A redox-mediated Kemp eliminase

In situ enzymatic control of colloidal phoresis and catalysis through hydrolysis of ATP

The ability to sense chemical gradients and respond with directional motility and chemical activity is a defining feature of complex living systems. There is a strong interest among scientists to design synthetic systems that emulate these properties. Here, we realize and control such behaviors in a synthetic system by tailoring multivalent interactions of adenosine nucleotides with catalytic microbeads. We first show that multivalent interactions of the bead with gradients of adenosine mono-, di- and trinucleotides (AM/D/TP) control both the phoretic motion and a proton-transfer catalytic reaction, and find that both effects are diminished greatly with increasing valence of phosphates. We exploit this behavior by using enzymatic hydrolysis of ATP to AMP, which downregulates multivalent interactivity in situ. This produces a sudden increase in transport of the catalytic microbeads (a phoretic jump), which is accompanied by increased catalytic activity. Finally, we show how this enzymatic activity can be systematically tuned, leading to simultaneous in situ spatial and temporal control of the location of the microbeads, as well as the products of the reaction that they catalyze. These findings open up new avenues for utilizing multivalent interaction-mediated programming of complex chemo-mechanical behaviors into active systems.

Substrate Conformational Switch Enables the Stereoselective Dimerization in P450 NascB: Insights from Molecular Dynamics Simulations and Quantum Mechanical/Molecular Mechanical Calculations

P450 NascB catalyzes the coupling of cyclo-(l-tryptophan-l-proline) (1) to generate (-)-naseseazine C (2) through intramolecular C-N bond formation and intermolecular C-C coupling. A thorough understanding of its catalytic mechanism is crucial for the engineering or design of P450-catalyzed C-N dimerization reactions. By employing MD simulations, QM/MM calculations, and enhanced sampling, we assessed various mechanisms from recent works. Our study demonstrates that the most favorable pathway entails the transfer of a hydrogen atom from N7-H to Cpd I. Subsequently, there is a conformational change in the substrate radical, shifting it from the Re-face to the Si-face of N7 in Substrate 1. The Si-face conformation of Substrate 1 is stabilized by the protein environment and the π-π stacking interaction between the indole ring and heme porphyrin. The subsequent intermolecular C3-C6' bond formation between Substrate 1 radical and Substrate 2 occurs via a radical attack mechanism. The conformational switch of the Substrate 1 radical not only lowers the barrier of the intermolecular C3-C6' bond formation but also yields the correct stereoselectivity observed in experiments. In addition, we evaluated the reactivity of the ferric-superoxide species, showing it is not reactive enough to initiate the hydrogen atom abstraction from the indole NH group of the substrate. Our simulation provides a comprehensive mechanistic insight into how the P450 enzyme precisely controls both the intramolecular C-N cyclization and intermolecular C-C coupling. The current findings align with the available experimental data, emphasizing the pivotal role of substrate dynamics in governing P450 catalysis.

Choose Your Own Adventure: A Comprehensive Database of Reactions Catalyzed by Cytochrome P450 BM3 Variants

Cytochrome P450 BM3 monooxygenase is the topic of extensive research as many researchers have evolved this enzyme to generate a variety of products. However, the abundance of information on increasingly diversified variants of P450 BM3 that catalyze a broad array of chemistry is not in a format that enables easy extraction and interpretation. We present a database that categorizes variants by their catalyzed reactions and includes details about substrates to provide reaction context. This database of >1500 P450 BM3 variants is downloadable and machine-readable and includes instructions to maximize ease of gathering information. The database allows rapid identification of commonly reported substitutions, aiding researchers who are unfamiliar with the enzyme in identifying starting points for enzyme engineering. For those actively engaged in engineering P450 BM3, the database, along with this review, provides a powerful and user-friendly platform to understand, predict, and identify the attributes of P450 BM3 variants, encouraging the further engineering of this enzyme.

Electrostatics as a Guiding Principle in Understanding and Designing Enzymes

Enzyme design faces challenges related to the implementation of the basic principles that govern the catalytic activity in natural enzymes. In this work, we revisit basic electrostatic concepts that have been shown to explain the origin of enzymatic efficiency like preorganization and reorganization. Using magnitudes such as the electrostatic potential and the electric field generated by the protein, we explain how these concepts work in different enzymes and how they can be used to rationalize the consequences of point mutations. We also discuss examples of protein design in which electrostatic effects have been implemented. For the near future, molecular simulations, coupled with the use of machine learning methods, can be used to implement electrostatics as a guiding principle for enzyme designs.

Prediction of Thermostability of Enzymes Based on the Amino Acid Index (AAindex) Database and Machine Learning

The combination of wet-lab experimental data on multi-site combinatorial mutations and machine learning is an innovative method in protein engineering. In this study, we used an innovative sequence-activity relationship (innov'SAR) methodology based on novel descriptors and digital signal processing (DSP) to construct a predictive model. In this paper, 21 experimental (R)-selective amine transaminases from Aspergillus terreus (AT-ATA) were used as an input to predict higher thermostability mutants than those predicted using the existing data. We successfully improved the coefficient of determination (R2) of the model from 0.66 to 0.92. In addition, root-mean-squared deviation (RMSD), root-mean-squared fluctuation (RMSF), solvent accessible surface area (SASA), hydrogen bonds, and the radius of gyration were estimated based on molecular dynamics simulations, and the differences between the predicted mutants and the wild-type (WT) were analyzed. The successful application of the innov'SAR algorithm in improving the thermostability of AT-ATA may help in directed evolutionary screening and open up new avenues for protein 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.

QM/MM Modeling Aided Enzyme Engineering in Natural Products Biosynthesis

Natural products and their derivatives are widely used across various industries, particularly pharmaceuticals. Modern engineered biosynthesis provides an alternative way of producing and meeting the growing need for diverse natural products. Natural enzymes, on the other hand, often exhibit unsatisfactory catalytic characteristics and necessitate further enzyme engineering modifications. QM/MM, as a powerful and extensively used computational tool in the field of enzyme catalysis, has been increasingly applied in rational enzyme engineering over the past decade. In this review, we summarize recent advances in QM/MM computational investigation on enzyme catalysis and enzyme engineering for natural product biosynthesis. The challenges and perspectives for future QM/MM applications aided enzyme engineering in natural product biosynthesis will also be discussed.

NMR-guided directed evolution

Directed evolution is a powerful tool for improving existing properties and imparting completely new functionalities to proteins1-4. Nonetheless, its potential in even small proteins is inherently limited by the astronomical number of possible amino acid sequences. Sampling the complete sequence space of a 100-residue protein would require testing of 20100 combinations, which is beyond any existing experimental approach. In practice, selective modification of relatively few residues is sufficient for efficient improvement, functional enhancement and repurposing of existing proteins5. Moreover, computational methods have been developed to predict the locations and, in certain cases, identities of potentially productive mutations6-9. Importantly, all current approaches for prediction of hot spots and productive mutations rely heavily on structural information and/or bioinformatics, which is not always available for proteins of interest. Moreover, they offer a limited ability to identify beneficial mutations far from the active site, even though such changes may markedly improve the catalytic properties of an enzyme10. Machine learning methods have recently showed promise in predicting productive mutations11, but they frequently require large, high-quality training datasets, which are difficult to obtain in directed evolution experiments. Here we show that mutagenic hot spots in enzymes can be identified using NMR spectroscopy. In a proof-of-concept study, we converted myoglobin, a non-enzymatic oxygen storage protein, into a highly efficient Kemp eliminase using only three mutations. The observed levels of catalytic efficiency exceed those of proteins designed using current approaches and are similar with those of natural enzymes for the reactions that they are evolved to catalyse. Given the simplicity of this experimental approach, which requires no a priori structural or bioinformatic knowledge, we expect it to be widely applicable and to enable the full potential of directed enzyme evolution.

Efficient Base-Catalyzed Kemp Elimination in an Engineered Ancestral Enzyme

The routine generation of enzymes with completely new active sites is a major unsolved problem in protein engineering. Advances in this field have thus far been modest, perhaps due, at least in part, to the widespread use of modern natural proteins as scaffolds for de novo engineering. Most modern proteins are highly evolved and specialized and, consequently, difficult to repurpose for completely new functionalities. Conceivably, resurrected ancestral proteins with the biophysical properties that promote evolvability, such as high stability and conformational diversity, could provide better scaffolds for de novo enzyme generation. Kemp elimination, a non-natural reaction that provides a simple model of proton abstraction from carbon, has been extensively used as a benchmark in de novo enzyme engineering. Here, we present an engineered ancestral β-lactamase with a new active site that is capable of efficiently catalyzing Kemp elimination. The engineering of our Kemp eliminase involved minimalist design based on a single function-generating mutation, inclusion of an extra polypeptide segment at a position close to the de novo active site, and sharply focused, low-throughput library screening. Nevertheless, its catalytic parameters (k_cat/K_M~2·10⁵ M⁻¹ s⁻¹, k_cat~635 s⁻¹) compare favorably with the average modern natural enzyme and match the best proton-abstraction de novo Kemp eliminases that are reported in the literature. The general implications of our results for de novo enzyme engineering are discussed.

Bioinspired Iron-Catalyzed Dehydration of Aldoximes to Nitriles: A General N-O Redox-Cleavage Method

Inspired by OxdA that operates biocatalytic aldoxime dehydration, we have developed an efficient iron catalyst, Cp*Fe(1,2-Cy₂PC₆H₄O) (1), which rapidly converts various aliphatic and aromatic aldoximes to nitriles with release of H₂O at room temperature. The catalysis involves redox activation of the N-O bond by a 1e^- transfer from the iron catalyst to the oxime. Such redox-mediated N-O cleavage was demonstrated by the isolation of a ferrous iminato intermediate from the reaction of the ketoxime substrate. This iron-catalyzed acceptorless dehydration approach represents a general method for the preparation of nitriles, and it also delivers salicylonitriles by catalyzing the Kemp elimination reaction.

Tuning the Catalytic Activity of Synthetic Enzyme KE15 with DNA

Efficiency improvement of synthetic enzymes through scaffold modifications suffers from limitations in terms of effectiveness, cost, and potential devastating consequences for protein structural stability. Here, we propose an alternative to scaffold modification, within electrostatic preorganization theory, where the enzyme's greater environment is designed to support the evolution of the reaction in the active site. We demonstrate the feasibility of such an approach by placing a (polar) DNA fragment in the surroundings of the Kemp eliminase enzyme KE15 (structure from Houk's group) and computing the resulting change in catalytic activity. We find that the introduction of a DNA fragment magnifies the contribution of protein residues to the stabilization of the transition state, estimated from electric field calculations with polarizable molecular dynamics. Our randomly generated test systems reveal a 2.0 kcal/mol reduction in activation energy, suggesting that even more significant catalytic improvements could be made by optimizing DNA size, sequence, and orientation with respect to the enzyme, validating our approach.

Perpetuating enzymatically-induced spatiotemporal pH and catalytic heterogeneity of a hydrogel by nanoparticles

The attainment of spatiotemporally inhomogeneous chemical and physical properties within a system is gaining attention across disciplines due to the resemblance to environmental and biological heterogeneity. Notably, the origin of natural pH gradients and how they have been incorporated in cellular systems is one of the most important questions in understanding the prebiotic origin of life. Herein, we have demonstrated a spatiotemporal pH gradient formation pattern on a hydrogel surface by employing two different enzymatic reactions, namely, the reactions of glucose oxidase (pH decreasing) and urease (pH increasing). We found here a generic pattern of spatiotemporal change in pH and proton transfer catalytic activity that was completely altered in a cationic gold nanoparticle containing hydrogel. In the absence of nanoparticles, the gradually generated macroscopic pH gradient slowly diminished with time, whereas the presence of nanoparticles helped to perpetuate the generated gradient effect. This behavior is due to the differential responsiveness of the interface of the cationic nanoparticle in temporally changing surroundings with increasing or decreasing pH or ionic contents. Moreover, the catalytic proton transfer ability of the nanoparticle showed a concerted kinetic response following the spatiotemporal pH dynamics in the gel matrix. Notably, this nanoparticle-driven spatiotemporally resolved gel matrix will find applicability in the area of the membrane-free generation and control of spatially segregated chemistry at the macroscopic scale.

This work reports perpetuating effect in enzymatically generated spatiotemporal pH gradient across a hydrogel in presence of cationic gold nanoparticle; showing a new route in spatially resolved chemistry in a membrane-free environment.

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.

Origin of Site Selectivity in Toluene Hydroxylation by Cytochrome P450 Enzymes

Computational studies are utilized to reveal factors that determine the site selectivity in toluene hydroxylation by cytochrome P450 enzymes (CYPs). The DFT-computed inherent barriers suggest that the priority of product formation is in the order of benzyl alcohol > ortho- ≈ para- > meta-cresol. However, the specific size and shape of the cavities at the active sites of different CYPs dramatically affect the binding orientation of toluene, and thus, the site selectivity can be reordered.

Theoretical Investigation on the Elusive Reaction Mechanism of Spirooxindole Formation Mediated by Cytochrome P450s: A Nascent Feasible Charge-Shift C-O Bond Makes a Difference

Spirooxindoles are pivotal biofunctional groups widely distributed in natural products and clinic drugs. However, construction of such subtle chiral skeletons is a long-standing challenge to both organic and bioengineering scientists. The knowledge of enzymatic spirooxindole formation in nature may inspire rational design of new catalysts. To this end, we presented a theoretical investigation on the elusive mechanism of the spiro-ring formation at the 3-position of oxindole mediated by cytochrome P450 enzymes (P450). Our calculated results demonstrated that the electrophilic attack of CpdI, the active species of P450, to the substrate, shows regioselectivity, i.e., the attack at the C9 position forms a tetrahedral intermediate involving an unusual feasible charge-shift C9^δ+-O^δ- bond, while the attack at the C1 position forms an epoxide intermediate. The predominant route is the first route with the charge-shift bonding intermediate due to holding a relatively lower barrier by >5 kcal mol^-1 than the epoxide route, which fits the experimental observations. Such a delocalized charge-shift bond facilitates the formation of a spiro-ring mainly through elongation of the C1-C9 bond to eliminate the aromatization of the tricyclic beta-carboline. Our theoretical results shed profound mechanistic insights for the first time into the elusive spirooxindole formation mediated by P450s.

Interconnectivity between Surface Reactivity and Self-Assembly of Kemp Elimination Catalyzing Nanorods

Understanding the fundamental facts behind dynamicity of catalytic processes has been a longstanding quest across disciplines. Herein, we report self-assembly of catalytically active gold nanorods that can be regulated by tuning its reactivity towards a proton transfer reaction at different pH. Unlike substrate-induced templating and co-operativity, the enhanced aggregation rate is due to alteration of catalytic surface charge only during reactivity as negatively charged transition state of reactant (5-nitrobenzisoxazole) is formed on positively charged nanorod while undergoing a concerted E2-pathway. Herein, enhanced diffusivity during catalytic processes might also act as an additional contributing factor. Furthermore, we have also shown that nanosized hydrophobic cavities of clustered nanorods can also efficiently accelerate the rate of an aromatic nucleophilic substitution reaction, which also demonstrates a catalytic phenomenon that can lead to cascading of other reactions where substrates and products of the starting reactions are not directly involved.

De Novo-Designed β-Sheet Heme Proteins

The field of de novo protein design has met with considerable success over the past few decades. Heme, a cofactor, has often been introduced to impart a diverse array of functions to a protein, ranging from electron transport to respiration. In nature, heme is found to occur predominantly in α-helical structures over β-sheets, which has resulted in significant designs of heme proteins utilizing coiled-coil helices. By contrast, there are only a few known β-sheet proteins that bind heme and designs of β-sheets frequently result in amyloid-like aggregates. This review reflects on our success in designing a series of multistranded β-sheet heme binding peptides that are well folded in both aqueous and membrane-like environments. Initially, we designed a β-hairpin peptide that self-assembles to bind heme and performs peroxidase activity in membrane. The β-hairpin was optimized further to accommodate a heme binding pocket within multistranded β-sheets for catalysis and electron transfer in membranes. Furthermore, we de novo designed and characterized β-sheet peptides and miniproteins that are soluble in an aqueous environment capable of binding single and multiple hemes with high affinity and stability. Collectively, these studies highlight the substantial progress made toward the design of functional β-sheets.

Superior Proton-Transfer Catalytic Promiscuity of Cytochrome c in Self-Organized Media

Evolutionarily elderly proteins commonly feature greater catalytic promiscuity. Cytochrome c is among the first set of proteins in evolution to have known prospects in electron transport and peroxidative properties. Here, we report that cyt c is also a proficient proton-transfer catalyst and enhances the Kemp elimination (KE; model reaction to show proton transfer catalytic property) by ∼750-fold on self-organized systems like micelles and vesicles. The self-organized systems mimic the mitochondrial environment in vitro for cyt c. Using an array of biophysical and biochemical mutational assays, both acid-base and redox mechanistic pathways have been explored. The histidine moiety close to hemin group (His18) is mainly responsible for proton abstraction to promote the concerted E2 pathway for KE catalysis when cyt c is in its oxidized form; this has also been confirmed by a H18A mutant of cyt c. However, the redox pathway is predominant under reducing conditions in the presence of dithiothreitol over the pH range 6-7.4. Interestingly, we found almost 750-fold enhanced KE catalysis by cyt c compared to aqueous buffer. Overall, in addition to providing mechanistic insights, the data reveal an unprecedented catalytic property of cyt c that could be of high importance in an evolutionary perspective considering its role in delineating the phylogenic tree and also towards generating programmable designer biocatalysts.

Enhancing a de novo enzyme activity by computationally-focused ultra-low-throughput screening

Directed evolution has revolutionized protein engineering. Still, enzyme optimization by random library screening remains sluggish, in large part due to futile probing of mutations that are catalytically neutral and/or impair stability and folding. FuncLib is a novel approach which uses phylogenetic analysis and Rosetta design to rank enzyme variants with multiple mutations, on the basis of predicted stability. Here, we use it to target the active site region of a minimalist-designed, de novo Kemp eliminase. The similarity between the Michaelis complex and transition state for the enzymatic reaction makes this system particularly challenging to optimize. Yet, experimental screening of a small number of active-site variants at the top of the predicted stability ranking leads to catalytic efficiencies and turnover numbers (∼2 × 104 M-1 s-1 and ∼102 s-1) for this anthropogenic reaction that compare favorably to those of modern natural enzymes. This result illustrates the promise of FuncLib as a powerful tool with which to speed up directed evolution, even on scaffolds that were not originally evolved for those functions, by guiding screening to regions of the sequence space that encode stable and catalytically diverse enzymes. Empirical valence bond calculations reproduce the experimental activation energies for the optimized eliminases to within ∼2 kcal mol-1 and indicate that the enhanced activity is linked to better geometric preorganization of the active site. This raises the possibility of further enhancing the stability-guidance of FuncLib by computational predictions of catalytic activity, as a generalized approach for computational enzyme design.

Kemp Elimination Reaction Catalyzed by Electric Fields

The Kemp elimination reaction is the most widely used in the de novo design of new enzymes. The effect of two different kinds of electric fields in the reactions of acetate as a base with benzisoxazole and 5-nitrobenzisoxazole as substrates have been theoretically studied. The effect of the solvent reaction field has been calculated using the SMD continuum model for several solvents; we have shown that solvents inhibit both reactions, the decrease of the reaction rate being larger as far as the dielectric constant is increased. The diminution of the reaction rate is especially remarkable between aprotic organic solvents and protic solvents as water, the electrostatic term of the hydrogen bonds being the main factor for the large inhibitory effect of water. The presence of an external electric field oriented in the direction of the charge transfer (z axis) increases it and, so, the reaction rate. In the reaction of the nitro compound, if the electric field is oriented in an orthogonal direction (x axis) the charge transfer to the NO₂ group is favored and there is a subsequent increase of the reaction rate. However, this increase is smaller than the one produced by the field in the z axis. It is worthwhile mentioning that one of the main effects of external electric fields of intermediate intensity is the reorientation of the reactants. Finally, the implications of our results in the de novo design of enzymes are discussed.

Minimalist de novo Design of Protein Catalysts

The field of protein design has grown enormously in the past few decades. In this review we discuss the minimalist approach to design of artificial enzymes, in which protein sequences are created with the minimum number of elements for folding and function. This method relies on identifying starting points in catalytically inert scaffolds for active site installation. The progress of the field from the original helical assemblies of the 1980s to the more complex structures of the present day is discussed, highlighting the variety of catalytic reactions which have been achieved using these methods. We outline the strengths and weaknesses of the minimalist approaches, describe representative design cases and put it in the general context of the de novo design of proteins.

Directed Evolution of Artificial Metalloenzymes: A Universal Means to Tune the Selectivity of Transition Metal Catalysts?

Transition metal catalysts mediate a wide variety of chemo-, stereo-, and regioselective transformations, and therefore play a pivotal role in modern synthetic organic chemistry. Steric and electronic effects of ligands provide organic chemists with an exceedingly useful tool. More than four decades ago, chemists began to think about a different approach, namely, embedding achiral ligand/metal moieties covalently or noncovalently in protein hosts with formation of artificial metalloenzymes. While structurally fascinating, this approach led in each case only to a single (bio)catalyst, with its selectivity and activity being a matter of chance. In order to solve this fundamental problem, my group proposed in 2000-2002 the idea of directed evolution of artificial metalloenzymes. In earlier studies, we had already demonstrated that directed evolution of enzymes constitutes a viable method for enhancing and inverting the stereoselectivity of enzymes as catalysts in organic chemistry. We speculated that it should also be possible to manipulate selectivity and activity of artificial metalloenzymes, which would provide organic chemists with a tool for optimizing essentially any transition metal catalyzed reaction type. In order to put this vision into practice, we first turned to the Whitesides system for artificial metalloenzyme formation, comprising a biotinylated diphosphine/Rh moiety, which is anchored noncovalently to avidin or streptavidin. Following intensive optimization, proof of principle was finally demonstrated in 2006, which opened the door to a new research area. This personal Account critically assesses these early studies as well as subsequent efforts from my group focusing on different protein scaffolds, and includes briefly some of the most important current contributions of other groups. Two primary messages emerge: First, since organic chemists continue to be extremely good at designing and implementing man-made transition metal catalysts, often on a large scale, those scientists that are active in the equally intriguing field of directed evolution of artificial metalloenzymes should be moderate when generalizing claims. All factors required for a truly viable catalytic system need to be considered, especially activity and ease of upscaling. Second, the most exciting and thus far very rare cases of directed evolution of artificial metalloenzymes are those that focus on selective transformations that are not readily possible using state of the art transition metal catalysts.

Open-Source, Python-Based Redevelopment of the ChemShell Multiscale QM/MM Environment

ChemShell is a scriptable computational chemistry environment with an emphasis on multiscale simulation of complex systems using combined quantum mechanical and molecular mechanical (QM/MM) methods. Motivated by a scientific need to efficiently and accurately model chemical reactions on surfaces and within microporous solids on massively parallel computing systems, we present a major redevelopment of the ChemShell code, which provides a modern platform for advanced QM/MM embedding models. The new version of ChemShell has been re-engineered from the ground up with a new QM/MM driver module, an improved parallelization framework, new interfaces to high performance QM and MM programs, and a user interface written in the Python programming language. The redeveloped package is capable of performing QM/MM calculations on systems of significantly increased size, which we illustrate with benchmarks on zirconium dioxide nanoparticles of over 160000 atoms.

Human Enzymes for Organic Synthesis

Human enzymes have been widely studied in various disciplines. The number of reactions taking place in the human body is vast, and so is the number of potential catalysts for synthesis. Herein, we focus on the application of human enzymes that catalyze chemical reactions in course of the metabolism of drugs and xenobiotics. Some of these reactions have been explored on the preparative scale. The major field of application of human enzymes is currently drug development, where they are applied for the synthesis of drug metabolites.

The evolution of multiple active site configurations in a designed enzyme

Developments in computational chemistry, bioinformatics, and laboratory evolution have facilitated the de novo design and catalytic optimization of enzymes. Besides creating useful catalysts, the generation and iterative improvement of designed enzymes can provide valuable insight into the interplay between the many phenomena that have been suggested to contribute to catalysis. In this work, we follow changes in conformational sampling, electrostatic preorganization, and quantum tunneling along the evolutionary trajectory of a designed Kemp eliminase. We observe that in the Kemp Eliminase KE07, instability of the designed active site leads to the emergence of two additional active site configurations. Evolutionary conformational selection then gradually stabilizes the most efficient configuration, leading to an improved enzyme. This work exemplifies the link between conformational plasticity and evolvability and demonstrates that residues remote from the active sites of enzymes play crucial roles in controlling and shaping the active site for efficient catalysis.

Structure and function of the cytochrome P450 peroxygenase enzymes

The cytochromes P450 (P450s or CYPs) constitute a large heme enzyme superfamily, members of which catalyze the oxidative transformation of a wide range of organic substrates, and whose functions are crucial to xenobiotic metabolism and steroid transformation in humans and other organisms. The P450 peroxygenases are a subgroup of the P450s that have evolved in microbes to catalyze the oxidative metabolism of fatty acids, using hydrogen peroxide as an oxidant rather than NAD(P)H-driven redox partner systems typical of the vast majority of other characterized P450 enzymes. Early members of the peroxygenase (CYP152) family were shown to catalyze hydroxylation at the α and β carbons of medium-to-long-chain fatty acids. However, more recent studies on other CYP152 family P450s revealed the ability to oxidatively decarboxylate fatty acids, generating terminal alkenes with potential applications as drop-in biofuels. Other research has revealed their capacity to decarboxylate and to desaturate hydroxylated fatty acids to form novel products. Structural data have revealed a common active site motif for the binding of the substrate carboxylate group in the peroxygenases, and mechanistic and transient kinetic analyses have demonstrated the formation of reactive iron-oxo species (compounds I and II) that are ultimately responsible for hydroxylation and decarboxylation of fatty acids, respectively. This short review will focus on the biochemical properties of the P450 peroxygenases and on their biotechnological applications with respect to production of volatile alkenes as biofuels, as well as other fine chemicals.

Computational Design of Protein Function

The computational design of enzymes is a tremendous challenge for both chemistry and biochemistry. The ability to design stable and functional biocatalysts that could operate under different conditions to perform chemical reactions without precedent in nature, allowing the large-scale production of chemicals à la carte, would revolutionise both synthetic, pharmacologic and materials chemistry. Despite the great advances achieved, this highly multidisciplinary area of research is still in its infancy. This chapter describes the ‘inside-out’ protocol for computational enzyme design and both the achievements and limitations of the current technology are highlighted. Furthermore, molecular dynamics simulations have proved to be invaluable in the enzyme design process, constituting an important tool for discovering elusive catalytically relevant conformations of the engineered or designed enzyme. As a complement to the ‘inside-out’ design protocol, different examples where hybrid QM/MM approaches have been directly applied to discover beneficial mutations in rational computational enzyme design are described.

Biocatalysts for the pharmaceutical industry created by structure-guided directed evolution of stereoselective enzymes

Enzymes have been used for a long time as catalysts in the asymmetric synthesis of chiral intermediates needed in the production of therapeutic drugs. However, this alternative to man-made catalysts has suffered traditionally from distinct limitations, namely the often observed wrong or insufficient enantio- and/or regioselectivity, low activity, narrow substrate range, and insufficient thermostability. With the advent of directed evolution, these problems can be generally solved. The challenge is to develop and apply the most efficient mutagenesis methods which lead to highest-quality mutant libraries requiring minimal screening. Structure-guided saturation mutagenesis and its iterative form have emerged as the method of choice for evolving stereo- and regioselective mutant enzymes needed in the asymmetric synthesis of chiral intermediates. The number of (industrial) applications in the preparation of chiral pharmaceuticals is rapidly increasing. This review features and analyzes typical case studies.