Department of Chemistry

Research Interests

The “genomics revolution” represents a paradigm shift in the understanding of life at a molecular level. Due to techniques pioneered by the various genome projects, it is now a relatively simple matter to understand the genetic blueprint of any given organism. However, obtaining the “parts list” of an organism only begins the process of understanding how that organism works. Building on the success of genomics, various other “omics” projects have been initiated that will provide a more complete picture of how life works at the chemical level.

Of particular interest has been the continued progress in structural genomics whose goal is to experimentally determine three-dimensional structures for a representative set of proteins as well as tools to enable biochemists interested in the function of an arbitrary protein to model its structure from one or more of the representative structures and to automatically annotate and assess the validity of that structure.

Current “Omics” projects seek to apply the genomics paradigm to such diverse areas of biochemistry as the study of metabolism (“metabolomics”) and the study of protein molecular motion (“dynameomics”). Compared with structural genomics, the representative results sets produced in these “Omics” projects lack agreed upon associated tools for automated annotation, assessment and comparative modeling. My research seeks to develop such tools, particularly in the field of dynameomics. This builds upon my previous research in the area of the uncertainty of protein structures as one key source of empirical uncertainty in the structure of a protein is that the protein itself has no absolutely precisely fixed structure but its conformation is instead dynamic in character.

Additionally, the experimental data for structural genomics and other “Omics” projects typically is
spectroscopic data limited in either resolution or sensitivity. The covariance transform has proven to be a useful method for enhancement of either the resolution or the sensitivity of spectroscopic data as well as for the computational reconstruction of spectra difficult to obtain experimentally due to limited sensitivity. My research seeks to further explore the properties of the covariance transform as well as to provide software that will increase the accessibility of this powerful approach toward analyzing spectroscopic data sets.

Selected Publications

Z-matrix formalism for quantitative noise assessment of covariance nuclear magnetic resonance spectra David A. Snyder, et al. J. Chem. Phys. 2008, 129, 104511.

Resolution-Enhanced 4D 15N/ 13C-NOESY Protein NMR Spectroscopy by Application of the Covariance Transform David A. Snyder, et al. J. Am. Chem. Soc. 2007, 129, 14126-14127.

Covariance NMR in higher dimensions: application to 4D NOESY spectroscopy of proteins. David A. Snyder, et al. J. Biomol NMR. 2007, 39, 165-175.

Lack of correlation between NMR spectral quality and success in crystallization demonstrates that NMR and X-ray crystallography are complementary methods for small protein structure determination D.A. Snyder, et al., J. Am. Chem. Soc. 2005, 127, 16505-16511. (Cited in Nature: News and Views)

Clustering algorithms for identifying core atom sets and for assessing the precision of protein structure ensembles David A. Snyder and Gaetano T. Montelione. Proteins: Struct. Funct. Bioinformatics. 2005, 59, 673-686 (Cover Article)