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IEEE/ACM Trans Comput Biol Bioinform. 2007 Jul-Sep;4(3):336-48.

CISA: combined NMR resonance connectivity information determination and sequential assignment.

Author information

1
Department of Computing Science, University of Alberta, Canada. xiangwan@cs.ualberta.ca

Abstract

A nearly complete sequential resonance assignment is a key factor leading to successful protein structure determination via NMR spectroscopy. Assuming the availability of a set of NMR spectral peak lists, most of the existing assignment algorithms first use the differences between chemical shift values for common nuclei across multiple spectra to provide the evidence that some pairs of peaks should be assigned to sequentially adjacent amino acid residues in the target protein. They then use these connectivities as constraints to produce a sequential assignment. At various levels of success, these algorithms typically generate a large number of potential connectivity constraints, and it grows exponentially as the quality of spectral data decreases. A key observation used in our sequential assignment program, CISA, is that chemical shift residual signature information can be used to improve the connectivity determination, and thus to dramatically decrease the number of predicted connectivity constraints. Fewer connectivity constraints lead to less ambiguities in the sequential assignment. Extensive simulation studies on several large test datasets demonstrated that CISA is efficient and effective, compared to three most recently proposed sequential resonance assignment programs RANDOM, PACES, and MARS.

PMID:
17666755
DOI:
10.1109/tcbb.2007.1047
[Indexed for MEDLINE]

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