Bioinformatic tools can offer exquisite insight into the ever growing quantities of genomic information. Gene sequences alone are usually sufficient to predict their membership in large structural superfamilies as well as more specific subfamilies that may share related functions. However, the scope of sequence data has so exceeded the realm of biochemical characterization that many subfamilies lack any structural, chemical or biological description. Strategies for predicting function within such groups are still in their infancy and will require a substantial investment in biochemistry to develop the essential links between sequence, structure and function. The nitroreductase superfamily offers an enticing platform to establish such a link to the redox chemistry of the flavin mononucleotide associated with these enzymes. This endeavor will capitalize on the extensive characterization of a few representative nitroreductases that lend their name to this superfamily and the recent release of a sequence similarity network that introduces a sophisticated order to more than 24,000 unique sequences. The iodotyrosine deiodinase group of this superfamily will now be investigated for its ability to promote sequential single electron transfer and suppress hydride transfer as a counterpoint to the ability of nitroreductases to act in the reverse by suppressing single electron transfer and promoting hydride transfer. Experimental strategies will include isotope and viscosity effects on catalysis by steady-state and rapid kinetics. Concurrently, substrate and flavin analogues will be used to measure the relative efficiencies of proton and electron transfer during deiodination. Key residues involved in these processes will then be identified by site-directed mutagenesis. A predictive understanding of flavin's redox chemistry will be constructed from these results and prior knowledge derived from nitroreductases. The principles developed by this comparison will be refined by two approaches. One will use structural and mechanistic data to direct conversion of a native deiodinase into a nitroreductase and vice versa. The other will examine the redox properties of superfamily members that have not yet been tested but can be anticipated from the correlations developed by this project.
Bioinformatic tools are critical for imposing order on genomic databases but insufficient biochemical data is available for predicting function. We now propose structural and mechanistic studies on iodotyrosine deiodinase to help provide functional diagnosis to the newly published sequence similarity network of its structural superfamily. Identifying the features that control the redox properties of their bound flavin cofactor should provide a foundation for discovering new catalytic activities and drug targets associated with virulence or resistance.
