Bridging the gap between homogenous and biological catalysis has become a constantly growing solution for sustainable chemistry. In this field, one of the rapidly growing approaches consists in physically merging transition metal homogenous catalysts within a biomolecular host. In the resulting hybrids, also called artificial metalloenzymes, the cofactor (synthetic) provides most of the catalytic specificity, whereas the protein environment generates and asymmetric second coordination sphere that dictates substrate, regio- and enantioselectivities.1 The successful development of artificial metalloenzymes stands on the quality of the molecular partnership between inorganic and biological partners in term of binding and catalytic control, and molecular information is fundamental in almost any aspect of the process. Computational methods offer an interesting alternative to reach atomic details on the mechanism of artificial metalloenzymes and in the help in their design and optimization.2 However, they are also of the most complicated processes molecular modeling has to deal with due to the complexity to explore simultaneously a very vast conformational space (metal mediated conformational changes, identification of substrate and cofactor location) and fine electronic effects (optimization of the coordination environment of the metal, characterization of transition state structures, discrimination between resting states and active forms of the enzymes, etc.). By applying an integrative computational framework, combining protein-ligand docking, QM and QM/MM approaches,3 the group has investigated the mechanistic behavior of enantioselective artificial imine reductases4 and metallo-hydratases. The results will be described. (1) Nanda, V.; Koder, R. L. Nature Chem. 2010, 2, 15. Rosati, F.; Roelfes, G. ChemCatChem 2010, 2, 916. Deuss, P. J.; den Heeten, R.; Laan, W.; Kamer, P. C. J. Chem. Eur. J. 2011, 17, 4680. (2) Kiss, G.; Çelebi-Ölçum, N.; Moretti, R.; Baker,D.; Houk, K. N. Angew. Chem. Int. Edit. 2013, 52, 5700. (3) Muñoz Robles, V.; Ortega-Carrasco, E.; Alonso-Cotchiso, L.; Rodriguez-Guerra, J.; Lledós, A.; Maréchal, J-D. ACS Catal. 2015, 5, 2469. (4) Muñoz Robles, V.; Vidossich, P.; lledós, A.; Ward, T. R.; Maréchal, J.-D. ACS Catal. 2014, 4, 833.

Computational modeling of artificial metalloenzymes

251st National Meeting (2016)

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