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

Immel, Stefan ; Köck, Matthias ; Reggelin, Michael (2022)
NMR-Based Configurational Assignments of Natural Products: Gibbs Sampling and Bayesian Inference Using Floating Chirality Distance Geometry Calculations.
In: Marine Drugs, 2022, 20 (1)
doi: 10.26083/tuprints-00020283
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: NMR-Based Configurational Assignments of Natural Products: Gibbs Sampling and Bayesian Inference Using Floating Chirality Distance Geometry Calculations
Language: English
Date: 13 April 2022
Place of Publication: Darmstadt
Year of primary publication: 2022
Publisher: MDPI
Journal or Publication Title: Marine Drugs
Volume of the journal: 20
Issue Number: 1
Collation: 22 Seiten
DOI: 10.26083/tuprints-00020283
Corresponding Links:
Origin: Secondary publication DeepGreen
Abstract:

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.

Uncontrolled Keywords: distance geometry calculations, configurational analysis, chirality, NMR spectroscopy, residual dipolar couplings, assignment probabilities, statistical error analysis
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-202834
Classification DDC: 500 Science and mathematics > 540 Chemistry
500 Science and mathematics > 570 Life sciences, biology
Divisions: 07 Department of Chemistry > Clemens-Schöpf-Institut > Organ Chemistry
Date Deposited: 13 Apr 2022 11:29
Last Modified: 14 Nov 2023 19:04
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/20283
PPN: 500731373
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