Systematic engineering of artificial metalloenzymes for new-to-nature reactions

Learning from Protein Engineering by Deconvolution of Multi-Mutational Variants

This review analyzes a development in biochemistry, enzymology and biotechnology that originally came as a surprise. Following the establishment of directed evolution of stereoselective enzymes in organic chemistry, the concept of partial or complete deconvolution of selective multi-mutational variants was introduced. Early deconvolution experiments of stereoselective variants led to the finding that mutations can interact cooperatively or antagonistically with one another, not just additively. During the past decade, this phenomenon was shown to be general. In some studies, molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) computations were performed in order to shed light on the origin of non-additivity at all stages of an evolutionary upward climb. Data of complete deconvolution can be used to construct unique multi-dimensional rugged fitness pathway landscapes, which provide mechanistic insights different from traditional fitness landscapes. Along a related line, biochemists have long tested the result of introducing two point mutations in an enzyme for mechanistic reasons, followed by a comparison of the respective double mutant in so-called double mutant cycles, which originally showed only additive effects, but more recently also uncovered cooperative and antagonistic non-additive effects. We conclude with suggestions for future work, and call for a unified overall picture of non-additivity and epistasis.

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.

Albumin-ruthenium catalyst conjugate for bio-orthogonal uncaging of alloc group

The employment of antibodies as a targeted drug delivery vehicle has proven successful which is exemplified by the emergence of antibody-drug conjugates (ADCs). However, ADCs are not without their shortcomings. Improvements may be made to the ADC platform by decoupling the cytotoxic drug from the delivery vehicle and conjugating an organometallic catalyst in its place. The resulting protein-metal catalyst conjugate was designed to uncage the masked cytotoxin administered as a separate entity. Macropinocytosis of albumin by cancerous cells suggests the potential of albumin acting as the tumor-targeting delivery vehicle. Herein reported are the first preparation and demonstration of ruthenium catalysts with cyclopentadienyl and quinoline-based ligands conjugated to albumin. The effective uncaging abilities were demonstrated on allyloxy carbamate (alloc)-protected rhodamine 110 and doxorubicin, providing a promising catalytic scaffold for the advancement of selective drug delivery methods in the future.

Creating new-to-nature carbon fixation: A guide

Synthetic biology aims at designing new biological functions from first principles. These new designs allow to expand the natural solution space and overcome the limitations of naturally evolved systems. One example is synthetic CO2-fixation pathways that promise to provide more efficient ways for the capture and conversion of CO2 than natural pathways, such as the Calvin Benson Bassham (CBB) cycle of photosynthesis. In this review, we provide a practical guideline for the design and realization of such new-to-nature CO2-fixation pathways. We introduce the concept of "synthetic CO2-fixation", and give a general overview over the enzymology and topology of synthetic pathways, before we derive general principles for their design from their eight naturally evolved analogs. We provide a comprehensive summary of synthetic carbon-assimilation pathways and derive a step-by-step, practical guide from the theoretical design to their practical implementation, before ending with an outlook on new developments in the field.

Effective engineering of a ketoreductase for the biocatalytic synthesis of an ipatasertib precursor

Semi-rational enzyme engineering is a powerful method to develop industrial biocatalysts. Profiting from advances in molecular biology and bioinformatics, semi-rational approaches can effectively accelerate enzyme engineering campaigns. Here, we present the optimization of a ketoreductase from Sporidiobolus salmonicolor for the chemo-enzymatic synthesis of ipatasertib, a potent protein kinase B inhibitor. Harnessing the power of mutational scanning and structure-guided rational design, we created a 10-amino acid substituted variant exhibiting a 64-fold higher apparent kcat and improved robustness under process conditions compared to the wild-type enzyme. In addition, the benefit of algorithm-aided enzyme engineering was studied to derive correlations in protein sequence-function data, and it was found that the applied Gaussian processes allowed us to reduce enzyme library size. The final scalable and high performing biocatalytic process yielded the alcohol intermediate with ≥ 98% conversion and a diastereomeric excess of 99.7% (R,R-trans) from 100 g L-1 ketone after 30 h. Modelling and kinetic studies shed light on the mechanistic factors governing the improved reaction outcome, with mutations T134V, A238K, M242W and Q245S exerting the most beneficial effect on reduction activity towards the target ketone.

Nanoconstruction on Living Cell Surfaces with Cucurbit[7]uril-Based Supramolecular Polymer Chemistry: Toward Cell-Based Delivery of Bio-Orthogonal Catalytic Systems

Employing living cells as carriers to transport transition metal-based catalysts for target-specific bio-orthogonal catalysis represents a cutting-edge approach in advancing precision biomedical applications. One of the initial hurdles in this endeavor involves effectively attaching the catalysts to the carrier cells while preserving the cells' innate ability to interact with biological systems and maintaining the unaltered catalytic activity. In this study, we have developed an innovative layer-by-layer method that leverages a noncovalent interaction between cucurbit[7]uril and adamantane as the primary driving force for crafting polymeric nanostructures on the surfaces of these carrier cells. The strong binding affinity between the host-guest pair ensures the creation of a durable polymer coating on the cell surfaces. Meanwhile, the layer-by-layer process offers high adaptability, facilitating the efficient loading of bio-orthogonal catalysts onto cell surfaces. Importantly, the polymeric coating shows no discernible impact on the cells' physiological characteristics, including their tropism, migration, and differentiation, while preserving the effectiveness of the bio-orthogonal catalysts.

Binding Interactions and Inhibition Mechanisms of Gold Complexes in Thiamine Diphosphate-Dependent Enzymes

Thiamine diphosphate (ThDP)-dependent enzymes possess the unique ability to generate a carbene within their active site. In this study, we sought to harness this carbene to produce a Au(I) N-heterocyclic complex directly in the active site of ThDP enzymes, thereby establishing a novel platform for artificial metalloenzymes. Because direct metalation of ThDP proved challenging, we synthesized a ThDP mimic that acts as a competitive inhibitor with a high affinity (Ki = 1.5 μM). Upon metalation with Au(I), we observed that the complex became a more potent inhibitor (Ki = 0.7 μM). However, detailed analysis of the inhibition mode, native mass spectrometry, and size exclusion experiments revealed that the complex does not bind specifically to the active site of ThDP enzymes. Instead, it exhibits unspecific binding and exceeds the 1:1 stoichiometry. Similar binding patterns were observed for other Au(I) species. These findings prompt an important question regarding the inherent propensity of ThDP enzymes to bind strongly to Au. If this phenomenon holds true, it could pave the way for the development of Au-based drugs targeting these enzymes.

Artificial Metalloenzyme-Catalyzed Enantioselective Amidation via Nitrene Insertion in Unactivated C(sp3)-H Bonds

Enantioselective C-H amidation offers attractive means to assemble C-N bonds to synthesize high-added value, nitrogen-containing molecules. In recent decades, complementary enzymatic and homogeneous-catalytic strategies for C-H amidation have been reported. Herein, we report on an artificial metalloenzyme (ArM) resulting from anchoring a biotinylated Ir-complex within streptavidin (Sav). The resulting ArM catalyzes the enantioselective amidation of unactivated C(sp³)-H bonds. Chemogenetic optimization of the Ir cofactor and Sav led to significant improvement in both the activity and enantioselectivity. Up to >700 TON and 92% ee for the amidation of unactivated C(sp³)-H bonds was achieved. The single crystal X-ray analysis of the artificial nitrene insertase (ANIase) combined with quantum mechanics-molecular mechanics (QM-MM) calculations sheds light on critical second coordination sphere contacts leading to improved catalytic performance.

Cell Extracts from Bacteria and Yeast Retain Metabolic Activity after Extended Storage and Repeated Thawing

Cell-free synthetic biology enables rapid prototyping of biological parts and synthesis of proteins or metabolites in the absence of cell growth constraints. Cell-free systems are frequently made from crude cell extracts, where composition and activity can vary significantly based on source strain, preparation and processing, reagents, and other considerations. This variability can cause extracts to be treated as black boxes for which empirical observations guide practical laboratory practices, including a hesitance to use dated or previously thawed extracts. To better understand the robustness of cell extracts over time, we assessed the activity of cell-free metabolism during storage. As a model, we studied conversion of glucose to 2,3-butanediol. We found that cell extracts from Escherichia coli and Saccharomyces cerevisiae subjected to an 18-month storage period and repeated freeze-thaw cycles retain consistent metabolic activity. This work gives users of cell-free systems a better understanding of the impacts of storage on extract behavior.

A Dual-Metal-Catalyzed Sequential Cascade Reaction in an Engineered Protein Cage

Herein, we describe the creation of an artificial protein cage housing a dual-metal-tagged guest protein that catalyzes a linear, two-step sequential cascade reaction. The guest protein consists of a fusion protein of HaloTag and monomeric rhizavidin. Inside the protein capsid, we established a ruthenium-catalyzed allylcarbamate deprotection reaction followed by a gold-catalyzed ring-closing hydroamination reaction that led to indoles and phenanthridines with an overall yield of up to 66 % in aqueous solutions. Furthermore, we show that the encapsulation stabilizes the metal catalysts against deactivation by air, proteins and cell lysate.

Ultrahigh-Throughput Screening of an Artificial Metalloenzyme using Double Emulsions

The potential for ultrahigh-throughput compartmentalization renders droplet microfluidics an attractive tool for the directed evolution of enzymes. Importantly, it ensures maintenance of the phenotype-genotype linkage, enabling reliable identification of improved mutants. Herein, we report an approach for ultrahigh-throughput screening of an artificial metalloenzyme in double emulsion droplets (DEs) using commercially available fluorescence-activated cell sorters (FACS). This protocol was validated by screening a 400 double-mutant streptavidin library for ruthenium-catalyzed deallylation of an alloc-protected aminocoumarin. The most active variants, identified by next-generation sequencing, were in good agreement with hits obtained using a 96-well plate procedure. These findings pave the way for the systematic implementation of FACS for the directed evolution of (artificial) enzymes and will significantly expand the accessibility of ultrahigh-throughput DE screening protocols.

MeDBA: the Metalloenzyme Data Bank and Analysis platform

Metalloenzymes are attractive research targets in fields of chemistry, biology, and medicine. Given that metalloenzymes can manifest conservation of metal-coordination and ligand binding modes, the excavation and expansion of metalloenzyme-specific knowledge is of interest in bridging metalloenzyme-related fields. Building on our previous metalloenzyme-ligand association database, MeLAD, we have expanded the scope of metalloenzyme-specific knowledge and services, by forming a versatile platform, termed the Metalloenzyme Data Bank and Analysis (MeDBA). The MeDBA provides: (i) manual curation of metalloenzymes into different categories, that this M-I, M-II and M-III; (ii) comprehensive information on metalloenzyme activities, expression profiles, family and disease links; (iii) structural information on metalloenzymes, in particular metal binding modes; (iv) metalloenzyme substrates and bioactive molecules acting on metalloenzymes; (v) excavated metal-binding pharmacophores and (vi) analysis tools for structure/metal active site comparison and metalloenzyme profiling. The MeDBA is freely available at https://medba.ddtmlab.org.

Designing Artificial Metalloenzymes by Tuning of the Environment beyond the Primary Coordination Sphere

Metalloenzymes catalyze a variety of reactions using a limited number of natural amino acids and metallocofactors. Therefore, the environment beyond the primary coordination sphere must play an important role in both conferring and tuning their phenomenal catalytic properties, enabling active sites with otherwise similar primary coordination environments to perform a diverse array of biological functions. However, since the interactions beyond the primary coordination sphere are numerous and weak, it has been difficult to pinpoint structural features responsible for the tuning of activities of native enzymes. Designing artificial metalloenzymes (ArMs) offers an excellent basis to elucidate the roles of these interactions and to further develop practical biological catalysts. In this review, we highlight how the secondary coordination spheres of ArMs influence metal binding and catalysis, with particular focus on the use of native protein scaffolds as templates for the design of ArMs by either rational design aided by computational modeling, directed evolution, or a combination of both approaches. In describing successes in designing heme, nonheme Fe, and Cu metalloenzymes, heteronuclear metalloenzymes containing heme, and those ArMs containing other metal centers (including those with non-native metal ions and metallocofactors), we have summarized insights gained on how careful controls of the interactions in the secondary coordination sphere, including hydrophobic and hydrogen bonding interactions, allow the generation and tuning of these respective systems to approach, rival, and, in a few cases, exceed those of native enzymes. We have also provided an outlook on the remaining challenges in the field and future directions that will allow for a deeper understanding of the secondary coordination sphere a deeper understanding of the secondary coordintion sphere to be gained, and in turn to guide the design of a broader and more efficient variety of ArMs.

An Artificial Metalloenzyme Based on a Copper Heteroscorpionate Enables sp3 C-H Functionalization via Intramolecular Carbene Insertion

The selective functionalization of sp³ C–H bonds is a versatile tool for the diversification of organic compounds. Combining attractive features of homogeneous and enzymatic catalysts, artificial metalloenzymes offer an ideal means to selectively modify these inert motifs. Herein, we report on a copper(I) heteroscorpionate complex embedded within streptavidin that catalyzes the intramolecular insertion of a carbene into sp³ C–H bonds. Target residues for genetic optimization of the artificial metalloenzyme were identified by quantum mechanics/molecular mechanics simulations. Double-saturation mutagenesis yielded detailed insight on the contribution of individual amino acids on the activity and the selectivity of the artificial metalloenzyme. Mutagenesis at a third position afforded a set of artificial metalloenzymes that catalyze the enantio- and regioselective formation of β- and γ-lactams with high turnovers and promising enantioselectivities.

Machine Learning Approaches for Metalloproteins

Metalloproteins are a family of proteins characterized by metal ion binding, whereby the presence of these ions confers key catalytic and ligand-binding properties. Due to their ubiquity among biological systems, researchers have made immense efforts to predict the structural and functional roles of metalloproteins. Ultimately, having a comprehensive understanding of metalloproteins will lead to tangible applications, such as designing potent inhibitors in drug discovery. Recently, there has been an acceleration in the number of studies applying machine learning to predict metalloprotein properties, primarily driven by the advent of more sophisticated machine learning algorithms. This review covers how machine learning tools have consolidated and expanded our comprehension of various aspects of metalloproteins (structure, function, stability, ligand-binding interactions, and inhibitors). Future avenues of exploration are also discussed.

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.

Synthetic prodrug design enables biocatalytic activation in mice to elicit tumor growth suppression

Considering the intrinsic toxicities of transition metals, their incorporation into drug therapies must operate at minimal amounts while ensuring adequate catalytic activity within complex biological systems. As a way to address this issue, this study investigates the design of synthetic prodrugs that are not only tuned to be harmless, but can be robustly transformed in vivo to reach therapeutically relevant levels. To accomplish this, retrosynthetic prodrug design highlights the potential of naphthylcombretastatin-based prodrugs, which form highly active cytostatic agents via sequential ring-closing metathesis and aromatization. Structural adjustments will also be done to improve aspects related to catalytic reactivity, intrinsic bioactivity, and hydrolytic stability. The developed prodrug therapy is found to possess excellent anticancer activities in cell-based assays. Furthermore, in vivo activation by intravenously administered glycosylated artificial metalloenzymes can also induce significant reduction of implanted tumor growth in mice.

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.

Exporting Metal-Carbene Chemistry to Live Mammalian Cells: Copper-Catalyzed Intracellular Synthesis of Quinoxalines Enabled by N-H Carbene Insertions

Implementing catalytic organometallic transformations in living settings can offer unprecedented opportunities in chemical biology and medicine. Unfortunately, the number of biocompatible reactions so far discovered is very limited, and essentially restricted to uncaging processes. Here, we demonstrate the viability of performing metal carbene transfer reactions in live mammalian cells. In particular, we show that copper (II) catalysts can promote the intracellular annulation of alpha-keto diazocarbenes with ortho-amino arylamines, in a process that is initiated by an N-H carbene insertion. The potential of this transformation is underscored by the in cellulo synthesis of a product that alters mitochondrial functions, and by demonstrating cell selective biological responses using targeted copper catalysts. Considering the wide reactivity spectrum of metal carbenes, this work opens the door to significantly expanding the repertoire of life-compatible abiotic reactions.

Directed Evolution of a Surface-Displayed Artificial Allylic Deallylase Relying on a GFP Reporter Protein

Artificial metalloenzymes (ArMs) combine characteristics of both homogeneous catalysts and enzymes. Merging abiotic and biotic features allows for the implementation of new-to-nature reactions in living organisms. Here, we present the directed evolution of an artificial metalloenzyme based on Escherichia coli surface-displayed streptavidin (SavSD hereafter). Through the binding of a ruthenium-pianostool cofactor to SavSD, an artificial allylic deallylase (ADAse hereafter) is assembled, which displays catalytic activity toward the deprotection of alloc-protected 3-hydroxyaniline. The uncaged aminophenol acts as a gene switch and triggers the overexpression of a fluorescent green fluorescent protein (GFP) reporter protein. This straightforward readout of ADAse activity allowed the simultaneous saturation mutagenesis of two amino acid residues in Sav near the ruthenium cofactor, expediting the screening of 2762 individual clones. A 1.7-fold increase of in vivo activity was observed for SavSD S112T-K121G compared to the wild-type SavSD (wt-SavSD). Finally, the best performing Sav isoforms were purified and tested in vitro (SavPP hereafter). For SavPP S112M-K121A, a total turnover number of 372 was achieved, corresponding to a 5.9-fold increase vs wt-SavPP. To analyze the marked difference in activity observed between the surface-displayed and purified ArMs, the oligomeric state of SavSD was determined. For this purpose, crosslinking experiments of E. coli cells overexpressing SavSD were carried out, followed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and Western blot. The data suggest that SavSD is most likely displayed as a monomer on the surface of E. coli. We hypothesize that the difference between the in vivo and in vitro screening results may reflect the difference in the oligomeric state of SavSD vs soluble SavPP (monomeric vs tetrameric). Accordingly, care should be applied when evolving oligomeric proteins using E. coli surface display.