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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.
While I have closed my research lab and am not currently taking further graduate students, I am still actively helping with the mentoring students in other research groups who are interested in protein structure-function relationships.
My own research interests continue to be in two main areas: the first is to solve detailed protein structures (especially by X-ray crystallography) and, in collaboration with researchers carrying out functional studies, figure out how they work; the second 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. Two longer-term projects in the first area have been studies of peroxiredoxins and their roles in redox protection and hydrogen peroxide signaling and studies of a variety of flavoenzymes and thei redox activities. Projects in the data-mining area have included work on (i) the non-planarity of the peptide bond, (ii) how covalent geometry depends on conformation 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 has been how protein crystallographers can make the best use of their data. Collaboratively with Kay Diederichs (Konstanz, Germany), we showed that conventional practices 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), and proposed two novel statistics, CC1/2 and CC*, as more robust measures of data quality that can improve data collection and structure refinement practices.
From Brereton Karplus (2015) Sci Adv Oct 16;1(9):e1501188
From Kean et al (2017) FEBS J 284, 3302