Thermostable D-amino acid decarboxylases derived from Thermotoga maritima diaminopimelate decarboxylase

Metabolic engineering combined with site-directed saturated mutations of α-keto acid decarboxylase for efficient production of 6-aminocaproic acid and 1,6-hexamethylenediamine

6-Aminocaproic acid (6ACA) and 1,6-hexamethylenediamine (HMDA) are key precursors for nylon synthesis, and both are produced using petroleum-based chemical processes. However, the utilization of bio-based raw materials for biological production of monomers is crucial for nylon industry. In this study, we demonstrated that metabolic engineering of Escherichia coli and selected mutations of α-keto acid decarboxylase successfully synthesized 6ACA and HMDA. An artificial iterative cycle from l-lysine to chain-extended α-ketoacids was introduced into Escherichia coli BL21 (DE3). Then, the extended α-ketoacids were decarboxylated and oxidized for 6ACA production. Overexpression of catalase (KatE) combined with the site-directed mutations of α-isopropylmalate synthase (LeuA) contributed synergistic enhancement effect on synthesis of 6ACA, resulting in a 1.3-fold increase in 6ACA titer. Selected mutations in α-keto acid decarboxylase (KivD) improved its specificity and 170.00 ± 5.57 mg/L of 6ACA with a yield of 0.13 mol/mol (6ACA/l-lysine hydrochloride) was achieved by shake flask cultivation of the engineered strain with the KivD# (F381Y/V461I). Meanwhile, the engineered E. coli could accumulate 84.67 ± 4.04 mg/L of HMDA with a yield of 0.08 mol/mol (HMDA/l-lysine hydrochloride) by replacing aldehyde dehydrogenase with bi-aminotransferases. This achievement marks a significant advancement in the biological synthesis of 6-carbon compounds, since the biosynthetic pathways of HMDA are rarely identified.

Multifunctional enzymes related to amino acid metabolism in bacteria

In bacteria, d-amino acids are primarily synthesized from l-amino acids by amino acid racemases, but some bacteria use d-amino acid aminotransferases to synthesize d-amino acids. d-Amino acids are peptidoglycan components in the cell wall involved in several physiological processes, such as bacterial growth, biofilm dispersal, and peptidoglycan metabolism. Therefore, their metabolism and physiological roles have attracted increasing attention. Recently, we identified novel bacterial d-amino acid metabolic pathways, which involve amino acid racemases, with broad substrate specificity, as well as multifunctional enzymes with d-amino acid-metabolizing activity. Here, I review these multifunctional enzymes and their related d- and l-amino acid metabolic pathways in Escherichia coli and the hyperthermophile Thermotoga maritima.

Diaminopimelic Acid Metabolism by Pseudomonadota in the Ocean

Diaminopimelic acid (DAP) is a unique component of the cell wall of Gram-negative bacteria. It is also an important component of organic matter and is widely utilized by microbes in the world's oceans. However, neither DAP concentrations nor marine DAP-utilizing microbes have been investigated. Here, DAP concentrations in seawater were measured and the diversity of marine DAP-utilizing bacteria and the mechanisms for their DAP metabolism were investigated. Free DAP concentrations in seawater, from surface to a 5,000 m depth, were found to be between 0.61 μM and 0.96 μM in the western Pacific Ocean. DAP-utilizing bacteria from 20 families in 4 phyla were recovered from the western Pacific seawater and 14 strains were further isolated, in which Pseudomonadota bacteria were dominant. Based on genomic and transcriptomic analyses combined with gene deletion and in vitro activity detection, DAP decarboxylase (LysA), which catalyzes the decarboxylation of DAP to form lysine, was found to be a key and specific enzyme involved in DAP metabolism in the isolated Pseudomonadota strains. Interrogation of the Tara Oceans database found that most LysA-like sequences (92%) are from Pseudomonadota, which are widely distributed in multiple habitats. This study provides an insight into DAP metabolism by marine bacteria in the ocean and contributes to our understanding of the mineralization and recycling of DAP by marine bacteria. IMPORTANCE DAP is a unique component of peptidoglycan in Gram-negative bacterial cell walls. Due to the large number of marine Gram-negative bacteria, DAP is an important component of marine organic matter. However, it remains unclear how DAP is metabolized by marine microbes. This study investigated marine DAP-utilizing bacteria by cultivation and bioinformational analysis and examined the mechanism of DAP metabolism used by marine bacteria. The results demonstrate that Pseudomonadota bacteria are likely to be an important DAP-utilizing group in the ocean and that DAP decarboxylase is a key enzyme involved in DAP metabolism. This study also sheds light on the mineralization and recycling of DAP driven by bacteria.

Computational Prediction of ω-Transaminase Specificity by a Combination of Docking and Molecular Dynamics Simulations

ω-Transaminases (ω-TAs) catalyze the conversion of ketones to chiral amines, often with high enantioselectivity and specificity, which makes them attractive for industrial production of chiral amines. Tailoring ω-TAs to accept non-natural substrates is necessary because of their limited substrate range. We present a computational protocol for predicting the enantioselectivity and catalytic selectivity of an ω-TA from Vibrio fluvialis with different substrates and benchmark it against 62 compounds gathered from the literature. Rosetta-generated complexes containing an external aldimine intermediate of the transamination reaction are used as starting conformations for multiple short independent molecular dynamics (MD) simulations. The combination of molecular docking and MD simulations ensures sufficient and accurate sampling of the relevant conformational space. Based on the frequency of near-attack conformations observed during the MD trajectories, enantioselectivities can be quantitatively predicted. The predicted enantioselectivities are in agreement with a benchmark dataset of experimentally determined ee% values. The substrate-range predictions can be based on the docking score of the external aldimine intermediate. The low computational cost required to run the presented framework makes it feasible for use in enzyme design to screen thousands of enzyme variants.