PhD defence J.Y. van der Meer
|When:||Fr 07-10-2016 at 12:45|
Mutability-landscape guided enzyme engineering
Improving the promiscuous C-C bond-forming activities of 4-oxalocrotonate tautomerase
Jan Ytzen van der Meer has improved two promiscuous C-C bond-forming activities of the enzyme 4-oxalocrotonate tautomerase (4-OT). Enzymes do not only play a crucial role in nature, they are also increasingly used as biocatalysts for the production of complex molecules such as pharmaceuticals. However, some of the reactions that are widely used in organic synthesis have not been observed in biological systems. Hence, there is no biocatalytic alternative available for those important reactions. One way of generating enzymes for these unnatural reactions is by exploiting the catalytic promiscuity of existing enzymes.
For improving the promiscuous Michael-type addition activity of 4-OT, he first determined the effects of nearly all possible single amino acid substitutions on both activity and enantioselectivity. In the resulting mutability landscapes, all positive, neutral and detrimental effects of these mutations are displayed. Guided by these mutability landscapes of 4-OT, he then generated a set of highly active and enantiocomplementary ‘Michaelases’. These enantioselective enzymes can be used for the convenient synthesis of both enantiomers of γ-nitroaldehydes, which are important precursors for pharmaceutically active GABA derivatives. The second promiscuous activity of 4-OT that was improved was the aldolase activity. Based on a similar approach, three residue positions were identified in 4-OT at which mutations led to a marked improvement of the promiscuous aldolase activity. Combinations of these mutations further improved this aldolase activity of 4-OT, allowing the enzymatic self- and cross-coupling of various aldehydes.
Taken together, the work described in the thesis of Van der Meer provides insights in the generation and application of mutability landscapes for efficient enzyme engineering and yielded comprehensive mutational data, which might be used as a unique training set to improve computational tools for enzyme engineering.
Promotors: Prof.dr. W.J. Quax and Prof.dr. G.J. Poelarends