooked, despite being one of the most common sources of genetic variation. Recent methodological advances and successful engineering stories have demonstrated that the time is ripe for greater emphasis on these mutations and their understudied effects. This review highlights the evolutionary importance and biotechnological relevance of insertions and deletions (indels). We provide a comprehensive overview of approaches that can be employed to include indels in random, (semi)-rational or computational protein engineering pipelines. Furthermore, we discuss the tolerance to indels at the structural level, address how domain indels can link the function of unrelated proteins, and feature studies that illustrate the surprising and intriguing potential of frameshift mutations.

Sequence and Structure-Guided Engineering of Urethanase from Agrobacterium tumefaciens d3 for Improved Catalytic Activity

The amidase from Agrobacterium tumefaciens d3 (AmdA) degrades the carcinogenic ethyl carbamate (EC) in alcoholic beverages. However, its limited catalytic activity hinders practical applications. Here, multiple sequence alignment was first used to predict single variants with improved activity. Afterward, AlphaFold 2 was applied to predict the three-dimensional structure of AmdA and 21 amino acids near the catalytic triad were randomized by saturation mutagenesis. Each of the mutation libraries was then screened, and the improved single variants were combined to obtain the best double variant I97L/G195A that showed a 3.1-fold increase in the urethanase activity and a 1.5-fold increase in ethanol tolerance. MD simulations revealed that the mutations shortened the distance between catalytic residues and the substrate and enhanced the occurrence of a critical hydrogen bond in the catalytic pocket. This study displayed a useful strategy to engineer an amidase for the improvement of urethanase activity, and the variant obtained provided a good candidate for applications in the food industry.

Optimization of the antimicrobial peptide Bac7 by deep mutational scanning

BACKGROUND

Intracellularly active antimicrobial peptides are promising candidates for the development of antibiotics for human applications. However, drug development using peptides is challenging as, owing to their large size, an enormous sequence space is spanned. We built a high-throughput platform that incorporates rapid investigation of the sequence-activity relationship of peptides and enables rational optimization of their antimicrobial activity. The platform is based on deep mutational scanning of DNA-encoded peptides and employs highly parallelized bacterial self-screening coupled to next-generation sequencing as a readout for their antimicrobial activity. As a target, we used Bac71-23, a 23 amino acid residues long variant of bactenecin-7, a potent translational inhibitor and one of the best researched proline-rich antimicrobial peptides.

RESULTS

Using the platform, we simultaneously determined the antimicrobial activity of >600,000 Bac71-23 variants and explored their sequence-activity relationship. This dataset guided the design of a focused library of ~160,000 variants and the identification of a lead candidate Bac7PS. Bac7PS showed high activity against multidrug-resistant clinical isolates of E. coli, and its activity was less dependent on SbmA, a transporter commonly used by proline-rich antimicrobial peptides to reach the cytosol and then inhibit translation. Furthermore, Bac7PS displayed strong ribosomal inhibition and low toxicity against eukaryotic cells and demonstrated good efficacy in a murine septicemia model induced by E. coli.

CONCLUSION

We demonstrated that the presented platform can be used to establish the sequence-activity relationship of antimicrobial peptides, and showed its usefulness for hit-to-lead identification and optimization of antimicrobial drug candidates.

Highly Efficient Libraries Design for Saturation Mutagenesis

Saturation mutagenesis is a semi-rational approach for protein engineering where sites are saturated either entirely or partially to include amino acids of interest. We previously reported on a codon compression algorithm, where a set of minimal degenerate codons are selected according to user-defined parameters such as the target organism, type of saturation and usage levels. Here, we communicate an addition to our web tool that considers the distance between the wild-type codon and the library, depending on its purpose. These forms of restricted collections further reduce library size, lowering downstream screening efforts or, in turn, allowing more comprehensive saturation of multiple sites. The library design tool can be accessed via http://www.dynamcc.com/dynamcc_d/.

Graphical Abstract

Remote B-Ring Oxidation of Sclareol with an Engineered P450 Facilitates Divergent Access to Complex Terpenoids

Though chiral pool synthesis is widely accepted as a powerful strategy in complex molecule synthesis, the effectiveness of the approach is intimately linked to the range of available chiral building blocks and the functional groups they possess. To date, there is still a pressing need for new remote functionalization methods that would allow the installation of useful chemical handles on these building blocks to enable a broader spectrum of synthetic manipulations. Herein, we report the engineering of a P450_BM3 variant for the regioselective C-H oxidation of sclareol at C6. The synthetic utility of the resulting product was demonstrated in a formal synthesis of ansellone B, the first total synthesis of the 2,3-seco-labdane excolide B, and a model study toward (+)-pallavicinin.

Synthetic acid stress-tolerance modules improve growth robustness and lysine productivity of industrial Escherichia coli in fermentation at low pH

Background

During fermentation, industrial microorganisms encounter multiple stresses that inhibit cell growth and decrease fermentation yields, in particular acid stress, which is due to the accumulation of acidic metabolites in the fermentation medium. Although the addition of a base to the medium can counteract the effect of acid accumulation, the engineering of acid-tolerant strains is considered a more intelligent and cost-effective solution. While synthetic biology theoretically provides a novel approach for devising such tolerance modules, in practice it is difficult to assemble stress-tolerance modules from hundreds of stress-related genes.

Results

In this study, we designed a set of synthetic acid-tolerance modules for fine-tuning the expression of multi-component gene blocks comprising a member of the proton-consuming acid resistance system (gadE), a periplasmic chaperone (hdeB), and reactive oxygen species (ROS) scavengers (sodB and katE). Directed evolution was used to construct an acid-responsive asr promoter library, from which four variants were selected and used in the synthetic modules. The module variants were screened in a stepwise manner under mild acidic conditions (pH 5–6), first by cell growth using the laboratory Escherichia coli strain MG1655 cultured in microplates, and then by lysine production performance using the industrial lysine-producing E. coli strain MG1655 SCEcL3 cultured first in multiple 10-mL micro-bioreactors, and then in 1.3-L parallel bioreactors. The procedure resulted in the identification of a best strain with lysine titer and yield at pH 6.0 comparable to the parent strain at pH 6.8.

Conclusion

Our results demonstrate a promising synthetic-biology strategy to enhance the growth robustness and productivity of E. coli upon the mildly acidic conditions, in both a general lab strain MG1655 and an industrial lysine-producing strain SCEcL3, by using the stress-responsive synthetic acid-tolerance modules comprising a limited number of genes. This study provides a reliable and efficient method for achieving synthetic modules of interest, particularly in improving the robustness and productivity of industrial strains.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-022-01795-4.

Combinatorial InVitroFlow-assisted Mutagenesis (CombIMut) yields a 41-fold improved CelA2 cellulase

The combination of diversity generation methods and ultrahigh-throughput screening (uHTS) technologies is key to efficiently explore nature's sequence space and elucidate structure-function relationships of enzymes. Beneficial substitutions often cluster in a few regions and simultaneous amino acid substitutions at multiple positions (e.g., by OmniChange) will likely lead to further improved enzyme variants. An extensive screening effort is required to identify such variants, as the simultaneous randomization of four codons can easily yield over 10⁵ potential enzyme variants. The combination of flow cytometer-based uHTS with cell-free compartmentalization technology using (w/o/w) double emulsions (InVitroFlow), provides analysis capabilities of up to 10⁷ events per hour, thus enabling efficient screening. InVitroFlow is an elegant solution since diversity loss through a transformation of host cells is omitted and emulsion compartments provide a genotype-phenotype linkage through a fluorescence readout. In this work, a multi-site saturation mutagenesis (mSSM) and an OmniChange library with four simultaneously saturated positions in the active site of CelA2 cellulase were screened using InVitroFlow. Screening of over 36 million events, yielded a significantly improved cellulase variant CelA2-M3 (H288F/H524Q) with an 8-fold increase in specific activity compared to the parent CelA2-H288F (83.9 U/mg) and a 41-fold increased specific activity (674.5 U/mg) compared to wildtype CelA2 (16.6 U/mg) for the substrate 4-MUC (4-methylumbelliferyl-β D-cellobioside). This article is protected by copyright. All rights reserved.

Algorithm-aided engineering of aliphatic halogenase WelO5* for the asymmetric late-stage functionalization of soraphens

Late-stage functionalization of natural products offers an elegant route to create novel entities in a relevant biological target space. In this context, enzymes capable of halogenating sp³ carbons with high stereo- and regiocontrol under benign conditions have attracted particular attention. Enabled by a combination of smart library design and machine learning, we engineer the iron/α-ketoglutarate dependent halogenase WelO5* for the late-stage functionalization of the complex and chemically difficult to derivatize macrolides soraphen A and C, potent anti-fungal agents. While the wild type enzyme WelO5* does not accept the macrolide substrates, our engineering strategy leads to active halogenase variants and improves upon their apparent k_cat and total turnover number by more than 90-fold and 300-fold, respectively. Notably, our machine-learning guided engineering approach is capable of predicting more active variants and allows us to switch the regio-selectivity of the halogenases facilitating the targeted analysis of the derivatized macrolides’ structure-function activity in biological assays.

The late-stage functionalization of unactivated carbon–hydrogen bonds is a difficult but important task, which has been met with promising but limited success through synthetic organic chemistry. Here the authors use machine learning to engineer WelO5* halogenase variants, which led to regioselective chlorination of inert C–H bonds on a representative polyketide that is a non-natural substrate for the enzyme.

A Sustainable Approach for Synthesizing (R)-4-Aminopentanoic Acid From Levulinic Acid Catalyzed by Structure-Guided Tailored Glutamate Dehydrogenase

In this study, a novel enzymatic approach to transform levulinic acid (LA), which can be obtained from biomass, into value-added (R)-4-aminopentanoic acid using an engineered glutamate dehydrogenase from Escherichia coli (EcGDH) was developed. Through crystal structure comparison, two residues (K116 and N348), especially residue 116, were identified to affect the substrate specificity of EcGDH. After targeted saturation mutagenesis, the mutant EcGDHK116C, which was active toward LA, was identified. Screening of the two-site combinatorial saturation mutagenesis library with EcGDHK116C as positive control, the k cat/K m of the obtained EcGDHK116Q/N348M for LA and NADPH were 42.0- and 7.9-fold higher, respectively, than that of EcGDHK116C. A molecular docking investigation was conducted to explain the catalytic activity of the mutants and stereoconfiguration of the product. Coupled with formate dehydrogenase, EcGDHK116Q/N348M was found to be able to convert 0.4 M LA by more than 97% in 11 h, generating (R)-4-aminopentanoic acid with >99% enantiomeric excess (ee). This dual-enzyme system used sustainable raw materials to synthesize (R)-4-aminopentanoic acid with high atom utilization as it utilizes cheap ammonia as the amino donor, and the inorganic carbonate is the sole by-product.

Fluorescence-Activated Cell Sorting as a Tool for Recombinant Strain Screening

Metabolic engineering of microbial cells is the discipline of optimizing microbial metabolism to enable and improve the production of target molecules ranging from biofuels and chemical building blocks to high-value pharmaceuticals. The advances in genetic engineering have eased the construction of highly engineered microbial strains and the generation of genetic libraries. Intracellular metabolite-responsive biosensors facilitate high-throughput screening of these libraries by connecting the levels of a metabolite of interest to a fluorescence output. Fluorescent-activated cell sorting (FACS) enables the isolation of highly fluorescent single cells and thus genotypes that produce higher levels of the metabolite of interest. Here, we describe a high-throughput screening method for recombinant yeast strain screening based on intracellular biosensors and FACS.

Critical assessment of structure-based approaches to improve protein resistance in aqueous ionic liquids by enzyme-wide saturation mutagenesis

Ionic liquids (IL) and aqueous ionic liquids (aIL) are attractive (co-)solvents for green industrial processes involving biocatalysts, but often reduce enzyme activity. Experimental and computational methods are applied to predict favorable substitution sites and, most often, subsequent site-directed surface charge modifications are introduced to enhance enzyme resistance towards aIL. However, almost no studies evaluate the prediction precision with random mutagenesis or the application of simple data-driven filtering processes. Here, we systematically and rigorously evaluated the performance of 22 previously described structure-based approaches to increase enzyme resistance to aIL based on an experimental complete site-saturation mutagenesis library of Bacillus subtilis Lipase A (BsLipA) screened against four aIL. We show that, surprisingly, most of the approaches yield low gain-in-precision (GiP) values, particularly for predicting relevant positions: 14 approaches perform worse than random mutagenesis. Encouragingly, exploiting experimental information on the thermostability of BsLipA or structural weak spots of BsLipA predicted by rigidity theory yields GiP = 3.03 and 2.39 for relevant variants and GiP = 1.61 and 1.41 for relevant positions. Combining five simple-to-compute physicochemical and evolutionary properties substantially increases the precision of predicting relevant variants and positions, yielding GiP = 3.35 and 1.29. Finally, combining these properties with predictions of structural weak spots identified by rigidity theory additionally improves GiP for relevant variants up to 4-fold to ∼10 and sustains or increases GiP for relevant positions, resulting in a prediction precision of ∼90% compared to ∼9% in random mutagenesis. This combination should be applicable to other enzyme systems for guiding protein engineering approaches towards improved aIL resistance.

Learning Strategies in Protein Directed Evolution

Synthetic biology is a fast-evolving research field that combines biology and engineering principles to develop new biological systems for medical, pharmacological, and industrial applications. Synthetic biologists use iterative "design, build, test, and learn" cycles to efficiently engineer genetic systems that are reliable, reproducible, and predictable. Protein engineering by directed evolution can benefit from such a systematic engineering approach for various reasons. Learning can be carried out before starting, throughout or after finalizing a directed evolution project. Computational tools, bioinformatics, and scanning mutagenesis methods can be excellent starting points, while molecular dynamics simulations and other strategies can guide engineering efforts. Similarly, studying protein intermediates along evolutionary pathways offers fascinating insights into the molecular mechanisms shaped by evolution. The learning step of the cycle is not only crucial for proteins or enzymes that are not suitable for high-throughput screening or selection systems, but it is also valuable for any platform that can generate a large amount of data that can be aided by machine learning algorithms. The main challenge in protein engineering is to predict the effect of a single mutation on one functional parameter-to say nothing of several mutations on multiple parameters. This is largely due to nonadditive mutational interactions, known as epistatic effects-beneficial mutations present in a genetic background may not be beneficial in another genetic background. In this work, we provide an overview of experimental and computational strategies that can guide the user to learn protein function at different stages in a directed evolution project. We also discuss how epistatic effects can influence the success of directed evolution projects. Since machine learning is gaining momentum in protein engineering and the field is becoming more interdisciplinary thanks to collaboration between mathematicians, computational scientists, engineers, molecular biologists, and chemists, we provide a general workflow that familiarizes nonexperts with the basic concepts, dataset requirements, learning approaches, model capabilities and performance metrics of this intriguing area. Finally, we also provide some practical recommendations on how machine learning can harness epistatic effects for engineering proteins in an "outside-the-box" way.

Directed Evolution of Transcription Factor-Based Biosensors for Altered Effector Specificity

Transcription factor-based biosensors are important tools in Synthetic Biology for the sensing of industrially valuable molecules and clinically important metabolites, therefore presenting applications in the bioremediation, industrial biotechnology, and biomedical fields. The directed evolution of allosteric transcription factors (aTFs) with the aim of altering effector specificity has the potential for the development of new biosensors to detect natural and nonnatural molecules, expanding the scope of available aTF-based biosensors. In this chapter, we delineate a general method for the directed evolution of aTFs. The theory of library design is discussed, along with the detailed methodology for an improved transformation of combined libraries, and the experimental search space by counterselection using fluorescence-activated cell sorting (FACS) is presented.

Asymmetric Cation-Olefin Monocyclization by Engineered Squalene-Hopene Cyclases

Squalene-hopene cyclases (SHCs) have great potential for the industrial synthesis of enantiopure cyclic terpenoids. A limitation of SHC catalysis has been the enzymes' strict (S)-enantioselectivity at the stereocenter formed after the first cyclization step. To gain enantio-complementary access to valuable monocyclic terpenoids, an SHC-wild-type library including 18 novel homologs was set up. A previously not described SHC (AciSHC) was found to synthesize small amounts of monocyclic (R)-γ-dihydroionone from (E/Z)-geranylacetone. Using enzyme and process optimization, the conversion to the desired product was increased to 79 %. Notably, analyzed AciSHC variants could finely differentiate between the geometric geranylacetone isomers: While the (Z)-isomer yielded the desired monocyclic (R)-γ-dihydroionone (>99 % ee), the (E)-isomer was converted to the (S,S)-bicyclic ether (>95 % ee). Applying the knowledge gained from the observed stereodivergent and enantioselective transformations to an additional SHC-substrate pair, access to the complementary (S)-γ-dihydroionone (>99.9 % ee) could be obtained.

The evolving art of creating genetic diversity: From directed evolution to synthetic biology

The ability to engineer biological systems, whether to introduce novel functionality or improved performance, is a cornerstone of biotechnology and synthetic biology. Typically, this requires the generation of genetic diversity to explore variations in phenotype, a process that can be performed at many levels, from single molecule targets (i.e., in directed evolution of enzymes) to whole organisms (e.g., in chassis engineering). Recent advances in DNA synthesis technology and automation have enhanced our ability to create variant libraries with greater control and throughput. This review highlights the latest developments in approaches to create such a hierarchy of diversity from the enzyme level to entire pathways in vitro, with a focus on the creation of combinatorial libraries that are required to navigate a target's vast design space successfully to uncover significant improvements in function.

Development of MEMS directed evolution strategy for multiplied throughput and convergent evolution of cytochrome P450 enzymes

Directed evolution (DE) inspired by natural evolution (NE) has been achieving tremendous successes in protein/enzyme engineering. However, the conventional "one-protein-for-one-task" DE cannot match the "multi-proteins-for-multi-tasks" NE in terms of screening throughput and efficiency, thus often failing to meet the fast-growing demands for biocatalysts with desired properties. In this study, we design a novel "multi-enzymes-for-multi-substrates" (MEMS) DE model and establish the proof-of-concept by running a NE-mimicking and higher-throughput screening on the basis of "two-P450s-against-seven-substrates" (2P×7S) in one pot. With the multiplied throughput and improved hit rate, we witness a series of convergent evolution events of the two archetypal cytochrome P450 enzymes (P450 BM3 and P450cam) in laboratory. It is anticipated that the new strategy of MEMS DE will find broader application for a larger repertoire of enzymes in the future. Furthermore, structural and substrate docking analysis of the two functionally convergent P450 variants provide important insights into how distinct P450 active-sites can reach a common catalytic goal.

A Machine Learning Study on the Thermostability Prediction of (R)-ω-Selective Amine Transaminase from Aspergillus terreus

Artificial intelligence technologies such as machine learning have been applied to protein engineering, with unique advantages in protein structure, function prediction, catalytic activity, and other issues in recent years. Screening better mutants is still a bottleneck in protein engineering. In this paper, a new sequence-activity relationship method was analyzed for its application in improving the thermal stability of Aspergillus terreus (R)-ω-selective amine transaminase. The experimental data from 6 single-point mutated enzymes were used as a learning dataset to build models and predict the thermostability of 2⁶ mutants. Based on digital signal processing (DSP), this method digitized the amino acid sequence of proteins by fast Fourier transform (FFT) and then established the best model applying partial least squares regression (PLSR) to screen out all possible mutants, especially those with high performance. In protein engineering, the innovative sequence activity relationship (ISAR) method can make a reasonable prediction using limited experimental data and significantly reduce the experimental cost. The half-life (T_1/2) of (R)-ω-transaminase was fitted with the amino acid sequence by the ISAR algorithm, resulting in an R² of 0.8929 and a cvRMSE of 4.89. At the same time, the mutants with higher T_1/2 than the existing ones were predicted, laying the groundwork for better (R)-ω-transaminase in the later stage. The ISAR algorithm is expected to provide a new technique for protein evolution and screening.

Engineering Aptazyme Switches for Conditional Gene Expression in Mammalian Cells Utilizing an In Vivo Screening Approach

Artificial RNA switches are an emerging class of genetic controllers suitable for synthetic biology applications. Aptazymes are fusions composed of an aptamer domain and a self-cleaving ribozyme. The utilization of aptazymes for conditional gene expression displays several advantages over employing conventional transcription factor-based techniques as aptazymes require minimal genomic space, fulfill their function without the need of protein cofactors and most importantly are reprogrammable with respect to ligand selectivity and the RNA function to be regulated. Technologies that enable the generation of aptazymes to defined input ligands are of interest for the construction of biocomputing devices and biosensing applications. In this chapter we present a method that facilitates the in vivo screening of randomized pools of aptazymes in mammalian cells.

High-Throughput Solid-Phase Assay for Substrate Profiling and Directed Evolution of Transketolase

Thiamine diphosphate-dependent enzymes, and specifically transketolases, form one of the most important families of biocatalytic tools for enantioselective carbon-carbon bond formation yielding various hydroxyketones of biological interest. To enable substrate profiling of transketolases for acceptance of different donors and acceptors, a simple, direct colorimetric assay based on pH reaction variation was developed to establish a high-throughput solid-phase assay. This assay reduces the screening effort in the directed evolution of transketolases, as only active variants are selected for further analysis. Transketolase activity is detected as bicarbonate anions released from the α-ketoacid donor substrate, which causes the pH to rise. A pH indicator, bromothymol blue, which changes color from yellow to blue in alkaline conditions, was used to directly detect, with the naked eye, clones expressing active transketolase variants, obviating enzyme extraction.

Improving the Cβ Stereoselectivity of l-Threonine Aldolase for the Synthesis of l-threo-4-Methylsulfonylphenylserine by Modulating the Substrate-Binding Pocket To Control the Orientation of the Substrate Entrance

l-Threonine aldolase from Actinocorallia herbida (AhLTA) is an ideal catalyst for producing l-threo-4-methylsulfonylphenylserine [(2S,3R)-1 b], a key chiral precursor for florfenicol and thiamphenicol. The moderate C_β stereoselectivity is the main obstacle to the industrial application of AhLTA. To address this issue, a combinatorial active-site saturation test (CAST) together with sequence conservatism analysis was applied to engineer the AhLTA toward improved C_β stereoselectivity. The optical mutant Y314R could asymmetrically synthesize l-threo-4-methylsulfonylphenylserine with 81 % diastereomeric excess (de), which is 23 % higher than wild-type AhLTA. Molecular dynamic (MD) simulations revealed that the mechanism for the improvement in C_β stereoselectivity of Y314R is due to the acylamino group of residues Arg314 controlling the orientation of substrate 4-methylsulfonyl benzaldehyde (1 a) in the active pocket by directed interaction with the methylsulfonyl group; this leads to asymmetric synthesis of l-threo-4-methylsulfonylphenylserine. The success in this study demonstrates that direct control of substrates in an active pocket is an attract strategy to address the C_β stereoselectivity problem of LTA and contribute to the industrial application of LTA.

Enhanced catalytic efficiency and coenzyme affinity of leucine dehydrogenase by comprehensive screening strategy for L-tert-leucine synthesis

L-tert-leucine (L-Tle) is widely used as vital chiral intermediate for pharmaceuticals and as chiral auxiliarie for organocatalysis. L-Tle is generally prepared via the asymmetric reduction of trimethylpyruvate (TMP) catalyzed by NAD⁺-dependent leucine dehydrogenase (LeuDH). To improve the catalytic efficiency and coenzyme affinity of LeuDH from Bacillus cereus, mutation libraries constructed by error-prone PCR and iterative saturation mutation were screened by two kinds of high-throughput methods. Compared with the wild type, the affinity of the selected mutant E24V/E116V for TMP and NADH increased by 7.7- and 2.8-fold, respectively. And the k_cat/K_m of E24V/E116V on TMP was 5.4-fold higher than that of the wild type. A coupled reaction comprising LeuDH with glucose dehydrogenase of Bacillus amyloliquefaciens resulted in substrate inhibition at high TMP concentrations (0.5 M), which was overcome by batch-feeding of the TMP substrate. The total turnover number and specific space-time conversion of 0.57 M substrate increased to 11,400 and 22.8 mmol·h^-1·L^-1·g^-1, respectively. KEY POINTS: • The constructed new high-throughput screening strategy takes into account the two indicators of catalytic efficiency and coenzyme affinity. • A more efficient leucine dehydrogenase (LeuDH) mutant (E24V/E116V) was identified. • E24V/E116V has potential for the industrial synthesis of L-tert-leucine.

A synthetic RNA-based biosensor for fructose-1,6-bisphosphate that reports glycolytic flux

RNA-based sensors for intracellular metabolites are a promising solution to the emerging issue of metabolic heterogeneity. However, their development, i.e., the conversion of an aptamer into an in vivo-functional intracellular metabolite sensor, still harbors challenges. Here, we accomplished this for the glycolytic flux-signaling metabolite, fructose-1,6-bisphosphate (FBP). Starting from in vitro selection of an aptamer, we constructed device libraries with a hammerhead ribozyme as actuator. Using high-throughput screening in yeast with fluorescence-activated cell sorting (FACS), next-generation sequencing, and genetic-environmental perturbations to modulate the intracellular FBP levels, we identified a sensor that generates ratiometric fluorescent readout. An abrogated response in sensor mutants and occurrence of two sensor conformations-revealed by RNA structural probing-indicated in vivo riboswitching activity. Microscopy showed that the sensor can differentiate cells with different glycolytic fluxes within yeast populations, opening research avenues into metabolic heterogeneity. We demonstrate the possibility to generate RNA-based sensors for intracellular metabolites for which no natural metabolite-binding RNA element exits.

Engineered pro-peptide enhances the catalytic activity of keratinase to improve the conversion ability of feather waste

Keratinase is an attractive industrial enzyme that can specifically catalyze keratin waste to obtain value-added products. A challenge to the application of keratinase is improving catalytic capacity to achieve efficient hydrolysis. In this study, we effectively expressed the keratinase gene from Bacillus licheniformis BBE11-1 in Bacillus subtilis WB600 based on pro-peptide engineering. Partial deletion of the pro-peptide sequence and the substitution of amino acid at the pro-peptide cleavage site (P1) suggested that the "chaperone effect" and "cleavage efficiency" of the pro-peptide determine the activity of the mature enzyme. Subsequently, seven target sites that can increase the activity of the mature enzyme by 16%-66% were obtained through the multiple sequence alignment of pro-peptides and site-directed mutation. We further performed combinatorial mutations at six sites based on the design principle of three-codon saturation mutations and obtained mutant 2-D12 (236.8 KU/mg) with a mature enzyme activity of 186% of the original (127.6 KU/mg). Finally, continuous fermentation was carried out in a 5-L bioreactor for 22 h, and the activity of the 2-D12 mature enzyme was increased to 391.6 KU/mg. Most importantly, 2-D12 could degrade more than 90% of feather waste into amino acids and peptides within 12 h with the aid of sulfite.