- About Us
Proteins play central roles in all aspects of biochemistry. In addition to the proteins that serve as enzymes catalyzing the reactions of metabolism, there are, among others, structural proteins, protein hormones, transport proteins, cell surface receptors and proteins involved in the regulation of DNA replication and transcription. A theme common to all classes of proteins is specific recognition and function through unique structure, and for that reason, detailed structural information is one key to developing a better understanding of the mechanisms involved in the specificity of recognition and catalysis.
My group has long had two main focusses: the first is to primarily use X-ray crystallography to solve detailed protein structures in collaboration with researchers carrying out functional studies of the same proteins; the second and growing focus is empirical studies of ultrahigh-resolution protein structures in the protein data bank to discover details and principles of protein structure that have not yet been recognized. Both kinds of studies lead to impactful publications.
Two projects with a protein structure-function focus are studies of peroxiredoxins and their role in redox protection and hydrogen peroxide signaling in eukaryotes and studies of flavoenzymes to investigate how the enzyme:flavin interactions influence the electronic structure of the flavin, and modulate its reactivity. Three projects in the data-mining area focus on (i) the planarity (or perhaps better the non-planarity) of the peptide bond, (ii) how covalent geometry depends on conformation (including the development and continuing improvement of a new conformation-dependent restraint library) and (iii) conformational preferences of protein chains. A “Protein Geometry Database” we developed is a useful tool for these and other studies.
One additional interest working to continuing to develop ideas about how protein crystallographers can make the best use of their data. Collaboratively with Kay Diederichs (Konstanz, Germany), we documented shortcomings of conventional measures of data quality used in protein crystallography, and showed that conventional practices have commonly led researchers to discard weak but still useful data (see "Assessing and maximizing data quality in macromolecular crystallography" Curr Opin Struct Biol (2015) 34:60-68). We further proposed two novel statistics, CC1/2 and CC*, that are much more robust measures of data quality, and now work continues to further ways use of these metrics can improve data collection and structure refinement practices.
I am not currently taking further graduate students into my group, but I am still actively helping with the mentoring students in other research groups who are interested in studying protein structure-function relationships, especially through studying structure through the use of protein crystallography.