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Provides protein engineers with tools to assess the atomistic implications of their mutations.

In recent years, computational methods have matured to a point at which it has become possible to equip protein structures with new functions. Computational ‘de-novo’ design has taken the approach of engineering entire active sites around a QM transition state and grafting these into stable protein scaffolds. While this lead to the production of new biocatalysts, we are far from achieving the level of specificity, turnover rates, and chemical sophistication that is evident from nature’s enzymes. We are working on significantly improving this by accounting for dynamic motions.
Over the past year, the focus of my research has been on methodological developments and on their applications towards biochemically relevant systems. More specifically, I have been working a) on a protocol for the automated and reliable setup of molecular dynamics (MD) simulations, b) on rapid ways of analyzing the vast amount of data that is generated in the course of such simulations, c) on applying both (a) and (b) towards the analysis and the redesign of a prenyltransferase (in collaboration with Prof. Chaitan Khosla at Stanford), and d) on the high-throughput screening and evaluation of computationally designed enzymes, binders, and structural proteins (in collaboration with Prof. David Baker at UW).