Journal Article

NOE<i>net</i>–Use of NOE networks for NMR resonance assignment of proteins with known 3D structure

Dirk Stratmann, Carine van Heijenoort and Eric Guittet

in Bioinformatics

Volume 25, issue 4, pages 474-481
Published in print February 2009 | ISSN: 1367-4803
Published online December 2008 | e-ISSN: 1460-2059 | DOI:

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Motivation: A prerequisite for any protein study by NMR is the assignment of the resonances from the 15N1H HSQC spectrum to their corresponding atoms of the protein backbone. Usually, this assignment is obtained by analyzing triple resonance NMR experiments. An alternative assignment strategy exploits the information given by an already available 3D structure of the same or a homologous protein. Up to now, the algorithms that have been developed around the structure-based assignment strategy have the important drawbacks that they cannot guarantee a high assignment accuracy near to 100%.

Results: We propose here a new program, called NOEnet, implementing an efficient complete search algorithm that ensures the correctness of the assignment results. NOEnet exploits the network character of unambiguous NOE constraints to realize an exhaustive search of all matching possibilities of the NOE network onto the structural one. NOEnet has been successfully tested on EIN, a large protein of 28 kDa, using only NOE data. The complete search of NOEnet finds all possible assignments compatible with experimental data that can be defined as an assignment ensemble. We show that multiple assignment possibilities of large NOE networks are restricted to a small spatial assignment range (SAR), so that assignment ensembles, obtained from accessible experimental data, are precise enough to be used for functional proteins studies, like protein–ligand interaction or protein dynamics studies. We believe that NOEnet can become a major tool for the structure-based backbone resonance assignment strategy in NMR.

Availability: The NOEnet program will be available under:


Supplementary Information: Supplementary data are available at Bioinformatics online.

Journal Article.  7626 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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