ArticleJournal of biomolecular NMR2008
A unifying probabilistic framework for analyzing residual dipolar couplings.
Article in Journal of biomolecular NMR, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- NMR-Based Configurational Assignments of Natural Products: Gibbs Sampling and Bayesian Inference Using Floating Chirality Distance Geometry Calculations.Marine drugs · 2021Article
- An Integrative Approach to Determine 3D Protein Structures Using Sparse Paramagnetic NMR Data and Physical Modeling.Frontiers in molecular biosciences · 2021Article
- Maximizing accuracy of RNA structure in refinement against residual dipolar couplings.Journal of biomolecular NMR · 2019Article
- Modern Technologies of Solution Nuclear Magnetic Resonance Spectroscopy for Three-dimensional Structure Determination of Proteins Open Avenues for Life Scientists.Computational and structural biotechnology journal · 2017Review
- Article
- New opportunities for tensor-free calculations of residual dipolar couplings for the study of protein dynamics.Journal of biomolecular NMR · 2014Review
- Expanding the utility of NMR restraints with paramagnetic compounds: background and practical aspects.Progress in nuclear magnetic resonance spectroscopy · 2011Review
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Authors and funding
3 authors.
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Abstract
Residual dipolar couplings provide complementary information to the nuclear Overhauser effect measurements that are traditionally used in biomolecular structure determination by NMR. In a de novo structure determination, however, lack of knowledge about the degree and orientation of molecular alignment complicates the analysis of dipolar coupling data. We present a probabilistic framework for analyzing residual dipolar couplings and demonstrate that it is possible to estimate the atomic coordinates, the complete molecular alignment tensor, and the error of the couplings simultaneously. As a by-product, we also obtain estimates of the uncertainty in the coordinates and the alignment tensor. We show that our approach encompasses existing methods for determining the alignment tensor as special cases, including least squares estimation, histogram fitting, and elimination of an explicit alignment tensor in the restraint energy.
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