Repertoire of Computationally Designed Peroxygenases for Enantiodivergent C-H Oxyfunctionalization Reactions

Unlocking the potential of enzyme engineering via rational computational design strategies

Enzymes play a pivotal role in various industries by enabling efficient, eco-friendly, and sustainable chemical processes. However, the low turnover rates and poor substrate selectivity of enzymes limit their large-scale applications. Rational computational enzyme design, facilitated by computational algorithms, offers a more targeted and less labor-intensive approach. There has been notable advancement in employing rational computational protein engineering strategies to overcome these issues, it has not been comprehensively reviewed so far. This article reviews recent developments in rational computational enzyme design, categorizing them into three types: structure-based, sequence-based, and data-driven machine learning computational design. Case studies are presented to demonstrate successful enhancements in catalytic activity, stability, and substrate selectivity. Lastly, the article provides a thorough analysis of these approaches, highlights existing challenges and potential solutions, and offers insights into future development directions.

Engineering of Unspecific Peroxygenases Using a Superfolder-Green-Fluorescent-Protein-Mediated Secretion System in Escherichia coli

Unspecific peroxygenases (UPOs), secreted by fungi, demonstrate versatility in catalyzing challenging selective oxyfunctionalizations. However, the number of peroxygenases and corresponding variants with tailored selectivity for a broader substrate scope is still limited due to the lack of efficient engineering strategies. In this study, a new unspecific peroxygenase from Coprinopsis marcescibilis (CmaUPO) is identified and characterized. To enhance or reverse the enantioselectivity of wildtype (WT) CmaUPO catalyzed asymmetric hydroxylation of ethylbenzene, CmaUPO was engineered using an efficient superfolder-green-fluorescent-protein (sfGFP)-mediated secretion system in Escherichia coli. Iterative saturation mutagenesis (ISM) was used to target the residual sites lining the substrate tunnel, resulting in two variants: T125A/A129G and T125A/A129V/A247H/T244A/F243G. The two variants greatly improved the enantioselectivities [21% ee (R) for WT], generating the (R)-1-phenylethanol or (S)-1-phenylethanol as the main product with 99% ee (R) and 84% ee (S), respectively. The sfGFP-mediated secretion system in E. coli demonstrates applicability for different UPOs (AaeUPO, CciUPO, and PabUPO-I). Therefore, this developed system provides a robust platform for heterologous expression and enzyme engineering of UPOs, indicating great potential for their sustainable and efficient applications in various chemical transformations.

Engineering carboxylic acid reductases and unspecific peroxygenases for flavor and fragrance biosynthesis

Emerging consumer demand for safer, more sustainable flavors and fragrances has created new challenges for the industry. Enzymatic syntheses represent a promising green production route, but the broad application requires engineering advancements for expanded diversity, improved selectivity, and enhanced stability to be cost-competitive with current methods. This review discusses recent advances and future outlooks for enzyme engineering in this field. We focus on carboxylic acid reductases (CARs) and unspecific peroxygenases (UPOs) that enable selective productions of complex flavor and fragrance molecules. Both enzyme types consist of natural variants with attractive characteristics for biocatalytic applications. Applying protein engineering methods, including rational design and directed evolution in concert with computational modeling, present excellent examples for property improvements to unleash the full potential of enzymes in the biosynthesis of value-added chemicals.

Opportunities and challenges in design and optimization of protein function

The field of protein design has made remarkable progress over the past decade. Historically, the low reliability of purely structure-based design methods limited their application, but recent strategies that combine structure-based and sequence-based calculations, as well as machine learning tools, have dramatically improved protein engineering and design. In this Review, we discuss how these methods have enabled the design of increasingly complex structures and therapeutically relevant activities. Additionally, protein optimization methods have improved the stability and activity of complex eukaryotic proteins. Thanks to their increased reliability, computational design methods have been applied to improve therapeutics and enzymes for green chemistry and have generated vaccine antigens, antivirals and drug-delivery nano-vehicles. Moreover, the high success of design methods reflects an increased understanding of basic rules that govern the relationships among protein sequence, structure and function. However, de novo design is still limited mostly to α-helix bundles, restricting its potential to generate sophisticated enzymes and diverse protein and small-molecule binders. Designing complex protein structures is a challenging but necessary next step if we are to realize our objective of generating new-to-nature activities.

Peroxygenase-Catalysed Sulfoxidations in Non-Aqueous Media

Chiral sulfoxides are valuable building blocks in asymmetric synthesis. However, the biocatalytic synthesis of chiral sulfoxides is still challenged by low product titres. Herein, we report the use of peroxygenase as a catalyst for asymmetric sulfoxidation under non-aqueous conditions. Upon covalent immobilisation, the peroxygenase showed stability and activity under neat reaction conditions. A large variety of sulfides was converted into chiral sulfoxides in very high product concentration with moderate to satisfactory optical purity (e. g. 626 mM of (R)-methyl phenyl sulfoxide in approx. 89 % ee in 48 h). Further polishing of the ee value via cascading methionine reductase A (MsrA) gave>99 % ee of the sulfoxide. The robustness of the enzymes and high product titer is superior to the state-of-the-art methodologies. Gram-scale synthesis has been demonstrated. Overall, we demonstrated a practical and facile catalytic method to synthesize chiral sulfoxides.

Enhancing the Catalytic Efficiency of D-lactonohydrolase through the Synergy of Tunnel Engineering, Evolutionary Analysis, and Force-Field Calculations

Computational design advances enzyme evolution and their use in biocatalysis in a faster and more efficient manner. In this study, a synergistic approach integrating tunnel engineering, evolutionary analysis, and force-field calculations has been employed to enhance the catalytic activity of D-lactonohydrolase (D-Lac), which is a pivotal enzyme involved in the resolution of racemic pantolactone during the production of vitamin B5. The best mutant, N96S/A271E/F274Y/F308G (M3), was obtained and its catalytic efficiency (kcat/KM) was nearly 23-fold higher than that of the wild-type. The M3 whole-cell converted 20 % of DL-pantolactone into D-pantoic acid (D-PA, >99 % e.e.) with a conversion rate of 47 % and space-time yield of 107.1 g L-1 h-1, demonstrating its great potential for industrial-scale D-pantothenic acid production. Molecular dynamics (MD) simulations revealed that the reduction in the steric hindrance within the substrate tunnel and conformational reconstruction of the distal loop resulted in a more favourable"catalytic" conformation, making it easier for the substrate and enzyme to enter their pre-reaction state. This study illustrates the potential of the distal residue on the pivotal loop at the entrance of the D-Lac substrate tunnel as a novel modification hotspot capable of reshaping energy patterns and consequently influencing the enzymatic activity.

Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering

Enzymes can be engineered at the level of their amino acid sequences to optimize key properties such as expression, stability, substrate range, and catalytic efficiency-or even to unlock new catalytic activities not found in nature. Because the search space of possible proteins is vast, enzyme engineering usually involves discovering an enzyme starting point that has some level of the desired activity followed by directed evolution to improve its "fitness" for a desired application. Recently, machine learning (ML) has emerged as a powerful tool to complement this empirical process. ML models can contribute to (1) starting point discovery by functional annotation of known protein sequences or generating novel protein sequences with desired functions and (2) navigating protein fitness landscapes for fitness optimization by learning mappings between protein sequences and their associated fitness values. In this Outlook, we explain how ML complements enzyme engineering and discuss its future potential to unlock improved engineering outcomes.

In silico design of multipoint mutants for enhanced performance of Thermomyces lanuginosus lipase for efficient biodiesel production

BACKGROUND

Biodiesel, an emerging sustainable and renewable clean energy, has garnered considerable attention as an alternative to fossil fuels. Although lipases are promising catalysts for biodiesel production, their efficiency in industrial-scale application still requires improvement.

RESULTS

In this study, a novel strategy for multi-site mutagenesis in the binding pocket was developed via FuncLib (for mutant enzyme design) and Rosetta Cartesian_ddg (for free energy calculation) to improve the reaction rate and yield of lipase-catalyzed biodiesel production. Thermomyces lanuginosus lipase (TLL) with high activity and thermostability was obtained using the Pichia pastoris expression system. The specific activities of the mutants M11 and M21 (each with 5 and 4 mutations) were 1.50- and 3.10-fold higher, respectively, than those of the wild-type (wt-TLL). Their corresponding melting temperature profiles increased by 10.53 and 6.01 °C, [Formula: see text] (the temperature at which the activity is reduced to 50% after 15 min incubation) increased from 60.88 to 68.46 °C and 66.30 °C, and the optimum temperatures shifted from 45 to 50 °C. After incubation in 60% methanol for 1 h, the mutants M11 and M21 retained more than 60% activity, and 45% higher activity than that of wt-TLL. Molecular dynamics simulations indicated that the increase in thermostability could be explained by reduced atomic fluctuation, and the improved catalytic properties were attributed to a reduced binding free energy and newly formed hydrophobic interaction. Yields of biodiesel production catalyzed by mutants M11 and M21 for 48 h at an elevated temperature (50 °C) were 94.03% and 98.56%, respectively, markedly higher than that of the wt-TLL (88.56%) at its optimal temperature (45 °C) by transesterification of soybean oil.

CONCLUSIONS

An integrating strategy was first adopted to realize the co-evolution of catalytic efficiency and thermostability of lipase. Two promising mutants M11 and M21 with excellent properties exhibited great potential for practical applications for in biodiesel production.

Computational Characterization of the Reactivity of Compound I in Unspecific Peroxygenases

Unspecific peroxygenases (UPOs) are emerging as promising biocatalysts for selective oxyfunctionalization of unactivated C-H bonds. However, their potential in large-scale synthesis is currently constrained by suboptimal chemical selectivity. Improving the selectivity of UPOs requires a deep understanding of the molecular basis of their catalysis. Recent molecular simulations have sought to unravel UPO's selectivity and inform their design principles. However, most of these studies focused on substrate-binding poses. Few researchers have investigated how the reactivity of CpdI, the principal oxidizing intermediate in the catalytic cycle, influences selectivity in a realistic protein environment. Moreover, the influence of protein electrostatics on the reaction kinetics of CpdI has also been largely overlooked. To bridge this gap, we used multiscale simulations to interpret the regio- and enantioselective hydroxylation of the n-heptane substrate catalyzed by Agrocybe aegerita UPO (AaeUPO). We comprehensively characterized the energetics and kinetics of the hydrogen atom-transfer (HAT) step, initiated by CpdI, and the subsequent oxygen rebound step forming the product. Notably, our approach involved both free energy and potential energy evaluations in a quantum mechanics/molecular mechanics (QM/MM) setting, mitigating the dependence of results on the choice of initial conditions. These calculations illuminate the thermodynamics and kinetics of the HAT and oxygen rebound steps. Our findings highlight that both the conformational selection and the distinct chemical reactivity of different substrate hydrogen atoms together dictate the regio- and enantio-selectivity. Building on our previous study of CpdI's formation in AaeUPO, our results indicate that the HAT step is the rate-limiting step in the overall catalytic cycle. The subsequent oxygen rebound step is swift and retains the selectivity determined by the HAT step. We also pinpointed several polar and charged amino acid residues whose electrostatic potentials considerably influence the reaction barrier of the HAT step. Notably, the Glu196 residue is pivotal for both the CpdI's formation and participation in the HAT step. Our research offers in-depth insights into the catalytic cycle of AaeUPO, which will be instrumental in the rational design of UPOs with enhanced properties.

Enantioselective High-Throughput Assay Showcased for the Identification of (R)- as well as (S)-Selective Unspecific Peroxygenases for C-H Oxidation

Identifying (bio)catalysts displaying high enantio-/stereoselectivity is a fundamental prerequisite for the advancement of asymmetric catalysis. Herein, a high-throughput, stereoselective screening assay is reported that gives information on enantioselectivity, stereopreference and activity as showcased for peroxygenase-catalyzed hydroxylation. The assay is based on spectrophotometric analysis of the simultaneous formation of NAD(P)H from the alcohol dehydrogenase catalyzed enantioselective oxidation of the sec-alcohol product formed in the peroxygenase reaction. The assay was applied to investigate a library comprising 44 unspecific peroxygenases (UPOs) containing 25 UPOs not reported yet. Thereby, previously non-described wild-type UPOs displaying (S)- as well as (R)-stereoselectivity for the hydroxylation of representative model substrates were identified, reaching up to 98 % ee for the (R)- and 94 % ee for the (S)-enantiomer. Homology models with concomitant docking studies indicated the structural reason for the observed complementary stereopreference.

Toward Kilogram-Scale Peroxygenase-Catalyzed Oxyfunctionalization of Cyclohexane

Mol-scale oxyfunctionalization of cyclohexane to cyclohexanol/cyclohexanone (KA-oil) using an unspecific peroxygenase is reported. Using AaeUPO from Agrocybe aegerita and simple H₂O₂ as an oxidant, cyclohexanol concentrations of more than 300 mM (>60% yield) at attractive productivities (157 mM h^-1, approx. 15 g L^-1 h^-1) were achieved. Current limitations of the proposed biooxidation system have been identified paving the way for future improvements and implementation.

Designed active-site library reveals thousands of functional GFP variants

Mutations in a protein active site can lead to dramatic and useful changes in protein activity. The active site, however, is sensitive to mutations due to a high density of molecular interactions, substantially reducing the likelihood of obtaining functional multipoint mutants. We introduce an atomistic and machine-learning-based approach, called high-throughput Functional Libraries (htFuncLib), that designs a sequence space in which mutations form low-energy combinations that mitigate the risk of incompatible interactions. We apply htFuncLib to the GFP chromophore-binding pocket, and, using fluorescence readout, recover >16,000 unique designs encoding as many as eight active-site mutations. Many designs exhibit substantial and useful diversity in functional thermostability (up to 96 °C), fluorescence lifetime, and quantum yield. By eliminating incompatible active-site mutations, htFuncLib generates a large diversity of functional sequences. We envision that htFuncLib will be used in one-shot optimization of activity in enzymes, binders, and other proteins.

De Novo Computational Design of a Lipase with Hydrolysis Activity towards Middle-Chained Fatty Acid Esters

Innovations in biocatalysts provide great prospects for intolerant environments or novel reactions. Due to the limited catalytic capacity and the long-term and labor-intensive characteristics of mining enzymes with the desired functions, de novo enzyme design was developed to obtain industrial application candidates in a rapid and convenient way. Here, based on the catalytic mechanisms and the known structures of proteins, we proposed a computational protein design strategy combining de novo enzyme design and laboratory-directed evolution. Starting with the theozyme constructed using a quantum-mechanical approach, the theoretical enzyme-skeleton combinations were assembled and optimized via the Rosetta "inside-out" protocol. A small number of designed sequences were experimentally screened using SDS-PAGE, mass spectrometry and a qualitative activity assay in which the designed enzyme 1a8uD₁ exhibited a measurable hydrolysis activity of 24.25 ± 0.57 U/g towards p-nitrophenyl octanoate. To improve the activity of the designed enzyme, molecular dynamics simulations and the RosettaDesign application were utilized to further optimize the substrate binding mode and amino acid sequence, thus keeping the residues of theozyme intact. The redesigned lipase 1a8uD₁-M8 displayed enhanced hydrolysis activity towards p-nitrophenyl octanoate-3.34 times higher than that of 1a8uD₁. Meanwhile, the natural skeleton protein (PDB entry 1a8u) did not display any hydrolysis activity, confirming that the hydrolysis abilities of the designed 1a8uD₁ and the redesigned 1a8uD₁-M8 were devised from scratch. More importantly, the designed 1a8uD₁-M8 was also able to hydrolyze the natural middle-chained substrate (glycerol trioctanoate), for which the activity was 27.67 ± 0.69 U/g. This study indicates that the strategy employed here has great potential to generate novel enzymes exhibiting the desired reactions.