The NOESY jigsaw: automated protein secondary structure and main-chain assignment from sparse, unassigned NMR data.


Journal Article

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. Deferring assignment eliminates the bottleneck faced by traditional approaches, which begin by correlating peaks among dozens of experiments. JIGSAW utilizes only four experiments, none of which requires 13C-labeled protein, thus dramatically reducing both the amount and expense of wet lab molecular biology and the total spectrometer time. Results for three test proteins demonstrate that JIGSAW correctly identifies 79-100% of alpha-helical and 46-65% of beta-sheet NOE connectivities and correctly aligns 33-100% of secondary structure elements. 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. It could be useful for quick structural assays to speed data to the biologist early in an investigation and could in principle be applied in an automation-like fashion to a large fraction of the proteome.

Full Text

Cited Authors

  • Bailey-Kellogg, C; Widge, A; Kelley, JJ; Berardi, MJ; Bushweller, JH; Donald, BR

Published Date

  • January 2000

Published In

Volume / Issue

  • 7 / 3-4

Start / End Page

  • 537 - 558

PubMed ID

  • 11108478

Pubmed Central ID

  • 11108478

Electronic International Standard Serial Number (EISSN)

  • 1557-8666

International Standard Serial Number (ISSN)

  • 1066-5277

Digital Object Identifier (DOI)

  • 10.1089/106652700750050934


  • eng