Specific host-guest interactions in a protein-based artificial transaminase

Dietmar Häring, Mark D. Distefano

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Artificial enzymes can be created by covalent attachment of a catalytic active group to a protein scaffold. Recently, we assembled an artificial transaminase by conjugation of intestinal fatty acid binding protein (IFABP) with a pyridoxamine derivative via a disulfide bond; the resulting constract catalyzed a transamination reaction 200-fold faster than free pyridoxamine. To identify the origin of this increased catalytic efficiency computer modeling was first used to identify two putative residues, Y14 and R126, that were in close proximity to the γ-carboxylate group of the substrate, α-ketoglutartate. These positions were mutated to phenylalanine and methionine, respectively, and used to prepare semisynthetic transaminases by conjugation to pyridoxamine (Px) or an N-methylated derivative (Px). Kinetic analysis of the resulting constructs showed that the R126 mutation reduced substrate affinity 3- to 6-fold while the additional Y14F mutation had a negligible effect. These results are consistent with a model for substrate recognition that involves an electrostatic interaction between the cationic guanidinium group of R126 and the anionic carboxylate from the substrate. Interestingly, one of the conjugates that contains an N-methylated pyridoxamine catalyzes a transamination reaction with a kcat we have thus far obtained and is 34-fold greater than that for the free cofactor in the absence of the protein.

Original languageEnglish (US)
Pages (from-to)2461-2466
Number of pages6
JournalBioorganic and Medicinal Chemistry
Volume9
Issue number9
DOIs
StatePublished - 2001

Bibliographical note

Funding Information:
We thank L. J. Banaszak and M. R. Lees for their support with the site-directed mutagenesis and D. P. Cistola for providing pMON-IFABPV60C. The Minnesota Supercomputing Institute provided computer time for the modeling. This work was supported by the National Science Foundation (CHE9807495) and a fellowship of the Deutsche Akademische Austauschdienst (NATO-Programm).

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