Elucidating Substrate Promiscuity within the FabI Enzyme Family

Reducing the vicissitudes of heterologous prochiral substrate catalysis by alcohol dehydrogenases through machine learning algorithms

Alcohol dehydrogenases (ADHs) are popular catalysts for synthesizing chiral synthons a vital step for active pharmaceutical intermediate (API) production. They are grouped into three superfamilies namely, medium-chain (MDRs), short-chain dehydrogenase/reductases (SDRs), and iron-containing alcohol dehydrogenases. The former two are used extensively for producing various chiral synthons. Many studies screen multiple enzymes or engineer a specific enzyme for catalyzing a substrate of interest. These processes are resource-intensive and intricate. The current study attempts to decipher the ability to match different ADHs with their ideal substrates using machine learning algorithms. We explore the catalysis of 284 antibacterial ketone intermediates, against MDRs and SDRs to demonstrate a unique pattern of activity. To facilitate machine learning we curated a dataset comprising 33 features, encompassing 4 descriptors for each compound. Subsequently, an ensemble of machine learning techniques viz. Partial Least Squares (PLS) regression, k-Nearest Neighbors (kNN) regression, and Support Vector Machine (SVM) regression, was harnessed. Moreover, the assimilation of Principal Component Analysis (PCA) augmented precision and accuracy, thereby refining and demarcating diverse compound classes. As such, this classification is useful for discerning substrates amenable to diverse alcohol dehydrogenases, thereby mitigating the reliance on high-throughput screening or engineering in identifying the optimal enzyme for specific substrate.

Asymmetric Ene-Reduction of α,β-Unsaturated Compounds by F420-Dependent Oxidoreductases A Enzymes from Mycobacterium smegmatis

The stereoselective reduction of alkenes conjugated to electron-withdrawing groups by ene-reductases has been extensively applied to the commercial preparation of fine chemicals. Although several different enzyme families are known to possess ene-reductase activity, the old yellow enzyme (OYE) family has been the most thoroughly investigated. Recently, it was shown that a subset of ene-reductases belonging to the flavin/deazaflavin oxidoreductase (FDOR) superfamily exhibit enantioselectivity that is generally complementary to that seen in the OYE family. These enzymes belong to one of several FDOR subgroups that use the unusual deazaflavin cofactor F₄₂₀. Here, we explore several enzymes of the FDOR-A subgroup, characterizing their substrate range and enantioselectivity with 20 different compounds, identifying enzymes (MSMEG_2027 and MSMEG_2850) that could reduce a wide range of compounds stereoselectively. For example, MSMEG_2027 catalyzed the complete conversion of both isomers of citral to (R)-citronellal with 99% ee, while MSMEG_2850 catalyzed complete conversion of ketoisophorone to (S)-levodione with 99% ee. Protein crystallography combined with computational docking has allowed the observed stereoselectivity to be mechanistically rationalized for two enzymes. These findings add further support for the FDOR and OYE families of ene-reductases displaying general stereocomplementarity to each other and highlight their potential value in asymmetric ene-reduction.

EP-Pred: A Machine Learning Tool for Bioprospecting Promiscuous Ester Hydrolases

When bioprospecting for novel industrial enzymes, substrate promiscuity is a desirable property that increases the reusability of the enzyme. Among industrial enzymes, ester hydrolases have great relevance for which the demand has not ceased to increase. However, the search for new substrate promiscuous ester hydrolases is not trivial since the mechanism behind this property is greatly influenced by the active site’s structural and physicochemical characteristics. These characteristics must be computed from the 3D structure, which is rarely available and expensive to measure, hence the need for a method that can predict promiscuity from sequence alone. Here we report such a method called EP-pred, an ensemble binary classifier, that combines three machine learning algorithms: SVM, KNN, and a Linear model. EP-pred has been evaluated against the Lipase Engineering Database together with a hidden Markov approach leading to a final set of ten sequences predicted to encode promiscuous esterases. Experimental results confirmed the validity of our method since all ten proteins were found to exhibit a broad substrate ambiguity.

Stabilization of enzymes via immobilization: Multipoint covalent attachment and other stabilization strategies

The use of enzymes in industrial processes requires the improvement of their features in many instances. Enzyme immobilization, a requirement to facilitate the recovery and reuse of these water-soluble catalysts, is one of the tools that researchers may utilize to improve many of their properties. This review is focused on how enzyme immobilization may improve enzyme stability. Starting from the stabilization effects that an enzyme may experience by the mere fact of being inside a solid particle, we detail other possibilities to stabilize enzymes: generation of favorable enzyme environments, prevention of enzyme subunit dissociation in multimeric enzymes, generation of more stable enzyme conformations, or enzyme rigidification via multipoint covalent attachment. In this last point, we will discuss the features of an "ideal" immobilization protocol to maximize the intensity of the enzyme-support interactions. The most interesting active groups in the support (glutaraldehyde, epoxide, glyoxyl and vinyl sulfone) will be also presented, discussing their main properties and uses. Some instances in which the number of enzyme-support bonds is not directly related to a higher stabilization will be also presented. Finally, the possibility of coupling site-directed mutagenesis or chemical modification to get a more intense multipoint covalent immobilization will be discussed.

Taking Advantage of Promiscuity of Cold-Active Enzymes

Cold-active enzymes increase their catalytic efficiency at low-temperature, introducing structural flexibility at or near the active sites. Inevitably, this feat seems to be accompanied by lower thermal stability. These characteristics have made cold-active enzymes into attractive targets for the industrial applications, since they could reduce the energy cost in the reaction, attenuate side-reactions, and simply be inactivated. In addition, the increased structural flexibility could result in broad substrate specificity for various non-native substrates, which is called substrate promiscuity. In this perspective, we deal with a less addressed aspect of cold-active enzymes, substrate promiscuity, which has enormous potential for semi-synthesis or enzymatic modification of fine chemicals and drugs. Further structural and directed-evolutional studies on substrate promiscuity of cold-active enzymes will provide a new workhorse in white biotechnology.

The 3-ketoacyl-CoA thiolase: an engineered enzyme for carbon chain elongation of chemical compounds

Because of their function of catalyzing the rearrangement of the carbon chains, thiolases have attracted increasing attentions over the past decades. The 3-ketoacyl-CoA thiolase (KAT) is a member of the thiolase, which is capable of catalyzing the Claisen condensation reaction between the two acyl-CoAs, thereby achieving carbon chain elongation. In this way, diverse value-added compounds might be synthesized starting from simple small CoA thioesters. However, most KATs are hampered by low stability and poor substrate specificity, which has hindered the development of large-scale biosynthesis. In this review, the common characteristics in the three-dimensional structure of KATs from different sources are summarized. Moreover, structure-guided rational engineering is discussed as a strategy for enhancing the performance of KATs. Finally, we reviewed the metabolic engineering applications of KATs for producing various energy-storage molecules, such as n-butanol, fatty acids, dicarboxylic acids, and polyhydroxyalkanoates. KEY POINTS: • Summarize the structural characteristics and catalyzation mechanisms of KATs. • Review on the rational engineering to enhance the performance of KATs. • Discuss the applications of KATs for producing energy-storage molecules.

Ligand- and Structure-Based Approaches of Escherichia coli FabI Inhibition by Triclosan Derivatives: From Chemical Similarity to Protein Dynamics Influence

Enoyl‐acyl carrier protein reductase (FabI) is the limiting step to complete the elongation cycle in type II fatty acid synthase (FAS) systems and is a relevant target for antibacterial drugs. E. coli FabI has been employed as a model to develop new inhibitors against FAS, especially triclosan and diphenyl ether derivatives. Chemical similarity models (CSM) were used to understand which features were relevant for FabI inhibition. Exhaustive screening of different CSM parameter combinations featured chemical groups, such as the hydroxy group, as relevant to distinguish between active/decoy compounds. Those chemical features can interact with the catalytic Tyr156. Further molecular dynamics simulation of FabI revealed the ionization state as a relevant for ligand stability. Also, our models point the balance between potency and the occupancy of the hydrophobic pocket. This work discusses the strengths and weak points of each technique, highlighting the importance of complementarity among approaches to elucidate EcFabI inhibitor's binding mode and offers insights for future drug discovery.

FabG: from a core to circumstantial catalyst

Core biochemical pathways such as Fatty-acid synthesis II (FAS II) is ascribed to the synthesis of fatty-acids, biotin and lipoic acid in prokaryotes. It has two dehydrogenases namely, FabG and FabI which interact with the fatty-acid chain bound to Acyl-carrier protein (ACP), a well-studied enzyme which binds to substrates of varying lengths. This protein-protein interaction 'broadens' the active site of these dehydrogenases thus, contributing to their flexible nature. This property is exploited for catalysing numerous chiral synthons, alkanes, long-chain alcohols and secondary metabolites in industries especially with FabG. FASI relegates FASII in eukaryotes making it a 'relic gene pool' and an antibacterial drug target with diverse inhibitor and substrate markush. FabG often substitutes other dehydrogenases for producing secondary metabolites in nature. This redundancy is probably due to gene duplication or addition events possibly making FabG, a progenitor to some of the complex short-chain dehydrogenases used in organisms and industries today.

Protein engineers turned evolutionists-the quest for the optimal starting point

The advent of laboratory directed evolution yielded a fruitful crosstalk between the disciplines of molecular evolution and bio-engineering. Here, we outline recent developments in both disciplines with respect to how one can identify the best starting points for directed evolution, such that highly efficient and robust tailor-made enzymes can be obtained with minimal optimization. Directed evolution studies have highlighted essential features of engineer-able enzymes: highly stable, mutationally robust enzymes with the capacity to accept a broad range of substrates. Robust, evolvable enzymes can be inferred from the natural sequence record. Broad substrate spectrum relates to conformational plasticity and can also be predicted by phylogenetic analyses and/or by computational design. Overall, an increasingly powerful toolkit is becoming available for identifying optimal starting points including network analyses of enzyme superfamilies and other bioinformatics methods.

Designing artificial metabolic pathways, construction of target enzymes, and analysis of their function

Artificial design of metabolic pathways is essential for the production of useful compounds using microbes. Based on this design, heterogeneous genes are introduced into the host, and then various analysis and evaluation methods are conducted to ensure that the target enzyme reactions are functionalized within the cell. In this chapter, we list successful examples of useful compounds produced by designing artificial metabolic pathways, and describe the methods involved in analyzing, evaluating, and optimizing the target enzyme reaction.

Combination of type II fatty acid biosynthesis enzymes and thiolases supports a functional β-oxidation reversal

An engineered reversal of the β-oxidation cycle (r-BOX) and the fatty acid biosynthesis (FAB) pathway are promising biological platforms for advanced fuel and chemical production in part due to their iterative nature supporting the synthesis of various chain length products. While diverging in their carbon-carbon elongation reaction mechanism, iterative operation of each pathway relies on common chemical conversions (reduction, dehydration, and reduction) differing only in the attached moiety (acyl carrier protein (ACP) in FAB vs Coenzyme A in r-BOX). Given this similarity, we sought to determine whether FAB enzymes can be used in the context of r-BOX as a means of expanding available r-BOX components with a ubiquitous set of well characterized enzymes. Using enzymes from the type II FAB pathway (FabG, FabZ, and FabI) in conjunction with a thiolase catalyzing a non-decarboxylative condensation, we demonstrate that FAB enzymes support a functional r-BOX. Pathway operation with FAB enzymes was improved through computationally directed protein design to develop FabZ variants with amino acid substitutions designed to disrupt hydrogen bonding at the FabZ-ACP interface and introduce steric and electrostatic repulsion between the FabZ and ACP. FabZ with R126W and R121E substitutions resulted in improved carboxylic acid and alcohol production from one- and multiple-turn r-BOX compared to the wild-type enzyme. Furthermore, the ability for FAB enzymes to operate on functionalized intermediates was exploited to produce branched chain carboxylic acids through an r-BOX with functionalized priming. These results not only provide an expanded set of enzymes within the modular r-BOX pathway, but can also potentially expand the scope of products targeted through this pathway by operating with CoA intermediates containing various functional groups.