Evolutionary Design of Enzyme Specificity and Chemistry

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DESCRIPTION (provided by applicant): The isolation of enzymes with tailored physical and catalytic properties is one of the fundamental thrusts of modern protein science, with the potential for profound societal, technological, and medical impact. The broad, long term objective of the proposed research is to develop the experimental framework that will define the scientific and technological foundation for evolutionary design of precisely specific and highly active new enzyme catalysts. Recent technological breakthroughs in our laboratory make possible the first ultra-high throughput analyses and selections of enzyme function in large libraries, based on expression on the surface of E. coli bacteria and sorting/analysis by high speed, multi-color flow cytometry. A key feature of our new approach is the ability to select for (and quantify) desired new activity, while simultaneously deselecting (and quantifying) unwanted catalytic activity. This new technology will be exploited to investigate, in a systematic manner, the key experimental parameters of mutation rate and mutation method during the evolution of the two most important aspects of enzyme catalysis; substrate specificity and catalytic chemistry. In particular, we will be evolving new specificity and catalytic chemistry in two complementary enzyme systems, the bacterial serine protease OmpT, a representative "specialist" enzyme, (an enzyme with precise substrate specificity), and P. solani cutinase, a member of the a/13 hydrolase superfamily that is a "generalist" enzyme (one that exhibits broad esterolytic reactivity with various substrates). The bottom line is that following the proposed studies, we will, for the first time, have enough systematic data to provide calibration with respect to what changes in substrate specificity and catalytic chemistry are feasible using enzyme directed evolution, and what approaches to enzyme randomization and substrate selection bring about maximum beneficial changes of function.