4.7 Article

NMR-Based Configurational Assignments of Natural Products: Gibbs Sampling and Bayesian Inference Using Floating Chirality Distance Geometry Calculations

Journal

MARINE DRUGS
Volume 20, Issue 1, Pages -

Publisher

MDPI
DOI: 10.3390/md20010014

Keywords

distance geometry calculations; configurational analysis; chirality; NMR spectroscopy; residual dipolar couplings; assignment probabilities; statistical error analysis

Ask authors/readers for more resources

This report introduces the floating chirality restrained distance geometry (fc-rDG) calculations for evolving structures from NMR data. Unlike evaluating pre-calculated structures, this method automatically generates multiple configurations within the experimental limits. The study also demonstrates the statistical significance of the unphysical rDG pseudo energies defined from NMR violations and their ability to assign probabilities to configurational assignments using Bayesian inference.
Floating chirality restrained distance geometry (fc-rDG) calculations are used to directly evolve structures from NMR data such as NOE-derived intramolecular distances or anisotropic residual dipolar couplings (RDCs). In contrast to evaluating pre-calculated structures against NMR restraints, multiple configurations (diastereomers) and conformations are generated automatically within the experimental limits. In this report, we show that the unphysical rDG pseudo energies defined from NMR violations bear statistical significance, which allows assigning probabilities to configurational assignments made that are fully compatible with the method of Bayesian inference. These diastereomeric differentiabilities then even become almost independent of the actual values of the force constants used to model the restraints originating from NOE or RDC data.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available