ArticleProtein engineering, design & selection : PEDS2021
Thermostable D-amino acid decarboxylases derived from Thermotoga maritima diaminopimelate decarboxylase.
Article in Protein engineering, design & selection : PEDS, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Elucidation of D-amino acid metabolic activity of ornithine decarboxylase from the hyperthermophile Thermotoga maritima.Extremophiles : life under extreme conditions · 2026Article
- Kinetic and homology model analysis of diaminopimelate decarboxylase from Cyanothece sp. ATCC 51142: unveiling a key enzyme in lysine biosynthesis.Bioscience reports · 2025Article
- Diaminopimelic Acid Metabolism byMicrobiology spectrum · 2022Article
- Computational Prediction of ω-Transaminase Specificity by a Combination of Docking and Molecular Dynamics Simulations.Journal of chemical information and modeling · 2021Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diaminopimelate decarboxylases (DAPDCs) are highly selective enzymes that catalyze the common final step in different lysine biosynthetic pathways, i.e. the conversion of meso-diaminopimelate (DAP) to L-lysine. We examined the modification of the substrate specificity of the thermostable decarboxylase from Thermotoga maritima with the aim to introduce activity with 2-aminopimelic acid (2-APA) since its decarboxylation leads to 6-aminocaproic acid (6-ACA), a building block for the synthesis of nylon-6. Structure-based mutagenesis of the distal carboxylate binding site resulted in a set of enzyme variants with new activities toward different D-amino acids. One of the mutants (E315T) had lost most of its activity toward DAP and primarily acted as a 2-APA decarboxylase. We next used computational modeling to explain the observed shift in catalytic activities of the mutants. The results suggest that predictive computational protocols can support the redesign of the catalytic properties of this class of decarboxylating PLP-dependent enzymes.
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