The carbon-carbon bond forming ability of transketolase (TK), along with its broad substrate specificity, makes it very attractive as a biocatalyst in industrial organic synthesis. Through the production of saturation mutagenesis libraries focused on individual active site residues, several variants of TK have been discovered with enhanced activities on non-natural substrates. We have used computational and bioinformatics tools to increase our understanding of TK and to guide engineering of the enzyme for further improvements in activity. Computational automated docking is a powerful technique with the potential to identify transient structures along an enzyme reaction pathway that are difficult to obtain by experimental structure determination. We have used the AutoDock algorithm to dock a series of known ketol donor and aldehyde acceptor substrates into the active site of E. coli TK, both in the presence and the absence of reactive intermediates. Comparison of docked conformations with available crystal structure complexes allows us to propose a more complete mechanism at a level of detail not currently possible by experimental structure determination alone. Statistical coupling analysis (SCA) utilises evolutionary sequence data present within multiple sequence alignments to identify energetically coupled networks of residues within protein structures. Using this technique we have identified several coupled networks within the TK enzyme which we have targeted for mutagenesis in multiple mutant variant libraries. Screening of these libraries for increased activity on the non-natural substrate propionaldehyde (PA) has identified combinations of mutations that act synergistically on enzyme activity. Notably, a double variant has been discovered with a 20-fold improvement in kcat relative to wild type on the PA reaction, this is higher than any other TK variant discovered to date.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:625763 |
| Date | January 2012 |
| Creators | Strafford, J. |
| Publisher | University College London (University of London) |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://discovery.ucl.ac.uk/1339145/ |
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