Designing better enzymes: Insights from directed evolution

Machine learning-assisted substrate binding pocket engineering based on structural information

Engineering enzyme-substrate binding pockets is the most efficient approach for modifying catalytic activity, but is limited if the substrate binding sites are indistinct. Here, we developed a 3D convolutional neural network for predicting protein-ligand binding sites. The network was integrated by DenseNet, UNet, and self-attention for extracting features and recovering sample size. We attempted to enlarge the dataset by data augmentation, and the model achieved success rates of 48.4%, 35.5%, and 43.6% at a precision of ≥50% and 52%, 47.6%, and 58.1%. The distance of predicted and real center is ≤4 Å, which is based on SC6K, COACH420, and BU48 validation datasets. The substrate binding sites of Klebsiella variicola acid phosphatase (KvAP) and Bacillus anthracis proline 4-hydroxylase (BaP4H) were predicted using DUnet, showing high competitive performance of 53.8% and 56% of the predicted binding sites that critically affected the catalysis of KvAP and BaP4H. Virtual saturation mutagenesis was applied based on the predicted binding sites of KvAP, and the top-ranked 10 single mutations contributed to stronger enzyme-substrate binding varied while the predicted sites were different. The advantage of DUnet for predicting key residues responsible for enzyme activity further promoted the success rate of virtual mutagenesis. This study highlighted the significance of correctly predicting key binding sites for enzyme engineering.

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.

Computation-guided engineering of distal mutations in an artificial enzyme

Artificial enzymes are valuable biocatalysts able to perform new-to-nature transformations with the precision and (enantio-)selectivity of natural enzymes. Although they are highly engineered biocatalysts, they often cannot reach catalytic rates akin those of their natural counterparts, slowing down their application in real-world industrial processes. Typically, their designs only optimise the chemistry inside the active site, while overlooking the role of protein dynamics on catalysis. In this work, we show how the catalytic performance of an already engineered artificial enzyme can be further improved by distal mutations that affect the conformational equilibrium of the protein. To this end, we subjected a specialised artificial enzyme based on the lactococcal multidrug resistance regulator (LmrR) to an innovative algorithm that quickly inspects the whole protein sequence space for hotpots which affect the protein dynamics. From an initial predicted selection of 73 variants, two variants with mutations distant by more than 11 Å from the catalytic pAF residue showed increased catalytic activity towards the new-to-nature hydrazone formation reaction. Their recombination displayed a 66% higher turnover number and 14 °C higher thermostability. Microsecond time scale molecular dynamics simulations evidenced a shift in the distribution of productive enzyme conformations, which are the result of a cascade of interactions initiated by the introduced mutations.

Epistasis arises from shifting the rate-limiting step during enzyme evolution of a β-lactamase

Epistasis, the non-additive effect of mutations, can provide combinatorial improvements to enzyme activity that substantially exceed the gains from individual mutations. Yet the molecular mechanisms of epistasis remain elusive, undermining our ability to predict pathogen evolution and engineer biocatalysts. Here we reveal how directed evolution of a β-lactamase yielded highly epistatic activity enhancements. Evolution selected four mutations that increase antibiotic resistance 40-fold, despite their marginal individual effects (≤2-fold). Synergistic improvements coincided with the introduction of super-stochiometric burst kinetics, indicating that epistasis is rooted in the enzyme's conformational dynamics. Our analysis reveals that epistasis stemmed from distinct effects of each mutation on the catalytic cycle. The initial mutation increased protein flexibility and accelerated substrate binding, which is rate-limiting in the wild-type enzyme. Subsequent mutations predominantly boosted the chemical steps by fine-tuning substrate interactions. Our work identifies an overlooked cause for epistasis: changing the rate-limiting step can result in substantial synergy that boosts enzyme activity.

Hydrophobicity-Based Force Field In Enzymes

The biocatalysis process takes place with the participation of enzymes, which, depending on the reaction carried out, require, apart from the appropriate arrangement of catalytic residues, an appropriate external force field. It is generated by the protein body. The relatively small size of the part directly involved in the process itself is supported by the presence of an often complex structure of the protein body, the purpose of which is to provide an appropriate local force field, eliminating the influence of water. Very often, the large size of the enzyme is an expression of the complex form of this field. In this paper, a comparative analysis of arbitrarily selected enzymes, representatives of different enzyme classes, was carried out, focusing on the measurement of the diversity of the force field provided by a given protein. This analysis was based on the fuzzy oil drop model (FOD) and its modified version (FOD-M), which takes into account the participation of nonaqueous external factors in shaping the structure and thus the force field within the protein. The degree and type of ordering of the hydrophobicity distribution in the protein molecule is the result of the influence of the environment but also the supplier of the local environment for a given process, including the catalysis process in particular. Determining the share of a nonaqueous environment is important due to the ubiquity of polar water, whose participation in processes with high specificity requires control. It can be assumed that some enzymes in their composition have a permanently built-in part, the role of which is reduced to that of a permanent chaperone. It provides a specific external force field needed for the process. The proposed model, generalized to other types of proteins, may also provide a form of recording the environment model for the simulation of the in silico protein folding process, taking into account the impact of its differentiation.

Fluctuation Relations to Calculate Protein Redox Potentials from Molecular Dynamics Simulations

The tunable design of protein redox potentials promises to open a range of applications in biotechnology and catalysis. Here, we introduce a method to calculate redox potential changes by combining fluctuation relations with molecular dynamics simulations. It involves the simulation of reduced and oxidized states, followed by the instantaneous conversion between them. Energy differences introduced by the perturbations are obtained using the Kubo-Onsager approach. Using a detailed fluctuation relation coupled with Bayesian inference, these are postprocessed into estimates for the redox potentials in an efficient manner. This new method, denoted MD + CB, is tested on a de novo four-helix bundle heme protein (the m4D2 "maquette") and five designed mutants, including some mutants characterized experimentally in this work. The MD + CB approach is found to perform reliably, giving redox potential shifts with reasonably good correlation (0.85) to the experimental values for the mutants. The MD + CB approach also compares well with redox potential shift predictions using a continuum electrostatic method. The estimation method employed within the MD + CB approach is straightforwardly transferable to standard equilibrium MD simulations and holds promise for redox protein engineering and design applications.

A circuitous route for in vitro multi-enzyme cascade production of cytidine triphosphate to overcome the thermodynamic bottleneck

Cytidine triphosphate (CTP), as a substance involved in the metabolism of phospholipids, proteins and nucleic acids, has precise drug effects and is a direct precursor for the synthesis of drugs such as citicoline. In this study, we established an in vitro six-enzyme cascade system to generate CTP. To avoid thermodynamic bottlenecks, we employed a circuitous and two-stage reaction strategy. Using cytidine as the key substrate, the final product CTP is obtained via the deamination and uridine phosphorylation pathways, relying on the irreversible reaction of cytidine triphosphate synthase to catalyze the amination of uridine triphosphate. Several extremophilic microbial-derived deaminases were screened and characterized, and a suitable cytidine deaminase was selected to match the first-stage reaction conditions. In addition, directed evolution modification of the rate-limiting enzyme CTP synthetase in the pathway yielded a variant that successfully relieved the product feedback inhibition, along with a 1.7-fold increase in activity over the wild type. After optimizing the reaction conditions, we finally carried out the catalytic reaction at an initial cytidine concentration of 20 mM, and the yield of CTP exceeded 82% within 10.0 h.

Microelectronic Morphogenesis: Smart Materials with Electronics Assembling into Artificial Organisms

Microelectronic morphogenesis is the creation and maintenance of complex functional structures by microelectronic information within shape-changing materials. Only recently has in-built information technology begun to be used to reshape materials and their functions in three dimensions to form smart microdevices and microrobots. Electronic information that controls morphology is inheritable like its biological counterpart, genetic information, and is set to open new vistas of technology leading to artificial organisms when coupled with modular design and self-assembly that can make reversible microscopic electrical connections. Three core capabilities of cells in organisms, self-maintenance (homeostatic metabolism utilizing free energy), self-containment (distinguishing self from nonself), and self-reproduction (cell division with inherited properties), once well out of reach for technology, are now within the grasp of information-directed materials. Construction-aware electronics can be used to proof-read and initiate game-changing error correction in microelectronic self-assembly. Furthermore, noncontact communication and electronically supported learning enable one to implement guided self-assembly and enhance functionality. Here, the fundamental breakthroughs that have opened the pathway to this prospective path are reviewed, the extent and way in which the core properties of life can be addressed are analyzed, and the potential and indeed necessity of such technology for sustainable high technology in society is discussed.

From random to rational: improving enzyme design through electric fields, second coordination sphere interactions, and conformational dynamics

Enzymes are versatile and efficient biological catalysts that drive numerous cellular processes, motivating the development of enzyme design approaches to tailor catalysts for diverse applications. In this perspective, we investigate the unique properties of natural, evolved, and designed enzymes, recognizing their strengths and shortcomings. We highlight the challenges and limitations of current enzyme design protocols, with a particular focus on their limited consideration of long-range electrostatic and dynamic effects. We then delve deeper into the impact of the protein environment on enzyme catalysis and explore the roles of preorganized electric fields, second coordination sphere interactions, and protein dynamics for enzyme function. Furthermore, we present several case studies illustrating successful enzyme-design efforts incorporating enzyme strategies mentioned above to achieve improved catalytic properties. Finally, we envision the future of enzyme design research, spotlighting the challenges yet to be overcome and the synergy of intrinsic electric fields, second coordination sphere interactions, and conformational dynamics to push the state-of-the-art boundaries.

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.

Mechanism-Based Redesign of GAP to Activate Oncogenic Ras

Ras GTPases play a crucial role in cell signaling pathways. Mutations of the Ras gene occur in about one third of cancerous cell lines and are often associated with detrimental clinical prognosis. Hot spot residues Gly12, Gly13, and Gln61 cover 97% of oncogenic mutations, which impair the enzymatic activity in Ras. Using QM/MM free energy calculations, we present a two-step mechanism for the GTP hydrolysis catalyzed by the wild-type Ras.GAP complex. We found that the deprotonation of the catalytic water takes place via the Gln61 as a transient Brønsted base. We also determined the reaction profiles for key oncogenic Ras mutants G12D and G12C using QM/MM minimizations, matching the experimentally observed loss of catalytic activity, thereby validating our reaction mechanism. Using the optimized reaction paths, we devised a fast and accurate procedure to design GAP mutants that activate G12D Ras. We replaced GAP residues near the active site and determined the activation barrier for 190 single mutants. We furthermore built a machine learning for ultrafast screening, by fast prediction of the barrier heights, tested both on the single and double mutations. This work demonstrates that fast and accurate screening can be accomplished via QM/MM reaction path optimizations to design protein sequences with increased catalytic activity. Several GAP mutations are predicted to re-enable catalysis in oncogenic G12D, offering a promising avenue to overcome aberrant Ras-driven signal transduction by activating enzymatic activity instead of inhibition. The outlined computational screening protocol is readily applicable for designing ligands and cofactors analogously.

New Insights into the Cooperativity and Dynamics of Dimeric Enzymes

A survey of protein databases indicates that the majority of enzymes exist in oligomeric forms, with about half of those found in the UniProt database being homodimeric. Understanding why many enzymes are in their dimeric form is imperative. Recent developments in experimental and computational techniques have allowed for a deeper comprehension of the cooperative interactions between the subunits of dimeric enzymes. This review aims to succinctly summarize these recent advancements by providing an overview of experimental and theoretical methods, as well as an understanding of cooperativity in substrate binding and the molecular mechanisms of cooperative catalysis within homodimeric enzymes. Focus is set upon the beneficial effects of dimerization and cooperative catalysis. These advancements not only provide essential case studies and theoretical support for comprehending dimeric enzyme catalysis but also serve as a foundation for designing highly efficient catalysts, such as dimeric organic catalysts. Moreover, these developments have significant implications for drug design, as exemplified by Paxlovid, which was designed for the homodimeric main protease of SARS-CoV-2.

An expandable, modular de novo protein platform for precision redox engineering

The electron-conducting circuitry of life represents an as-yet untapped resource of exquisite, nanoscale biomolecular engineering. Here, we report the characterization and structure of a de novo diheme "maquette" protein, 4D2, which we subsequently use to create an expanded, modular platform for heme protein design. A well-folded monoheme variant was created by computational redesign, which was then utilized for the experimental validation of continuum electrostatic redox potential calculations. This demonstrates how fundamental biophysical properties can be predicted and fine-tuned. 4D2 was then extended into a tetraheme helical bundle, representing a 7 nm molecular wire. Despite a molecular weight of only 24 kDa, electron cryomicroscopy illustrated a remarkable level of detail, indicating the positioning of the secondary structure and the heme cofactors. This robust, expressible, highly thermostable and readily designable modular platform presents a valuable resource for redox protein design and the future construction of artificial electron-conducting circuitry.

Application of molecular dynamics simulation in the field of food enzymes: improving the thermal-stability and catalytic ability

Enzymes can produce high-quality food with low pollution, high function, high acceptability, and medical aid. However, most enzymes, in their native form, do not meet the industrial requirements. Sequence-based and structure-based methods are the two main strategies used for enzyme modification. Molecular Dynamics (MD) simulation is a sufficiently comprehensive technology, from a molecular perspective, which has been widely used for structure information analysis and enzyme modification. In this review, we summarize the progress and development of MD simulation, particularly for software, force fields, and a standard procedure. Subsequently, we review the application of MD simulation in various food enzymes for thermostability and catalytic improvement was reviewed in depth. Finally, the limitations and prospects of MD simulation in food enzyme modification research are discussed. This review highlights the significance of MD simulation and its prospects in food enzyme modification.

Second-Shell Residues Contribute to Catalysis by Predominately Preorganizing the Apo State in PafA

Residues beyond the first coordination shell are often observed to make considerable cumulative contributions in enzymes. Due to typically indirect perturbations of multiple physicochemical properties of the active site, however, their individual and specific roles in enzyme catalysis and disease-causing mutations remain difficult to predict and understand at the molecular level. Here we analyze the contributions of several second-shell residues in phosphate-irrepressible alkaline phosphatase of flavobacterium (PafA), a representative system as one of the most efficient enzymes. By adopting a multifaceted approach that integrates quantum-mechanical/molecular-mechanical free energy computations, molecular-mechanical molecular dynamics simulations, and density functional theory cluster model calculations, we probe the rate-limiting phosphoryl transfer step and structural properties of all relevant enzyme states. In combination with available experimental data, our computational results show that mutations of the studied second-shell residues impact catalytic efficiency mainly by perturbation of the apo state and therefore substrate binding, while they do not affect the ground state or alter the nature of phosphoryl transfer transition state significantly. Several second-shell mutations also modulate the active site hydration level, which in turn influences the energetics of phosphoryl transfer. These mechanistic insights also help inform strategies that may improve the efficiency of enzyme design and engineering by going beyond the current focus on the first coordination shell.

Selective Oxidation of Halophenols Catalyzed by an Artificial Miniaturized Peroxidase

The development of artificial enzymes for application in sustainable technologies, such as the transformation of environmental pollutants or biomass, is one of the most challenging goals in metalloenzyme design. In this work, we describe the oxidation of mono-, di-, tri- and penta-halogenated phenols catalyzed by the artificial metalloenzyme Fe-MC6*a. It promoted the dehalogenation of 4-fluorophenol into the corresponding 1,4-benzoquinone, while under the same experimental conditions, 4-chloro, 4-bromo and 4-iodophenol were selectively converted into higher molecular weight compounds. Analysis of the 4-chlorophenol oxidation products clarified that oligomers based on C-O bonds were exclusively formed in this case. All results show that Fe-MC6*a holds intriguing enzymatic properties, as it catalyzes halophenol oxidation with substrate-dependent chemoselectivity.

Design of Artificial Enzymes: Insights into Protein Scaffolds

The design of artificial enzymes has emerged as a promising tool for the generation of potent biocatalysts able to promote new-to-nature reactions with improved catalytic performances, providing a powerful platform for wide-ranging applications and a better understanding of protein functions and structures. The selection of an appropriate protein scaffold plays a key role in the design process. This review aims to give a general overview of the most common protein scaffolds that can be exploited for the generation of artificial enzymes. Several examples are discussed and categorized according to the strategy used for the design of the artificial biocatalyst, namely the functionalization of natural enzymes, the creation of a new catalytic site in a protein scaffold bearing a wide hydrophobic pocket and de novo protein design. The review is concluded by a comparison of these different methods and by our perspective on the topic.

Esterase Sequence Composition Patterns for the Identification of Catalytic Triad Microenvironment Motifs

Ester hydrolysis is of wide biomedical interest, spanning from the green synthesis of pharmaceuticals to biomaterials' development. Existing peptide-based catalysts exhibit low catalytic efficiency compared to natural enzymes, due to the conformational heterogeneity of peptides. Moreover, there is lack of understanding of the correlation between the primary sequence and catalytic function. For this purpose, we statistically analyzed 22 EC 3.1 hydrolases with known catalytic triads, characterized by unique and well-defined mechanisms. The aim was to identify patterns at the sequence level that will better inform the creation of short peptides containing important information for catalysis, based on the catalytic triad, oxyanion holes and the triad residues microenvironments. Moreover, fragmentation schemes of the primary sequence of selected enzymes alongside the study of their amino acid frequencies, composition, and physicochemical properties are proposed. The results showed highly conserved catalytic sites with distinct positional patterns and chemical microenvironments that favor catalysis and revealed variations in catalytic site composition that could be useful for the design of minimalistic catalysts.

Klebsiella pneumoniae Carbapenemase Variants Resistant to Ceftazidime-Avibactam: an Evolutionary Overview

First variants of the Klebsiella pneumoniae carbapenemase (KPC), KPC-2 and KPC-3, have encountered a worldwide success, particularly in K. pneumoniae isolates. These beta-lactamases conferred resistance to most beta-lactams including carbapenems but remained susceptible to new beta-lactam/beta-lactamase inhibitors, such as ceftazidime-avibactam. After the marketing of ceftazidime-avibactam, numerous variants of KPC resistant to this association have been described among isolates recovered from clinical samples or derived from experimental studies. In KPC variants resistant to ceftazidime-avibactam, point mutations, insertions and/or deletions have been described in various hot spots. Deciphering the impact of these mutations is crucial, not only from a therapeutic point of view, but also to follow the evolution in time and space of KPC variants resistant to ceftazidime-avibactam. In this review, we describe the mutational landscape of the KPC beta-lactamase toward ceftazidime-avibactam resistance based on a multidisciplinary approach including epidemiology, microbiology, enzymology, and thermodynamics. We show that resistance is associated with three hot spots, with a high representation of insertions and deletions compared with other class A beta-lactamases. Moreover, extension of resistance to ceftazidime-avibactam is associated with a trade-off in the resistance to other beta-lactams and a decrease in enzyme stability. Nevertheless, the high natural stability of KPC could underlay the propensity of this enzyme to acquire in vivo mutations conferring resistance to ceftazidime-avibactam (CAZavi), particularly via insertions and deletions.

Switch off/switch on of a cysteinyl protease as a way to preserve the active catalytic group by modification with a reversible covalent thiol modifier: Immobilization of ficin on vinyl-sulfone activated supports

The immobilization of ficin (a cysteinyl proteases) on vinyl sulfone agarose produced its almost full inactivation. It was observed that the incubation of the free and immobilized enzyme in β-mercaptoethanol produced a 20 % of enzyme activity recovery, suggesting that the inactivation due to the immobilization could be a consequence of the modification of the catalytic Cys. To prevent the enzyme inactivation during the immobilization, switching off of ficin via Cys reaction with dipyridyl-disulfide was implemented, giving a reversible disulfide bond that produced a fully inactive enzyme. The switch on of ficin activity was implemented by incubation in 1 M β-mercaptoethanol. Using this strategy to immobilize the enzyme on vinyl sulfone agarose beads, the expressed activity of the immobilized ficin could be boosted up to 80 %. The immobilized enzyme presented a thermal stabilization similar to that obtained using ficin-glyoxyl-agarose beads. This procedure may be extended to many enzymes containing critical Cys, to permit their immobilization or chemical modification.

Generalized Born Implicit Solvent Models Do Not Reproduce Secondary Structures of De Novo Designed Glu/Lys Peptides

We test a range of standard generalized Born (GB) models and protein force fields for a set of five experimentally characterized, designed peptides comprising alternating blocks of glutamate and lysine, which have been shown to differ significantly in α-helical content. Sixty-five combinations of force fields and GB models are evaluated in >800 μs of molecular dynamics simulations. GB models generally do not reproduce the experimentally observed α-helical content, and none perform well for all five peptides. These results illustrate that these models are not usefully predictive in this context. These peptides provide a useful test set for simulation methods.

Cross-linked enzyme aggregates immobilization: preparation, characterization, and applications

Enzymes are commonly used as biocatalysts for various biological and chemical processes. However, some major drawbacks of free enzymes (e.g. poor reusability and instability) significantly restrict their industrial practices. How to overcome these weaknesses remain considerable challenges. Enzyme immobilization is one of the most effective ways to improve the reusability and stability of enzymes. Cross-linked enzyme aggregates (CLEAs) has been known as a novel and versatile carrier-free immobilization method. CLEAs is attractive due to its simplicity and robustness, without purification. It generally shows: high catalytic specificity and selectivity, good operational and storage stabilities, and good reusability. Moreover, co-immobilization of different kinds of enzymes can be acquired. These CLEAs advantages provide opportunities for further industrial applications. Herein, the preparation parameters of CLEAs were first summarized. Next, characterization of structural and catalytic properties, stability and reusability are also proposed. Finally, some important applications of this technique in: environmental protection, industrial chemistry, food industry, and pharmaceutical synthesis and delivery are introduced. Potential challenges and future research directions, such as improving cross-linking efficiency and internal mass transfer efficiency, are also presented. This implies that CLEAs provide an efficient and feasible technique to improve the properties of enzymes for use in the industry.

Whole-cell Biotransformation of Penicillin G by a Three-enzyme Co-expression System with Engineered Deacetoxycephalosporin C Synthase

Deacetoxycephalosporin C synthase (DAOCS) catalyzes the tranformation of penicillin G to phenylacetyl-7-aminodeacetoxycephalosporanic acid (G-7-ADCA) in dependence on 2-oxoglutarate (2OG). However, the low activity of DAOCS and the expense of 2OG restricted the practical application in the production of G-7-ADCA. Herein, a rational design campaign was performed on a DAOCS from Streptomyces clavuligerus (scDAOCS) in the quest to construct novel expandases. The resulting mutants showed 25~58% increase in activity compared to the template. The dominant DAOCS variants were then embeded into a three-enzyme co-expression system, consisting of a catalase and a L-glutamic oxidase for the generation of 2OG, to convert penicillin G into G-7-ADCA in E. coli . The engineered whole-cell enzyme cascade was applied on an up scaled reaction, exhibiting a yield of G-7-ADCA up to 39.21 mM (14.6 g·L -1 ) with a conversion of 78.42 mol%. This work highlights the potential of the integrated whole-cell system that may inspire further research on green and efficient production of 7-ADCA.

The Influence of Codon Usage, Protein Abundance, and Protein Stability on Protein Evolution Vary by Evolutionary Distance and the Type of Protein

In general, the evolutionary rate of proteins is not primarily related to protein and amino acid functions, and factors such as protein abundance, codon usage, and the protein's T_M are more important. To better understand the factors that affect protein evolution, E. coli MG1655 orthologs were compared to those in closely related bacteria and to more distantly related prokaryotes, eukaryotes, and archaea. Also, the evolution of different types of proteins was studied. The analyses indicate that the amino acid conservation of enzymes that do not use macromolecules (e.g. DNA, RNA, and proteins) as substrates and that carry out metabolic processes involving small molecules (i.e. small molecule enzymes) is different than other enzymes. For example, the small molecule enzymes have a lower percent identity than other enzymes when sequences from closely related bacteria are compared. Analyses indicate the lower percent identity is not a result of the amino acid or codon usage of the small molecule enzymes. The small molecule enzymes also don't have a significantly lower protein abundance indicating that is also not likely an important factor driving differences in amino acid conservation. Analyses indicate different methods to measure the T_M of proteins have different relationships between amino acid conservation over different evolutionary distances. In totality, the results demonstrate that the relationship between the factors thought to affect protein evolution (protein abundance, codon usage, and proteins T_Ms) and protein evolution are complex and depend on the factor, the organisms, and the type of proteins being analyzed.

Optimized Hemolysin type 1 secretion system in Escherichia coli by directed evolution of the Hly enhancer fragment and including a terminator region

Type 1 secretion systems (T1SS) have a relatively simple architecture compared to other classes of secretion systems and therefore, are attractive to be optimized by protein engineering. Here, we report a KnowVolution campaign for the hemolysin (Hly) enhancer fragment, an untranslated region upstream of the hlyA gene, of the hemolysin T1SS of Escherichia coli to enhance its secretion efficiency. The best performing variant of the Hly enhancer fragment contained five nucleotide mutations at five positions (A30U, A36U, A54G, A81U, and A116U) resulted in a 2‐fold increase in the secretion level of a model lipase fused to the secretion carrier HlyA1. Computational analysis suggested that altered affinity to the generated enhancer fragment towards the S1 ribosomal protein contributes to the enhanced secretion levels. Furthermore, we demonstrate that involving a native terminator region along with the generated Hly enhancer fragment increased the secretion levels of the Hly system up to 5‐fold.

Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis

Autonomous computations that rely on automated reaction network elucidation algorithms may pave the way to make computational catalysis on a par with experimental research in the field. Several advantages of this approach are key to catalysis: (i) automation allows one to consider orders of magnitude more structures in a systematic and open-ended fashion than what would be accessible by manual inspection. Eventually, full resolution in terms of structural varieties and conformations as well as with respect to the type and number of potentially important elementary reaction steps (including decomposition reactions that determine turnover numbers) may be achieved. (ii) Fast electronic structure methods with uncertainty quantification warrant high efficiency and reliability in order to not only deliver results quickly, but also to allow for predictive work. (iii) A high degree of autonomy reduces the amount of manual human work, processing errors, and human bias. Although being inherently unbiased, it is still steerable with respect to specific regions of an emerging network and with respect to the addition of new reactant species. This allows for a high fidelity of the formalization of some catalytic process and for surprising in silico discoveries. In this work, we first review the state of the art in computational catalysis to embed autonomous explorations into the general field from which it draws its ingredients. We then elaborate on the specific conceptual issues that arise in the context of autonomous computational procedures, some of which we discuss at an example catalytic system.

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.

In vivo, in vitro and in silico: an open space for the development of microbe-based applications of synthetic biology

Living systems are studied using three complementary approaches: living cells, cell-free systems and computer-mediated modelling. Progresses in understanding, allowing researchers to create novel chassis and industrial processes rest on a cycle that combines in vivo, in vitro and in silico studies. This design-build-test-learn iteration loop cycle between experiments and analyses combines together physiology, genetics, biochemistry and bioinformatics in a way that keeps going forward. Because computer-aided approaches are not directly constrained by the material nature of the entities of interest, we illustrate here how this virtuous cycle allows researchers to explore chemistry which is foreign to that present in extant life, from whole chassis to novel metabolic cycles. Particular emphasis is placed on the importance of evolution.

Antibodies targeting enzyme inhibition as potential tools for research and drug development

Antibodies have transformed biomedical research and are now being used for different experimental applications. Generally, the interaction of enzymes with their specific antibodies can lead to a reduction in their enzymatic activity. The effect of the antibody is dependent on its narrow i.e. the regions of the enzyme to which it is directed. The mechanism of this inhibition is rarely a direct combination of the antibodies with the catalytic site, but is rather due to steric hindrance, barring the substrate access to the active site. In several systems, however, the interaction with the antibody induces conformational changes on the enzyme that can either inhibit or enhance its catalytic activity. The extent of enzyme inhibition or enhancement is, therefore, a reflection of the nature and distribution of the various antigenic determinants on the enzyme molecule. Currently, the mode of action of many enzymes has been elucidated at the molecular level. We here review the molecular mechanisms and recent trends by which antibodies inhibit the catalytic activity of enzymes and provide examples of how specific antibodies can be useful for the neutralization of biologically active molecules.

Modulating Glycoside Hydrolase Activity between Hydrolysis and Transfer Reactions Using an Evolutionary Approach

The proteins within the CAZy glycoside hydrolase family GH13 catalyze the hydrolysis of polysaccharides such as glycogen and starch. Many of these enzymes also perform transglycosylation in various degrees, ranging from secondary to predominant reactions. Identifying structural determinants associated with GH13 family reaction specificity is key to modifying and designing enzymes with increased specificity towards individual reactions for further applications in industrial, chemical, or biomedical fields. This work proposes a computational approach for decoding the determinant structural composition defining the reaction specificity. This method is based on the conservation of coevolving residues in spatial contacts associated with reaction specificity. To evaluate the algorithm, mutants of α-amylase (TmAmyA) and glucanotransferase (TmGTase) from Thermotoga maritima were constructed to modify the reaction specificity. The K98P/D99A/H222Q variant from TmAmyA doubled the transglycosydation/hydrolysis (T/H) ratio while the M279N variant from TmGTase increased the hydrolysis/transglycosidation ratio five-fold. Molecular dynamic simulations of the variants indicated changes in flexibility that can account for the modified T/H ratio. An essential contribution of the presented computational approach is its capacity to identify residues outside of the active center that affect the reaction specificity.

Intelligent host engineering for metabolic flux optimisation in biotechnology

Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.

Mini Review: Advances in 2-Haloacid Dehalogenases

The 2-haloacid dehalogenases (EC 3.8.1.X) are industrially important enzymes that catalyze the cleavage of carbon-halogen bonds in 2-haloalkanoic acids, releasing halogen ions and producing corresponding 2-hydroxyl acids. These enzymes are of particular interest in environmental remediation and environmentally friendly synthesis of optically pure chiral compounds due to their ability to degrade a wide range of halogenated compounds with astonishing efficiency for enantiomer resolution. The 2-haloacid dehalogenases have been extensively studied with regard to their biochemical characterization, protein crystal structures, and catalytic mechanisms. This paper comprehensively reviews the source of isolation, classification, protein structures, reaction mechanisms, biochemical properties, and application of 2-haloacid dehalogenases; current trends and avenues for further development have also been included.

Evolution of dynamical networks enhances catalysis in a designer enzyme

Activation heat capacity is emerging as a crucial factor in enzyme thermoadaptation, as shown by the non-Arrhenius behaviour of many natural enzymes. However, its physical origin and relationship to the evolution of catalytic activity remain uncertain. Here we show that directed evolution of a computationally designed Kemp eliminase reshapes protein dynamics, which gives rise to an activation heat capacity absent in the original design. These changes buttress transition-state stabilization. Extensive molecular dynamics simulations show that evolution results in the closure of solvent-exposed loops and a better packing of the active site. Remarkably, this gives rise to a correlated dynamical network that involves the transition state and large parts of the protein. This network tightens the transition-state ensemble, which induces a negative activation heat capacity and non-linearity in the activity-temperature dependence. Our results have implications for understanding enzyme evolution and suggest that selectively targeting the conformational dynamics of the transition-state ensemble by design and evolution will expedite the creation of novel enzymes.

Rigidifying a De Novo Enzyme Increases Activity and Induces a Negative Activation Heat Capacity

Conformational sampling profoundly impacts the overall activity and temperature dependence of enzymes. Peroxidases have emerged as versatile platforms for high-value biocatalysis owing to their broad palette of potential biotransformations. Here, we explore the role of conformational sampling in mediating activity in the de novo peroxidase C45. We demonstrate that 2,2,2-triflouoroethanol (TFE) affects the equilibrium of enzyme conformational states, tending toward a more globally rigid structure. This is correlated with increases in both stability and activity. Notably, these effects are concomitant with the emergence of curvature in the temperature-activity profile, trading off activity gains at ambient temperature with losses at high temperatures. We apply macromolecular rate theory (MMRT) to understand enzyme temperature dependence data. These data point to an increase in protein rigidity associated with a difference in the distribution of protein dynamics between the ground and transition states. We compare the thermodynamics of the de novo enzyme activity to those of a natural peroxidase, horseradish peroxidase. We find that the native enzyme resembles the rigidified de novo enzyme in terms of the thermodynamics of enzyme catalysis and the putative distribution of protein dynamics between the ground and transition states. The addition of TFE apparently causes C45 to behave more like the natural enzyme. Our data suggest robust, generic strategies for improving biocatalytic activity by manipulating protein rigidity; for functional de novo protein catalysts in particular, this can provide more enzyme-like catalysts without further rational engineering, computational redesign, or directed evolution.

A Brief History of De Novo Protein Design: Minimal, Rational, and Computational

Protein design has come of age, but how will it mature? In the 1980s and the 1990s, the primary motivation for de novo protein design was to test our understanding of the informational aspect of the protein-folding problem; i.e., how does protein sequence determine protein structure and function? This necessitated minimal and rational design approaches whereby the placement of each residue in a design was reasoned using chemical principles and/or biochemical knowledge. At that time, though with some notable exceptions, the use of computers to aid design was not widespread. Over the past two decades, the tables have turned and computational protein design is firmly established. Here, I illustrate this progress through a timeline of de novo protein structures that have been solved to atomic resolution and deposited in the Protein Data Bank. From this, it is clear that the impact of rational and computational design has been considerable: More-complex and more-sophisticated designs are being targeted with many being resolved to atomic resolution. Furthermore, our ability to generate and manipulate synthetic proteins has advanced to a point where they are providing realistic alternatives to natural protein functions for applications both in vitro and in cells. Also, and increasingly, computational protein design is becoming accessible to non-specialists. This all begs the questions: Is there still a place for minimal and rational design approaches? And, what challenges lie ahead for the burgeoning field of de novo protein design as a whole?

Machine Learning for Chemical Reactions

Machine learning (ML) techniques applied to chemical reactions have a long history. The present contribution discusses applications ranging from small molecule reaction dynamics to computational platforms for reaction planning. ML-based techniques can be particularly relevant for problems involving both computation and experiments. For one, Bayesian inference is a powerful approach to develop models consistent with knowledge from experiments. Second, ML-based methods can also be used to handle problems that are formally intractable using conventional approaches, such as exhaustive characterization of state-to-state information in reactive collisions. Finally, the explicit simulation of reactive networks as they occur in combustion has become possible using machine-learned neural network potentials. This review provides an overview of the questions that can and have been addressed using machine learning techniques, and an outlook discusses challenges in this diverse and stimulating field. It is concluded that ML applied to chemistry problems as practiced and conceived today has the potential to transform the way with which the field approaches problems involving chemical reactions, in both research and academic teaching.

Immobilization of the Peroxygenase from Agrocybe aegerita. The Effect of the Immobilization pH on the Features of an Ionically Exchanged Dimeric Peroxygenase

This paper outlines the immobilization of the recombinant dimeric unspecific peroxygenase from Agrocybe aegerita (rAaeUPO). The enzyme was quite stable (remaining unaltered its activity after 35 h at 47 °C and pH 7.0). Phosphate destabilized the enzyme, while glycerol stabilized it. The enzyme was not immobilized on glyoxyl-agarose supports, while it was immobilized albeit in inactive form on vinyl-sulfone-activated supports. rAaeUPO immobilization on glutaraldehyde pre-activated supports gave almost quantitative immobilization yield and retained some activity, but the biocatalyst was very unstable. Its immobilization via anion exchange on PEI supports also produced good immobilization yields, but the rAaeUPO stability dropped. However, using aminated agarose, the enzyme retained stability and activity. The stability of the immobilized enzyme strongly depended on the immobilization pH, being much less stable when rAaeUPO was adsorbed at pH 9.0 than when it was immobilized at pH 7.0 or pH 5.0 (residual activity was almost 0 for the former and 80% for the other preparations), presenting stability very similar to that of the free enzyme. This is a very clear example of how the immobilization pH greatly affects the final biocatalyst performance.

Allosteric communication in class A β-lactamases occurs via cooperative coupling of loop dynamics

Understanding allostery in enzymes and tools to identify it offer promising alternative strategies to inhibitor development. Through a combination of equilibrium and nonequilibrium molecular dynamics simulations, we identify allosteric effects and communication pathways in two prototypical class A β-lactamases, TEM-1 and KPC-2, which are important determinants of antibiotic resistance. The nonequilibrium simulations reveal pathways of communication operating over distances of 30 Å or more. Propagation of the signal occurs through cooperative coupling of loop dynamics. Notably, 50% or more of clinically relevant amino acid substitutions map onto the identified signal transduction pathways. This suggests that clinically important variation may affect, or be driven by, differences in allosteric behavior, providing a mechanism by which amino acid substitutions may affect the relationship between spectrum of activity, catalytic turnover, and potential allosteric behavior in this clinically important enzyme family. Simulations of the type presented here will help in identifying and analyzing such differences.