[nmrlearner]

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 H N â??H N 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

• • Journal Journal of Biomolecular NMR
  • Online ISSN 1573-5001
  • Print ISSN 0925-2738
  • Journal Volume Volume 46
  • Journal Issue Volume 46, Number 2

Source: Journal of Biomolecular NMR