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High-throughput, data-directed computational protocols for Structural Genomics (or Proteomics) are required in order to evaluate the protein products of genes for structure and function at rates comparable to current gene-sequencing technology. This paper presents the Jigsaw algorithm, a novel high-throughput, automated approach to protein structure characterization with nuclear magnetic resonance (NMR). Jigsaw consists of two main components: (1) graph-based secondary structure pattern identification in unassigned heteronuclear NMR data, and (2) assignment of spectral peaks by probabilistic alignment of identified secondary structure elements against the primary sequence. Jigsaw's deferment of assignment until after secondary structure identification differs greatly from traditional approaches, which begin by correlating peaks among dozens of experiments. By deferring assignment, Jigsaw not only eliminates this bottleneck, it also allows the number of experiments to be reduced from dozens to four, none of which requires 13C-labeled protein. This in turn dramatically reduces the amount and expense of wet lab molecular biology for protein expression and purification, as well as the total spectrometer time to collect data.
Our results for three test proteins demonstrate that we are able to identify and align approximately 80 percent of alpha-helical and 60 percent of beta-sheet structure. Jigsaw is extremely fast, running in minutes on a Pentium-class Linux workstation. This approach yields quick and reasonably accurate (as opposed to the traditional slow and extremely accurate) structure calculations, utilizing a suite of graph analysis algorithms to compensate for the data sparseness. Jigsaw could be used for quick structural assays to speed data to the biologist early in the process of investigation, and could in principle be applied in an automation-like fashion to a large fraction of the proteome.
To appear in
The Fourth Annual International Conference on Computational
Molecular Biology (RECOMB'2000), Tokyo, Japan, April 8-11,
Bibliographic citation for this report: [plain text] [BIB] [BibTeX] [Refer]
Or copy and paste:
Chris Bailey-Kellogg, Alik Widge, John J. Kelley, Marcelo J. Berardi, John H. Bushweller, and Bruce Randall Donald, "The NOESY Jigsaw: Automated Protein Secondary Structure and Main-Chain Assignment from Sparse, Unassigned NMR Data." Dartmouth Computer Science Technical Report PCS-TR99-358, October 1999.
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