Robust structure-based resonance assignment for functional protein studies by NMR.
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)-H(N) NOEs networks, as well as (1)H-(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.
Stratmann
Dirk
D
NMR, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands.
Guittet
Eric
E
van Heijenoort
Carine
C
D000465:Algorithms; D000595:Amino Acid Sequence; D008958:Models, Molecular; D009113:Muramidase / Q000737:chemistry*; D019906:Nuclear Magnetic Resonance, Biomolecular / Q000379:methods*; D017433:Protein Structure, Secondary
D009113:Muramidase