Synthetic Enzymes for Synthetic Biology

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P355956.doc

Synthetic Enzymes for Synthetic Biology

Our ability to design cell factories to produce valuable chemicals requires the â??recombinationâ? not only of existing but also designer enzymes into novel metabolic pathways to achieve entirely new metabolic function. This poses two distinct challenges that need to be solved in an integrated way if we ambition to fully deliver on the promise of synthetic biology. The first challenge deals with the rapid design of novel enzymes with high level of activities for reactions not known to be catalyzed in nature. We will review Arzedaâ??s published and unpublished successes in the computational de novo design of synthetic enzymes with entirely new catalytic sites[1â??5]. The practical impact of our technology will be highlighted through case studies of our recent industrial collaborations, including the repurposing of existing natural active sites to catalyze novel reactions and the design of novel enzymes for the fermentation of key chemical building blocks.
The second challenge is that of finding optimal ways to arrange natural and designed enzymes to biosynthetize a small-molecule of interest. To illustrate our progress towards solving this challenge, we will discuss a novel tool that Arzeda has been developing for the automated design of novel biosynthetic pathways. Inspired by retrosynthetic methods in organic chemistry, our software draws on databases of known natural enzymatic reactions as well as reactions that can be catalyzed by computationally designed enzymes to exhaustively enumerate biosynthetic routes from a set of desired metabolites down to any small molecule of interest. Pathways are ranked based on thermodynamic feasibility, designability of each enzymatic step (with specific metrics relating to our computational enzyme design software) and overall complexity and predicted yield of the synthetic route. We will present applications of industrial relevance in the field of fine and bulk chemicals.
1. Jiang L, Althoff EA, Clemente FR, Doyle L, Röthlisberger D, Zanghellini A, Gallaher JL, Betker JL, Tanaka F, Barbas CF 3rd, et al.: De novo computational design of retro- aldol enzymes. Science 2008, 319:1387â??1391.
2. Althoff EA, Wang L, Jiang L, Giger L, Lassila JK, Wang Z, Smith M, Hari S, Kast P, Herschlag D, et al.: Robust design and optimization of retroaldol enzymes. Protein Sci. Publ. Protein Soc. 2012, 21:717â??726.
3. Zanghellini A, Siegel JB, Lovick HM, Kiss G, Lambert AR, St Clair JL, Gallaher JL, Hilvert D, Gelb MH, Stoddard BL, et al.: Computational design of an enzyme catalyst for a stereoselective bimolecular Diels-Alder reaction. Science 2010, 329:309â??313.
4. Rothlisberger D, Khersonsky O, Wollacott AM, Jiang L, DeChancie J, Betker J, Gallaher JL, Althoff EA, Zanghellini A, Dym O, et al.: Kemp elimination catalysts by computational enzyme design. Nature 2008, 453:190â??195.
5. Khersonsky O, Kiss G, Röthlisberger D, Dym O, Albeck S, Houk KN, Baker D, Tawfik DS: Bridging the gaps in design methodologies by evolutionary optimization of the stability and proficiency of designed Kemp eliminase KE59. Proc. Natl. Acad. Sci.
2012, 109:10358â??10363.