Development and application of efficient and accurate methods for computational enzymology

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We are developing a research program in computational enzymology. Computational enzymology is the study of enzymes and the biochemical reactions they catalyze using computer simulations. At present time, the only methods for simulating enzymatic reactions that are sufficiently accurate to reveal the mechanism of an enzyme require weeks, if not months of calculation on a supercomputer. Our long-term objective is to develop new methods that allow the same simulations to be performed in a few hours, and that enable computational studies at a much larger scale. Our current focus is on metalloenzymes, which are proteins that get their biochemical activity from binding a metal atom such as zinc, iron, or magnesium. Metalloenzymes are involved in numerous biological functions such as respiration, digestion, tissue growth, and blood-pressure regulation, and are prime therapeutic targets. Some metalloenzyme inhibitors are efficient drugs against hypertension, HIV/AIDS, and cancer. To develop these new methods, we first simulate--as accurately as possible--the "stripped down" versions of all possible chemical reactions observed in a given metalloenzyme. The resulting data, generated once and for all, is then represented with mathematical formulas that are mimicking its properties realistically enough that they can be used as substitutes for the original simulations. The formulas are then used to simulate chemical reactions in their "dressed up" versions, and to study the function of any enzyme having the same general architecture as the prototype. We are interested in answering the following questions: Why is a given metalloenzyme more active bound to a certain metal than to another? In enzymes containing more than one metal atom in their active site, what is the role of each one? Which motions of the enzyme are important for its activity? Can these movements be inhibited using a small molecule? In the short term, our work will help to understand the molecular details of numerous catalytic mechanisms. In the long term, it will help to decode the function of an enzyme from its three-dimensional structure, to develop new inhibitors, and to reduce the side effects of already existing inhibitors.