Ward, T.R.

J. Organomet. Chem. 2004, 689, 4868-4871, 10.1016/j.jorganchem.2004.09.032

We report on the phenomenon of protein-accelerated catalysis in the field of artificial metalloenzymes based on the non-covalent incorporation of biotinylated rhodium–diphosphine complexes in (strept)avidin as host proteins. By incrementally varying the [Rh(COD)(Biot-1)]+ vs. (strept)avidin ratio, we show that the enantiomeric excess of the produced acetamidoalanine decreases slowly. This suggests that the catalyst inside (strept)avidin is more active than the catalyst outside the host protein. Both avidin and streptavidin display protein-accelerated catalysis as the protein embedded catalyst display 12.0- and 3.0-fold acceleration over the background reaction with a catalyst devoid of protein. Thus, these artificial metalloenzymes display an increase both in activity and in selectivity for the reduction of acetamidoacrylic acid.

Creus, M.

Protein Expression Purif. 2011, 77, 131-139, 10.1016/j.pep.2011.01.003

The avidin–biotin technology has many applications, including molecular detection; immobilization; protein purification; construction of supramolecular assemblies and artificial metalloenzymes. Here we present the recombinant expression of novel biotin-binding proteins from bacteria and the purification and characterization of a secreted burkavidin from the human pathogen Burkholderia pseudomallei. Expression of the native burkavidin in Escherichia coli led to periplasmic secretion and formation of a biotin-binding, thermostable, tetrameric protein containing an intra-monomeric disulphide bond. Burkavidin showed one main species as measured by isoelectric focusing, with lower isoelectric point (pI) than streptavidin. To exemplify the potential use of burkavidin in biotechnology, an artificial metalloenzyme was generated using this novel protein-scaffold and shown to exhibit enantioselectivity in a rhodium-catalysed hydrogenation reaction.

Novič, M.

Mol. Divers. 2007, 11, 141-152, 10.1007/s11030-008-9068-x

The counter propagation artificial neural networks (CP-ANNs) were used to develop a quantitative structure-selectivity relationship (QSSR) for a set of artificial metalloenzymes. The artificial metalloenzymes consist of biotinylated rhodium-diphosphine complexes incorporated in streptavidin mutants acting as host protein. Such hybrid catalysts have been shown to be good enantioselective hydrogenation catalysts for acetamidoacrylic acid. The descriptor-based models were constructed to predict enantiomeric excess (%ee) on the basis of the catalyst structures originating from docking simulations. 3D molecular descriptors for the docked ligands structures were computed. The relative arrangement of guest and host molecules was coded using distance descriptors (Rh-Cα interatomic distances); the diversity of the mutant proteins at the position S112 was coded with molecular descriptors for the sequence of three neighboring amino acids (T111-S112X-G113). The selection of testing samples for the external model validation was based on the Kohonen mapping. The final model trained by two thirds of the entire dataset was characterized by satisfactory statistical parameters for the external test set (R = 0.953 and RMS = 16.8 %ee). The proposed procedure of docking-based descriptor generation thus appears as a promising alternative to the full characterization of the complex structure by experimental or computational methods.

Jarvis, A.G.; Kamer, P.C.J.

Angew. Chem. Int. Ed. 2017, 129, 13784-13788, 10.1002/ange.201705753