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Organic Structures From 2D NMR Spectra

The derivation of structural information from spectroscopic data is now an integral part of organic chemistry courses at all Universities. Over recent years, a number of powerful two-dimensional NMR techniques (e.g. HSQC, HMBC, TOCSY, COSY and NOESY) have been developed and these have vastly expanded the amount of structural information that can be obtained by NMR spectroscopy. Improvements in NMR instrumentation now mean that 2D NMR spectra are routinely (and sometimes automatically) acquired during the identification and characterisation of organic compounds.

Organic Structures from 2D NMR Spectra

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The elucidation of chemical structures from 2D NMR data commonly utilizes a combination of COSY, HMQC/HSQC, and HMBC data. Generally COSY connectivities are assumed to mostly describe the separation of protons that are separated by 1 skeletal bond (3JHH), while HMBC connectivities represent protons separated from carbon atoms by 1 to 2 skeletal bonds (2JCH and 3JCH). Obviously COSY and HMBC connectivities of lengths greater than those described have been detected. Though experimental techniques have recently been described to aid in the identification of the nature of the couplings the detection of whether a coupling is 2-bond or greater still remains a challenge in most laboratories. In the StrucEluc software system the common lengths of the connectivities, 1-bond for COSY and 1- or 2-bond for HMBC, derived from 2D NMR data are set as the default. Therefore, in the presence of any extended connectivities contradictions can appear in the 2D NMR data. In this article, algorithmic methods for the detection and removal of contradictions in 2D NMR data that have been developed in support of StrucEluc are described. The methods are based on the analysis of molecular connectivity diagrams, MCDs. These methods have been implemented in the StrucEluc system and tested by solving 50 structural problems with 2D NMR spectral data containing contradictions. The presence of contradictions was detected by the algorithm in 90% of the cases, and the contradictions were automatically removed in approximately 50% of the problems. A method of "fuzzy" structure generation in the presence of contradictions has been suggested and successfully tested in this work. This work will demonstrate examples of the application of developed methods to a number of structural problems.

nmrshiftdb2 is a NMR database (web database) for organic structures and their nuclear magnetic resonance (nmr) spectra. It allows for spectrum prediction (13C, 1H and other nuclei) as well as for searching spectra, structures and other properties. The nmrshiftdb2 software is open source, the data is published under an open content license. The core of nmrshiftdb2 are fully assigned spectra with raw data and peak lists (we have pure peak lists as well). Those datasets are peer reviewed by a board of reviewers. The project is supported by a scientific advisory board. nmrshiftdb2 is part of the NFDI4Chem initiative and will provide a component for a curated repository there. Please consult the documentation for more detailed information.

Data from ChiuZ Wed, 08 May 2019 10:09:53 -0000nmrshiftdb2 contains structures published in the journal 'Chemie in Unserer Zeit' as part of a series on extraction and spectroscopy of natural products. The corresponding raw data as well as assigned structures are available. The compounds can be found here. DOIs (digital object identifiers) are assigned to these datasets. For example, data for Quinine are identified by DOI 10.18716/nmrshiftdb2/60004827/chiuz_cdcl3.

This archive includes six types of problems from the midterm and final exams of my Chem 203 Organic Spectroscopy class. The first three focus on infrared spectroscopy, mass spectrometry, and 1D NMR spectroscopy. The next focuses on using these three techniques together to determine the structures of organic compounds. The last two categories incorporate 2D NMR spectroscopy and are thus considered "advanced." The advanced spectral analysis problems focusing on analyzing 1- and 2D NMR spectra to address questions of stereochemistry. The advanced structure determination problems focus on using all of these techniques to determine the structures of organic compounds.

Typical performance of ultrafast 1H COSY and 1H-13C HSQC experiments on modern NMR hardware. Average approximate values are given, obtained from experimental data and theoretical calculations. Single-scan limits of detection are given at natural abundance for room temperature (RT) and cryogenic probes. While these values seem relatively high, the LOD can be easily decreased by signal averaging, as in conventional NMR. The performance of such a multi-scan approach is further discussed in the manuscript. Spectral widths are calculated assuming that the spatial encoding time is Te = 30 ms, which corresponds to a reasonable compromise between resolution and spectral width, and that the maximum gradient amplitude used during acquisition is 30 Spectral widths are given regardless of any folding or spectral/spatial manipulation that can further increase the effective observable spectral width.

(a) Representative selection of real-time 2D HSQC NMR spectra arising from the reaction of triflic anhydride, ketone (7) and acetonitrile-d3 shown in (b). Spectra show species (7, 11, 12 and 13) present in the aliphatic window range (1.54 - 2.87 ppm for 1H, 23.7 - 33.7 ppm for 13C) at key time points as the reaction progresses, depicted by arrows (red, blue, green and magenta respectively) (79).

A group of scientists, including EPFL professors Lyndon Emsley, head of the Laboratory of Magnetic Resonance, Michele Ceriotti, head of the Laboratory of Computational Science and Modelling, and PhD student Manuel Cordova, decided to resolve this issue by formulating a technique of assigning NMR spectra of organic crystals probabilistically, straight from their 2D chemical structures.

They began by developing their own database of chemical shifts for organic solids by integrating the Cambridge Structural Database (CSD), a database of over 200,000 three-dimensional organic structures, with ShiftML, a machine learning algorithm they had formulated together earlier that facilitates the prediction of chemical shifts straight from the structure of molecular solids.

The representation is an interpretation of the covalent bonds around the atom in a molecule and does not comprise any 3D structural features: this enabled them to acquire the probabilistic assignment of the NMR spectra of organic crystals straight from their two-dimensional (2D) chemical structures via a marginalization scheme that integrated the distributions from all the atoms in the molecule.

Finally, they assessed the performance of the framework on a benchmark set of 100 crystal structures with between 10 and 20 dissimilar carbon atoms. They used the ShiftML predicted shifts for each atom as the precise assignment and omitted them from the statistical distributions used to assign the molecules. The precise assignment was discovered among the two most likely assignments in over 80% of cases.

Interpretive Summary: Phytate is an important phosphorous (P) and mineral storage compound in plant seeds, such as cottonseed. It is estimated that over 51 million metric tons of phytate (in K and Mg salts) are produced in crop seeds and fruits globally each year. Cottonseed meal in poultry feed contains about 4.4% (w/w) of phytate. However, up to two-thirds of P bound to phytate is unavailable to poultry. On the other hand, phytate has been reported to be a potential anti-cancer agent. In regards to environmental concern, phytate represents a significant portion of P in animal manure, and its runoff and leaching play a role in eutriphication of surface waters. It is proposed that both the benign and adverse effects of phytate in nutrition and environment are mainly due to its unique structure, where strong chelating ability makes phytate interact with many cations. In this study, we report solid state 1D 13C and 2D 1H-13C NMR spectra of 9 metal phytate compounds. Spectral analysis provides evidence for interconverting conformations that are influenced by different valent cations. Structural information derived from this research improves our understanding of the roles of phytates in metal storage in plants, lability and bioavailability of these organic P compounds in biological systems and the environment, and immobilization of heavy metals in contaminated soils.

Technical Abstract: Phytate is an important phosphorous and mineral storage compound in plant seeds. Both the benign and adverse effects of phytate in nutrition and environment are mainly due to its unique conformational structures, where a strong chelating ability makes phytate interact with many cations (such as Zn2+, Ca2+, Mg2+, and Al3+). However, information is scant on the conformational forms of different solid metal phytate compounds although phytate in solution exists in two conformations: one axial and five equatorial phosphates (1a/5e) structure and an inverted 5a/1e structure. Consequently, we investigated the spectral features of nine representative metal phytate compounds by solid state 1D 13C cross polarization magic angle spinning (CPMAS) and 2D 13C-1H heteronuclear correlation (HETCOR) NMR. A broad peak appeared in all solid 1D NMR spectra of hydrogen monovalent, divalent, and trivalent metal phytate compounds. The spectra of hydrogen monovalent and divalent compounds could be deconvoluted to two separate resonance peaks. 2D HETCOR clearly showed distinct 13C-1H correlations for inositol C-H moieties in hydrogen metal phytates. Based on the known structures of myo-inositol and Na phytate, we interpret the solid NMR structures of hydrogen metal phytates as mixtures of interconverting ring conformations. The observed difference in the 2D HETCOR NMR spectra between hydrogen metal phytates and non-hydrogen metal phytates provides a spectroscopic method to distinguish the two types of phytate compounds from each other. 041b061a72


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