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Updated: Sep 5, 2025
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Structure and Coordination Determination of Peptide-metal Complexes Using 1D and 2D 1H NMR
Published on: December 16, 2013
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NMR-Based Configurational Assignments of Natural Products: How Floating Chirality Distance Geometry Calculations
Stefan Immel1, Matthias Köck2, Michael Reggelin1
1Clemens-Schöpf-Institut für Organische Chemie und Biochemie, Technische Universität Darmstadt, Alarich-Weiss-Straße 4, 64287 Darmstadt, Germany.
Journal of Natural Products
|July 12, 2022
Summary
This study introduces a Bayesian inference method to accurately determine the 3D structures of natural products using NMR data. This approach enhances the reliability of structural elucidation in chemistry, reducing errors from incomplete data.
Area of Science:
- Organic Chemistry
- Computational Chemistry
- Medicinal Chemistry
Background:
- Accurate 3D structural assignment of natural products is crucial for drug discovery and development.
- Current methods for structural elucidation can be time-consuming and prone to errors, especially when relying on incomplete experimental data or computational models.
Purpose of the Study:
- To develop and validate a quantitative method for assessing the reliability of configurational assignments in natural product structural elucidation.
- To demonstrate an approach that unambiguously establishes relative configurations using experimental NMR data, minimizing reliance on computationally intensive analyses.
Main Methods:
- Utilized Bayesian inference combined with floating-chirality distance geometry simulations.
- Employed Nuclear Magnetic Resonance (NMR) data, including Nuclear Overhauser Effect (NOE)/Rotating-frame Overhauser Effect (ROE) data, Residual Dipolar Couplings (RDCs), and Residual Quadrupolar Couplings (RQCs).
- Applied the methodology to three natural compounds: jatrohemiketal, artemisinin, and Taxol.
Main Results:
- Successfully and unambiguously established the relative configurations of the tested natural compounds.
- Quantitatively demonstrated the reliability of inferring molecular geometries from various combinations of experimental NMR data.
- Showcased the ability to resolve configurational ambiguities without extensive Density Functional Theory (DFT) calculations.
Conclusions:
- The presented Bayesian inference methodology provides a robust and efficient way to determine 3D configurations and conformations of natural products.
- This approach significantly reduces the risk of incorrect structural assignments, improving the quality of structural elucidation in chemistry.
- Offers a reliable alternative to time-consuming DFT-based analyses for configurational and conformational studies.
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