Towards automated structure-based NMR resonance assignment
RECOMB'10 Proceedings of the 14th Annual international conference on Research in Computational Molecular Biology
A Tabu Search Approach for the NMR Protein Structure-Based Assignment Problem
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Motivation: A prerequisite for any protein study by NMR is the assignment of the resonances from the 15N−1H 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: http://www.icsn.cnrs-gif.fr/download/nmr Contact:carine@icsn.cnrs-gif.fr; eric.guittet@icsn.cnrs-gif.fr Supplementary Information:Supplementary data are available at Bioinformatics online.