Optimization of reorganization energy drives evolution of the designed Kemp eliminase KE07

Directed Evolution of Protoglobin Optimizes the Enzyme Electric Field

To unravel why computational design fails in creating viable enzymes, while directed evolution (DE) succeeds, our research delves into the laboratory evolution of protoglobin. DE has adapted this protein to efficiently catalyze carbene transfer reactions. We show that the previously proposed enhanced substrate access and binding alone cannot account for increased yields during DE. The 3D electric field in the entire active site is tracked through protein dynamics, clustered using the affinity propagation algorithm, and subjected to principal component analysis. This analysis reveals notable changes in the electric field with DE, where distinct field topologies influence transition state energetics and mechanism. A chemically meaningful field component emerges and takes the lead during DE and facilitates crossing the barrier to carbene transfer. Our findings underscore intrinsic electric field dynamic's influence on enzyme function, the ability of the field to switch mechanisms within the same protein, and the crucial role of the field in enzyme design.

Theoretical Improvements in Enzyme Efficiency Associated with Noisy Rate Constants and Increased Dissipation

Previous studies have revealed the extraordinarily large catalytic efficiency of some enzymes. High catalytic proficiency is an essential accomplishment of biological evolution. Natural selection led to the increased turnover number, kcat, and enzyme efficiency, kcat/KM, of uni-uni enzymes, which convert a single substrate into a single product. We added or multiplied random noise with chosen rate constants to explore the correlation between dissipation and catalytic efficiency for ten enzymes: beta-galactosidase, glucose isomerase, β-lactamases from three bacterial strains, ketosteroid isomerase, triosephosphate isomerase, and carbonic anhydrase I, II, and T200H. Our results highlight the role of biological evolution in accelerating thermodynamic evolution. The catalytic performance of these enzymes is proportional to overall entropy production-the main parameter from irreversible thermodynamics. That parameter is also proportional to the evolutionary distance of β-lactamases PC1, RTEM, and Lac-1 when natural or artificial evolution produces the optimal or maximal possible catalytic efficiency. De novo enzyme design and attempts to speed up the rate-limiting catalytic steps may profit from the described connection between kinetics and thermodynamics.

Stereodivergent atom-transfer radical cyclization by engineered cytochromes P450

Naturally occurring enzymes can be a source of unnatural reactivity that can be molded by directed evolution to generate efficient biocatalysts with valuable activities. Owing to the lack of exploitable stereocontrol elements in synthetic systems, steering the absolute and relative stereochemistry of free-radical processes is notoriously difficult in asymmetric catalysis. Inspired by the innate redox properties of first-row transition-metal cofactors, we repurposed cytochromes P450 to catalyze stereoselective atom-transfer radical cyclization. A set of metalloenzymes was engineered to impose substantial stereocontrol over the radical addition step and the halogen rebound step in these unnatural processes, allowing enantio- and diastereodivergent radical catalysis. This evolvable metalloenzyme platform represents a promising solution to tame fleeting radical intermediates for asymmetric catalysis.

Active site dynamics and catalytic mechanism in arabinan hydrolysis catalyzed by GH43 endo-arabinanase from QM/MM molecular dynamics simulation and potential energy surface

The endo-1,5-α-L-arabinanases, belonging to glycoside hydrolase family 43 (GH43), catalyse the hydrolysis of α-1,5-arabinofuranosidic bonds in arabinose-containing polysaccharides. These enzymes are proposed targets for industrial and medical applications. Here, molecular dynamics (MD), potential energy surface and free energy (potential of mean force) simulations are undertaken using hybrid quantum mechanical/molecular mechanical (QM/MM) potentials to understand the active site dynamics, catalytic mechanism and the electrostatic influence of active site residues of the GH43 endo-arabinanase from G. stearothermophilus. The calculated results give support to the single-displacement mechanism proposed for the inverting GH43 enzymes: first a proton is transferred from the general acid E201 to the substrate, followed by a nucleophilic attack by water, activated by the general base D27, on the anomer carbon. A conformational change (²E ↔E₃ ↔ ⁴E) in the -1 sugar ring is observed involving a transition state featuring an oxocarbenium ion character. Residues D87, K106, H271 are highlighted as potential targets for future mutation experiments in order to increase the efficiency of the reaction. To our knowledge, this is the first QM/MM study providing molecular insights into the glycosidic bond hydrolysis of a furanoside substrate by an inverting GH in solution.Communicated by Ramaswamy H. Sarma.

Designing better enzymes: Insights from directed evolution

De novo enzymes can be created by computational design and directed evolution. Here, we review recent insights into the origins of catalytic power in evolved designer enzymes to pinpoint opportunities for next-generation designs: Evolution precisely organizes active sites, introduces catalytic H-bonding networks, invokes electrostatic catalysis, and creates dynamical networks embedding the active site in a reactive protein scaffold. Such insights foster our fundamental knowledge of enzyme catalysis and fuel the future design of tailor-made enzymes.

Harnessing Conformational Plasticity to Generate Designer Enzymes

Recent years have witnessed an explosion of interest in understanding the role of conformational dynamics both in the evolution of new enzymatic activities from existing enzymes and in facilitating the emergence of enzymatic activity de novo on scaffolds that were previously non-catalytic. There are also an increasing number of examples in the literature of targeted engineering of conformational dynamics being successfully used to alter enzyme selectivity and activity. Despite the obvious importance of conformational dynamics to both enzyme function and evolvability, many (although not all) computational design approaches still focus either on pure sequence-based approaches or on using structures with limited flexibility to guide the design. However, there exist a wide variety of computational approaches that can be (re)purposed to introduce conformational dynamics as a key consideration in the design process. Coupled with laboratory evolution and more conventional existing sequence- and structure-based approaches, these techniques provide powerful tools for greatly expanding the protein engineering toolkit. This Perspective provides an overview of evolutionary studies that have dissected the role of conformational dynamics in facilitating the emergence of novel enzymes, as well as advances in computational approaches that allow one to target conformational dynamics as part of enzyme design. Harnessing conformational dynamics in engineering studies is a powerful paradigm with which to engineer the next generation of 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.

Bottom-Up Nonempirical Approach To Reducing Search Space in Enzyme Design Guided by Catalytic Fields

Currently developed protocols of theozyme design still lead to biocatalysts with much lower catalytic activity than enzymes existing in nature, and, so far, the only avenue of improvement was the in vitro laboratory-directed evolution (LDE) experiments. In this paper, we propose a different strategy based on "reversed" methodology of mutation prediction. Instead of common "top-down" approach, requiring numerous assumptions and vast computational effort, we argue for a "bottom-up" approach that is based on the catalytic fields derived directly from transition state and reactant complex wave functions. This enables direct one-step determination of the general quantitative angular characteristics of optimal catalytic site and simultaneously encompasses both the transition-state stabilization (TSS) and ground-state destabilization (GSD) effects. We further extend the static catalytic field approach by introducing a library of atomic multipoles for amino acid side-chain rotamers, which, together with the catalytic field, allow one to determine the optimal side-chain orientations of charged amino acids constituting the elusive structure of a preorganized catalytic environment. Obtained qualitative agreement with experimental LDE data for Kemp eliminase KE07 mutants validates the proposed procedure, yielding, in addition, a detailed insight into possible dynamic and epistatic effects.

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.

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.

CADEE: Computer-Aided Directed Evolution of Enzymes

The tremendous interest in enzymes as biocatalysts has led to extensive work in enzyme engineering, as well as associated methodology development. Here, a new framework for computer-aided directed evolution of enzymes (CADEE) is presented which allows a drastic reduction in the time necessary to prepare and analyze in silico semi-automated directed evolution of enzymes. A pedagogical example of the application of CADEE to a real biological system is also presented in order to illustrate the CADEE workflow.

First-Principles Free-Energy Barriers for Photoelectrochemical Surface Reactions: Proton Abstraction at TiO_{2}(110)

We explicitly calculate the free-energy barrier for the initial proton abstraction in the water splitting reaction at rutile TiO_{2}(110) through ab initio molecular dynamics. Combining solid-state embedding, an energy based reaction coordinate and state-of-the-art free-energy reconstruction techniques renders the calculation tractable at the hybrid density-functional theory level. The obtained free-energy barrier of approximately 0.2 eV, depending slightly on the orientation of the first acceptor water molecule, suggests a hindered reaction on the pristine rutile surface.

The role of side chain entropy and mutual information for improving the de novo design of Kemp eliminases KE07 and KE70

Side chain entropy and mutual entropy information between residue pairs have been calculated for two de novo designed Kemp eliminase enzymes, KE07 and KE70, and for their most improved versions at the end of laboratory directed evolution (LDE).

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.

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.