Growth-Based, High-Throughput Selection for NADH Preference in an Oxygen-Dependent Biocatalyst

Engineering new-to-nature biochemical conversions by combining fermentative metabolism with respiratory modules

Anaerobic microbial fermentations provide high product yields and are a cornerstone of industrial bio-based processes. However, the need for redox balancing limits the array of fermentable substrate-product combinations. To overcome this limitation, here we design an aerobic fermentative metabolism that allows the introduction of selected respiratory modules. These can use oxygen to re-balance otherwise unbalanced fermentations, hence achieving controlled respiro-fermentative growth. Following this design, we engineer and characterize an obligate fermentative Escherichia coli strain that aerobically ferments glucose to stoichiometric amounts of lactate. We then re-integrate the quinone-dependent glycerol 3-phosphate dehydrogenase and demonstrate glycerol fermentation to lactate while selectively transferring the surplus of electrons to the respiratory chain. To showcase the potential of this fermentation mode, we direct fermentative flux from glycerol towards isobutanol production. In summary, our design permits using oxygen to selectively re-balance fermentations. This concept is an advance freeing highly efficient microbial fermentation from the limitations imposed by traditional redox balancing.

Automated in vivo enzyme engineering accelerates biocatalyst optimization

Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. Their realization through in vitro enzyme characterization and engineering is mostly low throughput and labor-intensive. Therefore, strategies for increasing throughput while diminishing manual labor are gaining momentum, such as in vivo screening and evolution campaigns. Computational tools like machine learning further support enzyme engineering efforts by widening the explorable design space. Here, we propose an integrated solution to enzyme engineering challenges whereby ML-guided, automated workflows (including library generation, implementation of hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection) could be realized to accelerate pipelines towards superior biocatalysts.

Growth-coupled enzyme engineering through manipulation of redox cofactor regeneration

Enzymes need to be efficient, robust, and highly specific for their effective use in commercial bioproduction. These properties can be introduced using various enzyme engineering techniques, with random mutagenesis and directed evolution (DE) often being chosen when there is a lack of structural information -or mechanistic understanding- of the enzyme. The screening or selection step of DE is the limiting part of this process, since it must ideally be (ultra)-high throughput, specifically target the catalytic activity of the enzyme and have an accurately quantifiable metric for said activity. Growth-coupling selection strategies involve coupling a desired enzyme activity to cellular metabolism and therefore growth, where growth (rate) becomes the output metric. Redox cofactors (NAD⁺/NADH and NADP⁺/NADPH) have recently been identified as promising target molecules for growth coupling, owing to their essentiality for cellular metabolism and ubiquitous nature. Redox cofactor oxidation or reduction can be disrupted through metabolic engineering and the use of specific culturing conditions, rendering the cell inviable unless a 'rescue' reaction complements the imposed metabolic deficiency. Using this principle, enzyme variants displaying improved cofactor oxidation or reduction rates can be selected for through an increased growth rate of the cell. In recent years, several E. coli strains have been developed that are deficient in the oxidation or reduction of NAD⁺/NADH and NADP⁺/NADPH pairs, and of non-canonical redox cofactor pairs NMN⁺/NMNH and NCD⁺/NCDH, which provides researchers with a versatile toolbox of enzyme engineering platforms. A range of redox cofactor dependent enzymes have since been engineered using a variety of these strains, demonstrating the power of using this growth-coupling technique for enzyme engineering. This review aims to summarize the metabolic engineering involved in creating strains auxotrophic for the reduced or oxidized state of redox cofactors, and the resulting successes in using them for enzyme engineering. Perspectives on the unique features and potential future applications of this technique are also presented.

Dual-channel glycolysis balances cofactor supply for l-homoserine biosynthesis in Corynebacterium glutamicum

l-Homoserine is an important platform compound that is widely used to produce many valuable bio-based products, but production of l-homoserine in Corynebacterium glutamicum remains low. In this study, an efficient l-homoserine-producing strain was constructed. Native pentose phosphate pathway (PPP) was enhanced and heterologous Entner-Doudoroff (ED) pathway was carefully introduced into l-homoserine-producing strain, which increased the l-homoserine titer. Coexpression of NADH-dependent aspartate-4-semialdehyde dehydrogenase and aspartate dehydrogenase could increase the titer from 11.3 to 13.3 g/L. Next, NADP⁺-dependent glyceraldehyde-3-phosphate dehydrogenase (NADP-GPD) was coexpressed with that of NAD⁺-dependent (NAD-GPD) to construct dual-channel glycolysis for balance of intracellular cofactors, which increased the l-homoserine titer by 48.6 % to 16.8 g/L. Finally, engineered strain Cg18-1 accumulated 63.5 g/L l-homoserine after 96 h in a 5 L bioreactor, the highest titer reported to date for C. glutamicum. This dual-channel glycolysis strategy provides a reference for automatic cofactor regulation to promote efficient biosynthesis of other target products.

Logistic Regression-Guided Identification of Cofactor Specificity-Contributing Residues in Enzyme with Sequence Datasets Partitioned by Catalytic Properties

Changing the substrate/cofactor specificity of an enzyme requires multiple mutations at spatially adjacent positions around the substrate pocket. However, this is challenging when solely based on crystal structure information because enzymes undergo dynamic conformational changes during the reaction process. Herein, we proposed a method for estimating the contribution of each amino acid residue to substrate specificity by deploying a phylogenetic analysis with logistic regression. Since this method can estimate the candidate amino acids for mutation by ranking, it is readable and can be used in protein engineering. We demonstrated our concept using redox cofactor conversion of the Escherichia coli malic enzyme as a model, which still lacks crystal structure elucidation. The use of logistic regression with amino acid sequences classified by cofactor specificity showed that the NADP⁺-dependent malic enzyme completely switched cofactor specificity to NAD⁺ dependence without the need for a practical screening step. The model showed that surrounding residues made a greater contribution to cofactor specificity than those in the interior of the substrate pocket. These residues might be difficult to identify from crystal structure observations. We show that a highly accurate and inferential machine learning model was obtained using amino acid sequences of structurally homologous and functionally distinct enzymes as input data.

Orthogonal glycolytic pathway enables directed evolution of noncanonical cofactor oxidase

Noncanonical cofactor biomimetics (NCBs) such as nicotinamide mononucleotide (NMN+) provide enhanced scalability for biomanufacturing. However, engineering enzymes to accept NCBs is difficult. Here, we establish a growth selection platform to evolve enzymes to utilize NMN+-based reducing power. This is based on an orthogonal, NMN+-dependent glycolytic pathway in Escherichia coli which can be coupled to any reciprocal enzyme to recycle the ensuing reduced NMN+. With a throughput of >106 variants per iteration, the growth selection discovers a Lactobacillus pentosus NADH oxidase variant with ~10-fold increase in NMNH catalytic efficiency and enhanced activity for other NCBs. Molecular modeling and experimental validation suggest that instead of directly contacting NCBs, the mutations optimize the enzyme's global conformational dynamics to resemble the WT with the native cofactor bound. Restoring the enzyme's access to catalytically competent conformation states via deep navigation of protein sequence space with high-throughput evolution provides a universal route to engineer NCB-dependent enzymes.

Directed evolution of phosphite dehydrogenase to cycle noncanonical redox cofactors via universal growth selection platform

Noncanonical redox cofactors are attractive low-cost alternatives to nicotinamide adenine dinucleotide (phosphate) (NAD(P)+) in biotransformation. However, engineering enzymes to utilize them is challenging. Here, we present a high-throughput directed evolution platform which couples cell growth to the in vivo cycling of a noncanonical cofactor, nicotinamide mononucleotide (NMN+). We achieve this by engineering the life-essential glutathione reductase in Escherichia coli to exclusively rely on the reduced NMN+ (NMNH). Using this system, we develop a phosphite dehydrogenase (PTDH) to cycle NMN+ with ~147-fold improved catalytic efficiency, which translates to an industrially viable total turnover number of ~45,000 in cell-free biotransformation without requiring high cofactor concentrations. Moreover, the PTDH variants also exhibit improved activity with another structurally deviant noncanonical cofactor, 1-benzylnicotinamide (BNA+), showcasing their broad applications. Structural modeling prediction reveals a general design principle where the mutations and the smaller, noncanonical cofactors together mimic the steric interactions of the larger, natural cofactors NAD(P)+.

Cloning and characterization of four enzymes responsible for cyclohexylamine degradation from Paenarthrobacter sp. TYUT067

Paenarthrobacter sp. TYUT067 is a soil bacterium that can degrade and use cyclohexylamine as the sole source of carbon and energy. However, the responsible enzymes involved in cyclohexylamine degradation by TYUT067 have not been cloned and characterized in detail yet. In this study, four possible cyclohexylamine degradation genes, one cyclohexylamine oxidase (Pachao), two cyclohexanone monooxygenases (Pachms) and one lactone hydrolase (Pamlh) were successfully cloned and heterologous expressed in Escherichia coli T7 host cells. The four enzymes were purified and characterized. The optimal pH and temperature of the purified enzymes toward their own substrates were 7.0 (PaCHAO), 8.0 (PaCHM1), 9.0 (PaCHM2 and PaMLH) and 30 °C (PaCHAO and PaMLH), 40 °C (PaCHM2) and 45 °C (PaCHM1), respectively, with K_M of 1.1 mM (PaCHAO), 0.1 mM (PaCHM1), 0.1 mM (PaCHM2) and 0.8 mM (PaMLH), and yielding a catalytic efficiency k_cat/K_M of 16.1 mM^-1 s^-1 (PaCHAO), 1.0 mM^-1 s^-1 (PaCHM1), 5.0 mM^-1 s^-1 (PaCHM2) and 124.4 mM^-1 s^-1 (PaMLH). In vitro mimicking the cyclohexylamine degradation pathway was conducted by using the combined three cyclohexylamine degradation enzymes (PaCHAO, PaCHM2 and PaMLH) with 10-50 mM cyclohexylamine, 100% conversion of cyclohexylamine could be finished within 12 h without any detected intermediates. The current study confirmed the enzymes responsible for cyclohexylamine degradation in TYUT067 for the first time, provide basic information for further investigation and application of these specific enzymes in pollution control.