Enzyme Evolution for Industrial Biocatalysis and Metabolic Engineering

Enzymes are incredibly proficient catalysts, having evolved through several billion years of natural selection to catalyze thousands of biochemical reactions critical to all life on the planet. When operating on natural substrates and products under physiological conditions, they can accelerate reactions up to 10^17 over that of uncatalyzed reactions. Unfortunately, they typically do not perform well for industrial applications, where pH, temperature, and solvent conditions as well as the substrates and products they operate on can deviate significantly from the environment in which they evolved. Thus, natural enzymes usually require some degree of optimization to function effectively as industrial biocatalysts.  This need applies to both cell free systems where enzymes function in much the same way as traditional chemocatalysts as well as to in vivo systems where the ability to successfully engineer cellular and metabolic processes is dependent on the efficient, rapid conversion of carbon sources into high value chemicals or fuels.

To address the limitations of natural enzymes, significant scientific and engineering efforts have been devoted to the subject of enzyme optimization over the last few decades, resulting in large advances in the speed and degree to which these proteins can be discovered and improved.  During this time the field has elucidated a number of aspects of general importance which help to frame and guide the optimization process. Three, roughly orthogonal, aspects are conceptualized here as: 1) Fitness function, 2) Diversity generation, and 3) Search algorithm.  Together, these aspects form a useful basis in which to approach the problem.  The interplay between these aspects and the roles of machine learning and high throughput synthetic biology will be discussed along with relevant examples that demonstrate the principles efficient enzyme optimization.

Speaker Bio:Richard J. Fox received his B.S. degree in nuclear engineering from the University of California, Santa Barbara, CA, and his M.S. and Ph.D. degrees in nuclear engineering from the University of California, Berkeley, CA. He has over 15 years of industry experience in software engineering, statistical analysis, and biotechnology.  He spent the last 10 years at Codexis, a San Francisco Bay Area based biotechnology company and world leader in enzyme engineering, specializing in the production of pharmaceuticals, biofuels, and industrial products.  At Codexis, he initiated a process to combine statistical & computational techniques with high throughput molecular biology and biochemistry, yielding important advantages for the company and its partners while fostering environmentally sustainable technologies.  He was a co-recipient of the 2006 and 2010 Presidential Green Chemistry Challenge Awards and actively publishes in the fields of statistics and enzyme engineering.

Speaker: Who: Richard Fox, Director of Computational Biology, Codexis Inc.

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