Robust structure-based resonance assignment for functional protein studies by NMR
Abstract High-throughput functional protein NMR studies, like protein interactions or dynamics, require an automated approach for the assignment of the protein backbone. With the availability of a growing number of protein 3D structures, a new class of automated approaches, called structure-based assignment, has been developed quite recently. Structure-based approaches use primarily NMR input data that are not based on J-coupling and for which connections between residues are not limited by through bonds magnetization transfer efficiency. We present here a robust structure-based assignment approach using mainly HN â??HN NOEs networks, as well as 1 Hâ??15 N residual dipolar couplings and chemical shifts. The NOEnet complete search algorithm is robust against assignment errors, even for sparse input data. Instead of a unique and partly erroneous assignment solution, an optimal assignment ensemble with an accuracy equal or near to 100% is given by NOEnet. We show that even low precision assignment ensembles give enough information for functional studies, like modeling of protein-complexes. Finally, the combination of NOEnet with a low number of ambiguous J-coupling sequential connectivities yields a high precision assignment ensemble. NOEnet will be available under: http://www.icsn.cnrs-gif.fr/download/nmr.
Content Type Journal Article
DOI 10.1007/s10858-009-9390-3
Authors
Dirk Stratmann, NMR, Utrecht University Padualaan 8 3584 CH Utrecht The Netherlands
Eric Guittet, Centre de Recherche de Gif, Laboratoire de Chimie et Biologie Structurales ICSN-CNRS 1, av. de la terrasse 91190 Gif-sur-Yvette France
Carine van Heijenoort, Centre de Recherche de Gif, Laboratoire de Chimie et Biologie Structurales ICSN-CNRS 1, av. de la terrasse 91190 Gif-sur-Yvette France
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Robust structure-based resonance assignment for functional protein studies by NMR
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