Enzyme engineering holds tremendous promise for the development of artificial catalysts that can shift industrial chemical processes away from reliance upon rare-earth or precious metal catalysts and from the extremes of temperature, pressure, and pH that industrial reactions often demand. Enzyme engineering offers a potential path to build novel biocatalysts that catalyze desired reactions, but the lack of core design principles limits their design and use. While active designs have been created computationally, the success rate is not always high and typically the small number of active constructs are many orders of magnitude slower than their natural counterparts. Current approaches involve many rounds of directed evolution in the laboratory to bring them to speed and are enormously time- and resource-intensive. As a more efficient alternative to directed evolution, our lab has developed a set of metrics, including computed chemical and electrostatic properties, for the improvement of in silico enzyme designs. Mycobacterium tuberculosis Rv0760c, a structural genomics protein, is structurally similar to Pseudomonas putida ketosteroid isomerase (PpKSI), which operates near the diffusion-controlled limit of catalysis. While Rv0760c has detectable KSI activity, its catalytic efficiency is three orders of magnitude lower than that of PpKSI and thus serves as a test case for our novel design principles. Starting from the Rv0760c structure, successive rounds of mutations were made in silico and tested computationally using our metrics pertaining to stability, electrostatics, and ligand binding. A total of 18,566 variants containing up to four mutations were made in silico, 15 of which have been selected for experimental characterization due to their favorable calculated properties. Analysis of these variants for catalytic activity will help to test our design principles and help facilitate and expand the use of designed enzymes.

Engineering increased activity into a ketosteroid isomerase homologue through predictive computational methods

253rd National Meeting (2017)

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