Computational redesign of a mononuclear zinc metalloenzyme for organophosphate hydrolysis

Directed Evolution of Protein Catalysts

Directed evolution is a powerful technique for generating tailor-made enzymes for a wide range of biocatalytic applications. Following the principles of natural evolution, iterative cycles of mutagenesis and screening or selection are applied to modify protein properties, enhance catalytic activities, or develop completely new protein catalysts for non-natural chemical transformations. This review briefly surveys the experimental methods used to generate genetic diversity and screen or select for improved enzyme variants. Emphasis is placed on a key challenge, namely how to generate novel catalytic activities that expand the scope of natural reactions. Two particularly effective strategies, exploiting catalytic promiscuity and rational design, are illustrated by representative examples of successfully evolved enzymes. Opportunities for extending these approaches to more complex biocatalytic systems are also considered.

Expanding the boundary of biocatalysis: design and optimization of in vitro tandem catalytic reactions for biochemical production

Biocatalysts have been increasingly used in the synthesis of fine chemicals and medicinal compounds due to significant advances in enzyme discovery and engineering. To mimic the synergistic effects of cascade reactions catalyzed by multiple enzymes in nature, researchers have been developing artificial tandem enzymatic reactions in vivo by harnessing synthetic biology and metabolic engineering tools. There is also growing interest in the development of one-pot tandem enzymatic or chemo-enzymatic processes in vitro due to their neat and concise catalytic systems and product purification procedures. In this review, we will briefly summarize the strategies of designing and optimizing in vitro tandem catalytic reactions, highlight a few representative examples, and discuss the future trend in this field.

Predictive methods for computational metalloenzyme redesign - a test case with carboxypeptidase A

Computational metalloenzyme design is a multi-scale problem. It requires treating the metal coordination quantum mechanically, extensive sampling of the protein backbone, and additionally accounting for the polarization of the active site by both the metal cation and the surrounding protein (a phenomenon called electrostatic preorganization). We bring together a combination of theoretical methods that jointly offer these desired qualities: QM/DMD for mixed quantum-classical dynamic sampling, quantum theory of atoms in molecules (QTAIM) for the assessment of electrostatic preorganization, and Density Functional Theory (DFT) for mechanistic studies. Within this suite of principally different methods, there are both complementarity of capabilities and cross-validation. Using these methods, predictions can be made regarding the relative activities of related enzymes, as we show on the native Zn²⁺-dependent carboxypeptidase A (CPA), and its mutant proteins, which are hypothesized to hydrolyze modified substrates. For the native CPA, we replicated the catalytic mechanism and the rate in close agreement with the experiment, giving validity to the QM/DMD predicted structure, the DFT mechanism, and the QTAIM assessment of catalytic activity. For most sequences of the modified substrate and tried CPA mutants, substantially worsened activity is predicted. However, for the substrate mutant that contains Asp instead of Phe at the C-terminus, one CPA mutant exhibits a reasonable activity, as predicted across the theoretical methods. CPA is a well-studied system, and here it serves as a testing ground for the offered methods.

Molecular Evidence of Adenosine Deaminase Linking Adenosine A2A Receptor and CD26 Proteins

Adenosine is an endogenous purine nucleoside that acts in all living systems as a homeostatic network regulator through many pathways, which are adenosine receptor (AR)-dependent and -independent. From a metabolic point of view, adenosine deaminase (ADA) is an essential protein in the regulation of the total intracellular and extracellular adenosine in a tissue. In addition to its cytosolic localization, ADA is also expressed as an ecto-enzyme on the surface of different cells. Dipeptidyl peptidase IV (CD26) and some ARs act as binding proteins for extracellular ADA in humans. Since CD26 and ARs interact with ADA at opposite sites, we have investigated if ADA can function as a cell-to-cell communication molecule by bridging the anchoring molecules CD26 and A2AR present on the surfaces of the interacting cells. By combining site-directed mutagenesis of ADA amino acids involved in binding to A2AR and a modification of the bioluminescence resonance energy transfer (BRET) technique that allows detection of interactions between two proteins expressed in different cell populations with low steric hindrance (NanoBRET), we show direct evidence of the specific formation of trimeric complexes CD26-ADA-A2AR involving two cells. By dynamic mass redistribution assays and ligand binding experiments, we also demonstrate that A2AR-NanoLuc fusion proteins are functional. The existence of this ternary complex is in good agreement with the hypothesis that ADA could bridge T-cells (expressing CD26) and dendritic cells (expressing A2AR). This is a new metabolic function for ecto-ADA that, being a single chain protein, it has been considered as an example of moonlighting protein, because it performs more than one functional role (as a catalyst, a costimulator, an allosteric modulator and a cell-to-cell connector) without partitioning these functions in different subunits.

Speeding up enzyme discovery and engineering with ultrahigh-throughput methods

Exploring the sequence space of enzyme catalysts is ultimately a numbers game. Ultrahigh-throughput screening methods for rapid analysis of millions of variants are therefore increasingly important for investigating sequence-function relationships, searching large metagenomic libraries for interesting activities, and accelerating enzyme evolution in the laboratory. Recent applications of such technologies are reviewed here, with a particular focus on the practical benefits of droplet-based microfluidics for the directed evolution of natural and artificial enzymes. Broader implementation of such rapid, cost-effective screening technologies is likely to redefine the way enzymes are studied and engineered for academic and industrial purposes.

Similar Active Sites and Mechanisms Do Not Lead to Cross-Promiscuity in Organophosphate Hydrolysis: Implications for Biotherapeutic Engineering

Organophosphate hydrolases are proficient catalysts of the breakdown of neurotoxic organophosphates and have great potential as both biotherapeutics for treating acute organophosphate toxicity and as bioremediation agents. However, proficient organophosphatases such as serum paraoxonase 1 (PON1) and the organophosphate-hydrolyzing lactonase SsoPox are unable to hydrolyze bulkyorganophosphates with challenging leaving groups such as diisopropyl fluorophosphate (DFP) or venomous agent X, creating a major challenge for enzyme design. Curiously, despite their mutually exclusive substrate specificities, PON1 and diisopropyl fluorophosphatase (DFPase) have essentially identical active sites and tertiary structures. In the present work, we use empirical valence bond simulations to probe the catalytic mechanism of DFPase as well as temperature, pH, and mutational effects, demonstrating that DFPase and PON1 also likely utilize identical catalytic mechanisms to hydrolyze their respective substrates. However, detailed examination of both static structures and dynamical simulations demonstrates subtle but significant differences in the electrostatic properties and solvent penetration of the two active sites and, most critically, the role of residues that make no direct contact with either substrate in acting as "specificity switches" between the two enzymes. Specifically, we demonstrate that key residues that are structurally and functionally critical for the paraoxonase activity of PON1 prevent it from being able to hydrolyze DFP with its fluoride leaving group. These insights expand our understanding of the drivers of the evolution of divergent substrate specificity in enzymes with identical active sites and guide the future design of organophosphate hydrolases that hydrolyze compounds with challenging leaving groups.

A review of computational tools for design and reconstruction of metabolic pathways

Metabolic pathways reflect an organism's chemical repertoire and hence their elucidation and design have been a primary goal in metabolic engineering. Various computational methods have been developed to design novel metabolic pathways while taking into account several prerequisites such as pathway stoichiometry, thermodynamics, host compatibility, and enzyme availability. The choice of the method is often determined by the nature of the metabolites of interest and preferred host organism, along with computational complexity and availability of software tools. In this paper, we review different computational approaches used to design metabolic pathways based on the reaction network representation of the database (i.e., graph or stoichiometric matrix) and the search algorithm (i.e., graph search, flux balance analysis, or retrosynthetic search). We also put forth a systematic workflow that can be implemented in projects requiring pathway design and highlight current limitations and obstacles in computational pathway design.

Importance of Scaffold Flexibility/Rigidity in the Design and Directed Evolution of Artificial Metallo-β-lactamases

We describe the design and evolution of catalytic hydrolase activity on a supramolecular protein scaffold, Zn₄:^C96RIDC1₄, which was constructed from cytochrome cb₅₆₂ building blocks via a metal-templating strategy. Previously, we reported that Zn₄:^C96RIDC1₄ could be tailored with tripodal (His/His/Glu), unsaturated Zn coordination motifs in its interfaces to generate a variant termed Zn₈:^A104AB3₄, which in turn displayed catalytic activity for the hydrolysis of activated esters and β-lactam antibiotics. Zn₈:^A104AB3₄ was subsequently subjected to directed evolution via an in vivo selection strategy, leading to a variant Zn₈:^A104/G57AB3₄ which displayed enzyme-like Michaelis-Menten behavior for ampicillin hydrolysis. A criterion for the evolutionary utility or designability of a new protein structure is its ability to accommodate different active sites. With this in mind, we examined whether Zn₄:^C96RIDC1₄ could be tailored with alternative Zn coordination sites that could similarly display evolvable catalytic activities. We report here a detailed structural and functional characterization of new variant Zn₈:AB5₄, which houses similar, unsaturated Zn coordination sites to those in Zn₈:^A104/G57AB3₄, but in completely different microenvironments. Zn₈:AB5₄ displays Michaelis-Menten behavior for ampicillin hydrolysis without any optimization. Yet, the subsequent directed evolution of Zn₈:AB5₄ revealed limited catalytic improvement, which we ascribed to the local protein rigidity surrounding the Zn centers and the lack of evolvable loop structures nearby. The relaxation of local rigidity via the elimination of adjacent disulfide linkages led to a considerable structural transformation with a concomitant improvement in β-lactamase activity. Our findings reaffirm previous observations that the delicate balance between protein flexibility and stability is crucial for enzyme design and evolution.

Artificial Metalloenzymes on the Verge of New-to-Nature Metabolism

Residing at the interface of chemistry and biotechnology, artificial metalloenzymes (ArMs) offer an attractive technology to combine the versatile reaction repertoire of transition metal catalysts with the exquisite catalytic features of enzymes. While earlier efforts in this field predominantly comprised studies in well-defined test-tube environments, a trend towards exploiting ArMs in more complex environments has recently emerged. Integration of these artificial biocatalysts in enzymatic cascades and using them in whole-cell biotransformations and in vivo opens up entirely novel prospects for both preparative chemistry and synthetic biology. We highlight selected recent developments with a particular focus on challenges and opportunities in the in vivo application of ArMs.

Efficient Degradation of Malathion in the Presence of Detergents Using an Engineered Organophosphorus Hydrolase Highly Expressed by Pichia pastoris without Methanol Induction

The biodegradation of pesticides by organophosphorus hydrolases (OPHs) requires an efficient enzyme production technology in industry. Herein, a Pichia pastoris strain was constructed for the extracellular expression of PoOPH_M9, an engineered malathion-degrading enzyme. After optimization, the maximum titer and yield of fermentation reached 50.8 kU/L and 4.1 g_protein/L after 3 days, with the highest space-time yield (STY) reported so far, 640 U L^-1 h^-1. PoOPH_M9 displayed its high activity and stability in the presence of 0.1% (w/w) plant-derived detergent. Only 0.04 mg/mL enzyme could completely remove 0.15 mM malathion in aqueous solution within 20 min. Furthermore, 12 μmol malathion on apples and cucumbers surfaces was completely removed by 0.05 mg/mL PoOPH_M9 in tap water after 35 min washing. The efficient production of the highly active PoOPH_M9 has cleared a major barrier to biodegradation of pesticide residues in food industry.

Enzyme engineering: reaching the maximal catalytic efficiency peak

The practical need for highly efficient enzymes presents new challenges in enzyme engineering, in particular, the need to improve catalytic turnover (k_cat) or efficiency (k_cat/K_M) by several orders of magnitude. However, optimizing catalysis demands navigation through complex and rugged fitness landscapes, with optimization trajectories often leading to strong diminishing returns and dead-ends. When no further improvements are observed in library screens or selections, it remains unclear whether the maximal catalytic efficiency of the enzyme (the catalytic 'fitness peak') has been reached; or perhaps, an alternative combination of mutations exists that could yield additional improvements. Here, we discuss fundamental aspects of the process of catalytic optimization, and offer practical solutions with respect to overcoming optimization plateaus.

Design of an enantioselective artificial metallo-hydratase enzyme containing an unnatural metal-binding amino acid

The design of artificial metalloenzymes is a challenging, yet ultimately highly rewarding objective because of the potential for accessing new-to-nature reactions. One of the main challenges is identifying catalytically active substrate-metal cofactor-host geometries. The advent of expanded genetic code methods for the in vivo incorporation of non-canonical metal-binding amino acids into proteins allow to address an important aspect of this challenge: the creation of a stable, well-defined metal-binding site. Here, we report a designed artificial metallohydratase, based on the transcriptional repressor lactococcal multidrug resistance regulator (LmrR), in which the non-canonical amino acid (2,2'-bipyridin-5yl)alanine is used to bind the catalytic Cu(ii) ion. Starting from a set of empirical pre-conditions, a combination of cluster model calculations (QM), protein-ligand docking and molecular dynamics simulations was used to propose metallohydratase variants, that were experimentally verified. The agreement observed between the computationally predicted and experimentally observed catalysis results demonstrates the power of the artificial metalloenzyme design approach presented here.

Artificial Metalloenzymes: Reaction Scope and Optimization Strategies

The incorporation of a synthetic, catalytically competent metallocofactor into a protein scaffold to generate an artificial metalloenzyme (ArM) has been explored since the late 1970's. Progress in the ensuing years was limited by the tools available for both organometallic synthesis and protein engineering. Advances in both of these areas, combined with increased appreciation of the potential benefits of combining attractive features of both homogeneous catalysis and enzymatic catalysis, led to a resurgence of interest in ArMs starting in the early 2000's. Perhaps the most intriguing of potential ArM properties is their ability to endow homogeneous catalysts with a genetic memory. Indeed, incorporating a homogeneous catalyst into a genetically encoded scaffold offers the opportunity to improve ArM performance by directed evolution. This capability could, in turn, lead to improvements in ArM efficiency similar to those obtained for natural enzymes, providing systems suitable for practical applications and greater insight into the role of second coordination sphere interactions in organometallic catalysis. Since its renaissance in the early 2000's, different aspects of artificial metalloenzymes have been extensively reviewed and highlighted. Our intent is to provide a comprehensive overview of all work in the field up to December 2016, organized according to reaction class. Because of the wide range of non-natural reactions catalyzed by ArMs, this was done using a functional-group transformation classification. The review begins with a summary of the proteins and the anchoring strategies used to date for the creation of ArMs, followed by a historical perspective. Then follows a summary of the reactions catalyzed by ArMs and a concluding critical outlook. This analysis allows for comparison of similar reactions catalyzed by ArMs constructed using different metallocofactor anchoring strategies, cofactors, protein scaffolds, and mutagenesis strategies. These data will be used to construct a searchable Web site on ArMs that will be updated regularly by the authors.

Toward high-resolution computational design of the structure and function of helical membrane proteins

The computational design of α-helical membrane proteins is still in its infancy but has already made great progress. De novo design allows stable, specific and active minimal oligomeric systems to be obtained. Computational reengineering can improve the stability and function of naturally occurring membrane proteins. Currently, the major hurdle for the field is the experimental characterization of the designs. The emergence of new structural methods for membrane proteins will accelerate progress.

Computational Redesign of Acyl-ACP Thioesterase with Improved Selectivity toward Medium-Chain-Length Fatty Acids

Enzyme and metabolic engineering offer the potential to develop biocatalysts for converting natural resources into a wide range of chemicals. To broaden the scope of potential products beyond natural metabolites, methods of engineering enzymes to accept alternative substrates and/or perform novel chemistries must be developed. DNA synthesis can create large libraries of enzyme-coding sequences, but most biochemistries lack a simple assay to screen for promising enzyme variants. Our solution to this challenge is structure-guided mutagenesis in which optimization algorithms select the best sequences from libraries based on specified criteria (i.e. binding selectivity). Here, we demonstrate this approach by identifying medium-chain (C₆-C₁₂) acyl-ACP thioesterases through structure-guided mutagenesis. Medium-chain fatty acids, products of thioesterase-catalyzed hydrolysis, are limited in natural abundance compared to long-chain fatty acids; the limited supply leads to high costs of C₆-C₁₀ oleochemicals such as fatty alcohols, amines, and esters. Here, we applied computational tools to tune substrate binding to the highly-active 'TesA thioesterase in Escherichia coli. We used the IPRO algorithm to design thioesterase variants with enhanced C₁₂- or C₈-specificity while maintaining high activity. After four rounds of structure-guided mutagenesis, we identified three thioesterases with enhanced production of dodecanoic acid (C₁₂) and twenty-seven thioesterases with enhanced production of octanoic acid (C₈). The top variants reached up to 49% C₁₂ and 50% C₈ while exceeding native levels of total free fatty acids. A comparably sized library created by random mutagenesis failed to identify promising mutants. The chain length-preference of 'TesA and the best mutant were confirmed in vitro using acyl-CoA substrates. Molecular dynamics simulations, confirmed by resolved crystal structures, of 'TesA variants suggest that hydrophobic forces govern 'TesA substrate specificity. We expect that the design rules we uncovered and the thioesterase variants identified will be useful to metabolic engineering projects aimed at sustainable production of medium-chain oleochemicals.

Repurposing proteins for new bioinorganic functions

Inspired by the remarkable sophistication and complexity of natural metalloproteins, the field of protein design and engineering has traditionally sought to understand and recapitulate the design principles that underlie the interplay between metals and protein scaffolds. Yet, some recent efforts in the field demonstrate that it is possible to create new metalloproteins with structural, functional and physico-chemical properties that transcend evolutionary boundaries. This essay aims to highlight some of these efforts and draw attention to the ever-expanding scope of bioinorganic chemistry and its new connections to synthetic biology, biotechnology, supramolecular chemistry and materials engineering.

Achievements and Challenges in Computational Protein Design

Computational protein design (CPD), a yet evolving field, includes computer-aided engineering for partial or full de novo designs of proteins of interest. Designs are defined by a requested structure, function, or working environment. This chapter describes the birth and maturation of the field by presenting 101 CPD examples in a chronological order emphasizing achievements and pending challenges. Integrating these aspects presents the plethora of CPD approaches with the hope of providing a "CPD 101". These reflect on the broader structural bioinformatics and computational biophysics field and include: (1) integration of knowledge-based and energy-based methods, (2) hierarchical designated approach towards local, regional, and global motifs and the integration of high- and low-resolution design schemes that fit each such region, (3) systematic differential approaches towards different protein regions, (4) identification of key hot-spot residues and the relative effect of remote regions, (5) assessment of shape-complementarity, electrostatics and solvation effects, (6) integration of thermal plasticity and functional dynamics, (7) negative design, (8) systematic integration of experimental approaches, (9) objective cross-assessment of methods, and (10) successful ranking of potential designs. Future challenges also include dissemination of CPD software to the general use of life-sciences researchers and the emphasis of success within an in vivo milieu. CPD increases our understanding of protein structure and function and the relationships between the two along with the application of such know-how for the benefit of mankind. Applied aspects range from biological drugs, via healthier and tastier food products to nanotechnology and environmentally friendly enzymes replacing toxic chemicals utilized in the industry.

Computational design to improve catalytic activity of cephalosporin C acylase from Pseudomonas strain N176

Engineering enzymes with high catalytic activities using enzyme designin silicoand a limited number of experimental evaluations is the new trend for the discovery of highly efficient biocatalysts.

TSTMP: target selection for structural genomics of human transmembrane proteins

The TSTMP database is designed to help the target selection of human transmembrane proteins for structural genomics projects and structure modeling studies. Currently, there are only 60 known 3D structures among the polytopic human transmembrane proteins and about a further 600 could be modeled using existing structures. Although there are a great number of human transmembrane protein structures left to be determined, surprisingly only a small fraction of these proteins have 'selected' (or above) status according to the current version the TargetDB/TargetTrack database. This figure is even worse regarding those transmembrane proteins that would contribute the most to the structural coverage of the human transmembrane proteome. The database was built by sorting out proteins from the human transmembrane proteome with known structure and searching for suitable model structures for the remaining proteins by combining the results of a state-of-the-art transmembrane specific fold recognition algorithm and a sequence similarity search algorithm. Proteins were searched for homologues among the human transmembrane proteins in order to select targets whose successful structure determination would lead to the best structural coverage of the human transmembrane proteome. The pipeline constructed for creating the TSTMP database guarantees to keep the database up-to-date. The database is available at http://tstmp.enzim.ttk.mta.hu.

Design and engineering of artificial oxygen-activating metalloenzymes

Many efforts are being made in the design and engineering of metalloenzymes with catalytic properties fulfilling the needs of practical applications. Progress in this field has recently been accelerated by advances in computational, molecular and structural biology. This review article focuses on the recent examples of oxygen-activating metalloenzymes, developed through the strategies of de novo design, miniaturization processes and protein redesign. Considerable progress in these diverse design approaches has produced many metal-containing biocatalysts able to adopt the functions of native enzymes or even novel functions beyond those found in Nature.

Artificial Metalloenzymes Based on the Biotin-Streptavidin Technology: Challenges and Opportunities

The biotin-streptavidin technology offers an attractive means to engineer artificial metalloenzymes (ArMs). Initiated over 50 years ago by Bayer and Wilchek, the biotin-(strept)avidin techonology relies on the exquisite supramolecular affinity of either avidin or streptavidin for biotin. This versatile tool, commonly referred to as "molecular velcro", allows nearly irreversible anchoring of biotinylated probes within a (strept)avidin host protein. Building upon a visionary publication by Whitesides from 1978, several groups have been exploiting this technology to create artificial metalloenzymes. For this purpose, a biotinylated organometallic catalyst is introduced within (strept)avidin to afford a hybrid catalyst that combines features reminiscent of both enzymes and organometallic catalysts. Importantly, ArMs can be optimized by chemogenetic means. Combining a small collection of biotinylated organometallic catalysts with streptavidin mutants allows generation of significant diversity, thus allowing optimization of the catalytic performance of ArMs. Pursuing this strategy, the following reactions have been implemented: hydrogenation, alcohol oxidation, sulfoxidation, dihydroxylation, allylic alkylation, transfer hydrogenation, Suzuki cross-coupling, C-H activation, and metathesis. In this Account, we summarize our efforts in the latter four reactions. X-ray analysis of various ArMs based on the biotin-streptavidin technology reveals the versatility and commensurability of the biotin-binding vestibule to accommodate and interact with transition states of the scrutinized organometallic transformations. In particular, streptavidin residues at positions 112 and 121 recurrently lie in close proximity to the biotinylated metal cofactor. This observation led us to develop a streamlined 24-well plate streptavidin production and screening platform to optimize the performance of ArMs. To date, most of the efforts in the field of ArMs have focused on the use of purified protein samples. This seriously limits the throughput of the optimization process. With the ultimate goal of complementing natural enzymes in the context of synthetic and chemical biology, we outline the milestones required to ultimately implement ArMs within a cellular environment. Indeed, we believe that ArMs may allow signficant expansion of the natural enzymes' toolbox to access new-to-nature reactivities in vivo. With this ambitious goal in mind, we report on our efforts to (i) activate the biotinylated catalyst precursor upon incorporation within streptavidin, (ii) minimize the effect of the cellular environment on the ArM's performance, and (iii) demonstrate the compatibility of ArMs with natural enzymes in cascade reactions.

Directed evolution of artificial metalloenzymes for in vivo metathesis

The field of biocatalysis has advanced from harnessing natural enzymes to using directed evolution to obtain new biocatalysts with tailor-made functions. Several tools have recently been developed to expand the natural enzymatic repertoire with abiotic reactions. For example, artificial metalloenzymes, which combine the versatile reaction scope of transition metals with the beneficial catalytic features of enzymes, offer an attractive means to engineer new reactions. Three complementary strategies exist: repurposing natural metalloenzymes for abiotic transformations; in silico metalloenzyme (re-)design; and incorporation of abiotic cofactors into proteins. The third strategy offers the opportunity to design a wide variety of artificial metalloenzymes for non-natural reactions. However, many metal cofactors are inhibited by cellular components and therefore require purification of the scaffold protein. This limits the throughput of genetic optimization schemes applied to artificial metalloenzymes and their applicability in vivo to expand natural metabolism. Here we report the compartmentalization and in vivo evolution of an artificial metalloenzyme for olefin metathesis, which represents an archetypal organometallic reaction without equivalent in nature. Building on previous work on an artificial metallohydrolase, we exploit the periplasm of Escherichia coli as a reaction compartment for the 'metathase' because it offers an auspicious environment for artificial metalloenzymes, mainly owing to low concentrations of inhibitors such as glutathione, which has recently been identified as a major inhibitor. This strategy facilitated the assembly of a functional metathase in vivo and its directed evolution with substantially increased throughput compared to conventional approaches that rely on purified protein variants. The evolved metathase compares favourably with commercial catalysts, shows activity for different metathesis substrates and can be further evolved in different directions by adjusting the workflow. Our results represent the systematic implementation and evolution of an artificial metalloenzyme that catalyses an abiotic reaction in vivo, with potential applications in, for example, non-natural metabolism.

Computational design of variants for cephalosporin C acylase from Pseudomonas strain N176 with improved stability and activity

In this report, redesigning cephalosporin C acylase from the Pseudomonas strain N176 revealed that the loss of stability owing to the introduced mutations at the active site can be recovered by repacking the nearby hydrophobic core regions. Starting from a quadruple mutant M31βF/H57βS/V68βA/H70βS, whose decrease in stability is largely owing to the mutation V68βA at the active site, we employed a computational enzyme design strategy that integrated design both at hydrophobic core regions for stability enhancement and at the active site for activity improvement. Single-point mutations L154βF, Y167βF, L180βF and their combinations L154βF/L180βF and L154βF/Y167βF/L180βF were found to display improved stability and activity. The two-point mutant L154βF/L180βF increased the protein melting temperature (T _m) by 11.7 °C and the catalytic efficiency V _max/K _m by 57 % compared with the values of the starting quadruple mutant. The catalytic efficiency of the resulting sixfold mutant M31βF/H57βS/V68βA/H70βS/L154βF/L180βF is recovered to become comparable to that of the triple mutant M31βF/H57βS/H70βS, but with a higher T _m. Further experiments showed that single-point mutations L154βF, L180βF, and their combination contribute no stability enhancement to the triple mutant M31βF/H57βS/H70βS. These results verify that the lost stability because of mutation V68βA at the active site was recovered by introducing mutations L154βF and L180βF at hydrophobic core regions. Importantly, mutation V68βA in the six-residue mutant provides more space to accommodate the bulky side chain of cephalosporin C, which could help in designing cephalosporin C acylase mutants with higher activities and the practical one-step enzymatic route to prepare 7-aminocephalosporanic acid at industrial-scale levels.

Beyond the outer limits of nature by directed evolution

For more than thirty years, biotechnology has borne witness to the power of directed evolution in designing molecules of industrial relevance. While scientists all over the world discuss the future of molecular evolution, dozens of laboratory-designed products are being released with improved characteristics in terms of turnover rates, substrate scope, catalytic promiscuity or stability. In this review we aim to present the most recent advances in this fascinating research field that are allowing us to surpass the limits of nature and apply newly gained attributes to a range of applications, from gene therapy to novel green processes. The use of directed evolution in non-natural environments, the generation of catalytic promiscuity for non-natural reactions, the insertion of unnatural amino acids into proteins or the creation of unnatural DNA, is described comprehensively, together with the potential applications in bioremediation, biomedicine and in the generation of new bionanomaterials. These successful case studies show us that the limits of directed evolution will be defined by our own imagination, and in some cases, stretching beyond that.

Genetic Optimization of Metalloenzymes: Enhancing Enzymes for Non-Natural Reactions

Artificial metalloenzymes have received increasing attention over the last decade as a possible solution to unaddressed challenges in synthetic organic chemistry. Whereas traditional transition-metal catalysts typically only take advantage of the first coordination sphere to control reactivity and selectivity, artificial metalloenzymes can modulate both the first and second coordination spheres. This difference can manifest itself in reactivity profiles that can be truly unique to artificial metalloenzymes. This Review summarizes attempts to modulate the second coordination sphere of artificial metalloenzymes by using genetic modifications of the protein sequence. In doing so, successful attempts and creative solutions to address the challenges encountered are highlighted.

Kinetic Characterization of 100 Glycoside Hydrolase Mutants Enables the Discovery of Structural Features Correlated with Kinetic Constants

The use of computational modeling algorithms to guide the design of novel enzyme catalysts is a rapidly growing field. Force-field based methods have now been used to engineer both enzyme specificity and activity. However, the proportion of designed mutants with the intended function is often less than ten percent. One potential reason for this is that current force-field based approaches are trained on indirect measures of function rather than direct correlation to experimentally-determined functional effects of mutations. We hypothesize that this is partially due to the lack of data sets for which a large panel of enzyme variants has been produced, purified, and kinetically characterized. Here we report the kcat and KM values of 100 purified mutants of a glycoside hydrolase enzyme. We demonstrate the utility of this data set by using machine learning to train a new algorithm that enables prediction of each kinetic parameter based on readily-modeled structural features. The generated dataset and analyses carried out in this study not only provide insight into how this enzyme functions, they also provide a clear path forward for the improvement of computational enzyme redesign algorithms.

Chemomimetic biocatalysis: exploiting the synthetic potential of cofactor-dependent enzymes to create new catalysts

Despite the astonishing breadth of enzymes in nature, no enzymes are known for many of the valuable catalytic transformations discovered by chemists. Recent work in enzyme design and evolution, however, gives us good reason to think that this will change. We describe a chemomimetic biocatalysis approach that draws from small-molecule catalysis and synthetic chemistry, enzymology, and molecular evolution to discover or create enzymes with non-natural reactivities. We illustrate how cofactor-dependent enzymes can be exploited to promote reactions first established with related chemical catalysts. The cofactors can be biological, or they can be non-biological to further expand catalytic possibilities. The ability of enzymes to amplify and precisely control the reactivity of their cofactors together with the ability to optimize non-natural reactivity by directed evolution promises to yield exceptional catalysts for challenging transformations that have no biological counterparts.

Structural principles for computational and de novo design of 4Fe-4S metalloproteins

Iron-sulfur centers in metalloproteins can access multiple oxidation states over a broad range of potentials, allowing them to participate in a variety of electron transfer reactions and serving as catalysts for high-energy redox processes. The nitrogenase FeMoCO cluster converts di-nitrogen to ammonia in an eight-electron transfer step. The 2(Fe4S4) containing bacterial ferredoxin is an evolutionarily ancient metalloprotein fold and is thought to be a primordial progenitor of extant oxidoreductases. Controlling chemical transformations mediated by iron-sulfur centers such as nitrogen fixation, hydrogen production as well as electron transfer reactions involved in photosynthesis are of tremendous importance for sustainable chemistry and energy production initiatives. As such, there is significant interest in the design of iron-sulfur proteins as minimal models to gain fundamental understanding of complex natural systems and as lead-molecules for industrial and energy applications. Herein, we discuss salient structural characteristics of natural iron-sulfur proteins and how they guide principles for design. Model structures of past designs are analyzed in the context of these principles and potential directions for enhanced designs are presented, and new areas of iron-sulfur protein design are proposed. This article is part of a Special issue entitled Biodesign for Bioenergetics--the design and engineering of electronic transfer cofactors, protein networks, edited by Ronald L. Koder and J.L Ross Anderson.

Computational strategies for the design of new enzymatic functions

In this contribution, recent developments in the design of biocatalysts are reviewed with particular emphasis in the de novo strategy. Studies based on three different reactions, Kemp elimination, Diels-Alder and Retro-Aldolase, are used to illustrate different success achieved during the last years. Finally, a section is devoted to the particular case of designed metalloenzymes. As a general conclusion, the interplay between new and more sophisticated engineering protocols and computational methods, based on molecular dynamics simulations with Quantum Mechanics/Molecular Mechanics potentials and fully flexible models, seems to constitute the bed rock for present and future successful design strategies.

Artificial Diiron Enzymes with a De Novo Designed Four-Helix Bundle Structure

A single polypeptide chain may provide an astronomical number of conformers. Nature selected only a trivial number of them through evolution, composing an alphabet of scaffolds, that can afford the complete set of chemical reactions needed to support life. These structural templates are so stable that they allow several mutations without disruption of the global folding, even having the ability to bind several exogenous cofactors. With this perspective, metal cofactors play a crucial role in the regulation and catalysis of several processes. Nature is able to modulate the chemistry of metals, adopting only a few ligands and slightly different geometries. Several scaffolds and metal-binding motifs are representing the focus of intense interest in the literature. This review discusses the widespread four-helix bundle fold, adopted as a scaffold for metal binding sites in the context of de novo protein design to obtain basic biochemical components for biosensing or catalysis. In particular, we describe the rational refinement of structure/function in diiron-oxo protein models from the due ferri (DF) family. The DF proteins were developed by us through an iterative process of design and rigorous characterization, which has allowed a shift from structural to functional models. The examples reported herein demonstrate the importance of the synergic application of de novo design methods as well as spectroscopic and structural characterization to optimize the catalytic performance of artificial enzymes.

Enzyme repurposing of a hydrolase as an emergent peroxidase upon metal binding

Adding a metal cofactor to a protein bearing a latent metal binding site endows the macromolecule with nascent catalytic activity.

As an alternative to Darwinian evolution relying on catalytic promiscuity, a protein may acquire auxiliary function upon metal binding, thus providing it with a novel catalytic machinery. Here we show that addition of cupric ions to a 6-phosphogluconolactonase 6-PGLac bearing a putative metal binding site leads to the emergence of peroxidase activity (k_cat 7.8 × 10^–2 s^–1, K_M 1.1 × 10^–5 M). Both X-ray crystallographic and EPR data of the copper-loaded enzyme Cu·6-PGLac reveal a bis-histidine coordination site, located within a shallow binding pocket capable of accommodating the o-dianisidine substrate.

X-ray crystallographic validation of structure predictions used in computational design for protein stabilization

Protein engineering aimed at enhancing enzyme stability is increasingly supported by computational methods for calculation of mutant folding energies and for the design of disulfide bonds. To examine the accuracy of mutant structure predictions underlying these computational methods, crystal structures of thermostable limonene epoxide hydrolase variants obtained by computational library design were determined. Four different predicted effects indeed contributed to the obtained stabilization: (i) enhanced interactions between a flexible loop close to the N-terminus and the rest of the protein; (ii) improved interactions at the dimer interface; (iii) removal of unsatisfied hydrogen bonding groups; and (iv) introduction of additional positively charged groups at the surface. The structures of an eightfold and an elevenfold mutant showed that most mutations introduced the intended stabilizing interactions, and side-chain conformations were correctly predicted for 72-88% of the point mutations. However, mutations that introduced a disulfide bond in a flexible region had a larger influence on the backbone conformation than predicted. The enzyme active sites were unaltered, in agreement with the observed preservation of catalytic activities. The structures also revealed how a c-Myc tag, which was introduced for facile detection and purification, can reduce access to the active site and thereby lower the catalytic activity. Finally, sequence analysis showed that comprehensive mutant energy calculations discovered stabilizing mutations that are not proposed by the consensus or B-FIT methods.

Valence bond and enzyme catalysis: a time to break down and a time to build up

Understanding enzyme catalysis and developing ability to control of it are two great challenges in biochemistry. A few successful examples of computational-based enzyme design have proved the fantastic potential of computational approaches in this field, however, relatively modest rate enhancements have been reported and the further development of complementary methods is still required. Herein we propose a conceptually simple scheme to identify the specific role that each residue plays in catalysis. The scheme is based on a breakdown of the total catalytic effect into contributions of individual protein residues, which are further decomposed into chemically interpretable components by using valence bond theory. The scheme is shown to shed light on the origin of catalysis in wild-type haloalkane dehalogenase (wt-DhlA) and its mutants. Furthermore, the understanding gained through our scheme is shown to have great potential in facilitating the selection of non-optimal sites for catalysis and suggesting effective mutations to enhance the enzymatic rate.

Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently

The amino acid sequence of a protein affects both its structure and its function. Thus, the ability to modify the sequence, and hence the structure and activity, of individual proteins in a systematic way, opens up many opportunities, both scientifically and (as we focus on here) for exploitation in biocatalysis. Modern methods of synthetic biology, whereby increasingly large sequences of DNA can be synthesised de novo, allow an unprecedented ability to engineer proteins with novel functions. However, the number of possible proteins is far too large to test individually, so we need means for navigating the 'search space' of possible protein sequences efficiently and reliably in order to find desirable activities and other properties. Enzymologists distinguish binding (Kd) and catalytic (kcat) steps. In a similar way, judicious strategies have blended design (for binding, specificity and active site modelling) with the more empirical methods of classical directed evolution (DE) for improving kcat (where natural evolution rarely seeks the highest values), especially with regard to residues distant from the active site and where the functional linkages underpinning enzyme dynamics are both unknown and hard to predict. Epistasis (where the 'best' amino acid at one site depends on that or those at others) is a notable feature of directed evolution. The aim of this review is to highlight some of the approaches that are being developed to allow us to use directed evolution to improve enzyme properties, often dramatically. We note that directed evolution differs in a number of ways from natural evolution, including in particular the available mechanisms and the likely selection pressures. Thus, we stress the opportunities afforded by techniques that enable one to map sequence to (structure and) activity in silico, as an effective means of modelling and exploring protein landscapes. Because known landscapes may be assessed and reasoned about as a whole, simultaneously, this offers opportunities for protein improvement not readily available to natural evolution on rapid timescales. Intelligent landscape navigation, informed by sequence-activity relationships and coupled to the emerging methods of synthetic biology, offers scope for the development of novel biocatalysts that are both highly active and robust.

Combined covalent-electrostatic model of hydrogen bonding improves structure prediction with Rosetta

Interactions between polar atoms are challenging to model because at very short ranges they form hydrogen bonds (H-bonds) that are partially covalent in character and exhibit strong orientation preferences; at longer ranges the orientation preferences are lost, but significant electrostatic interactions between charged and partially charged atoms remain. To simultaneously model these two types of behavior, we refined an orientation dependent model of hydrogen bonds [Kortemme et al. J. Mol. Biol. 2003, 326, 1239] used by the molecular modeling program Rosetta and then combined it with a distance-dependent Coulomb model of electrostatics. The functional form of the H-bond potential is physically motivated and parameters are fit so that H-bond geometries that Rosetta generates closely resemble H-bond geometries in high-resolution crystal structures. The combined potentials improve performance in a variety of scientific benchmarks including decoy discrimination, side chain prediction, and native sequence recovery in protein design simulations and establishes a new standard energy function for Rosetta.