Natural enzymes have been optimized through evolution, a colossal experiment in the diversification of protein structure-function relationships carried out over enormous stretches of time. As such, it is critical to understand both the structure of an enzyme and its history. Thus, my research leverages molecular phylogenetic analyses and ancestral sequence reconstruction to help frame questions and develop hypotheses directed towards understanding sequence-structure-function relationships. I am particularly interested in the impact that residues outside the active site have on the evolution of new enzyme functions and how this information can be leveraged to redesign enzymes for alternative purposes. In order to test our hypotheses, atomistic models of enzymes are constructed and often molecular dynamics (MD) simulations are performed first to understand fluctuations in the structure and ligand-binding to an allosteric or active site as an example. In addition, enzyme mechanisms are studied using hybrid Quantum Mechanical/Molecular Mechanical (QM/MM) methods. In the QM/MM approach, a portion of the system, typically the active site of the enzyme, is described quantum mechanically (e.g., with Density Functional Theory (DFT) methods), and the rest of the system is described with a more computationally efficient MM force field. The 2013 Nobel Prize in Chemistry was awarded to Martin Karplus, Arieh Warshel and Michael Levitt for the development of these multi-scale methods. Finally, in collaboration with experimentalists, our predictions are tested.
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