Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Identifying gene regulatory networks from experimental data
RECOMB '99 Proceedings of the third annual international conference on Computational molecular biology
An algorithm for clustering cDNAs for gene expression analysis
RECOMB '99 Proceedings of the third annual international conference on Computational molecular biology
Algorithms for choosing differential gene expression experiments
RECOMB '99 Proceedings of the third annual international conference on Computational molecular biology
Extracting structural information using time-frequency analysis of protein NMR data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
A random graph approach to NMR sequential assignment
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
Protein similarity from knot theory and geometric convolution
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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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 applies graph algorithms and probabilistic reasoning techniques, enforcing first-principles consistency rules in order to overcome a 5-10% signal-to-noise ratio. It 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 13 C-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 &agr;-helical and 60 percent of &bgr;-sheet structure. JIGSAW is very 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.