Automated compound verification using 2D-NMR HSQC data in an open-access environment.

Since the introduction of NMR prediction software, medicinal chemists have imagined submitting their compounds to corporate compound registration systems that would ultimately display a simplified pass/fail result. We initially implemented such a system based on HPLC and liquid chromatography mass spectrometry (LCMS) data that is embedded within our industry standard sample submission and registration process. By using gradient-heteronuclear single quantum coherence (HSQC) experiments, we have extended this concept to NMR data through a comparison of experimentally acquired data against predicted (1)H and (13)C NMR data. Integration of our compound registration system with our analytical instruments now provides our chemists unattended and automated NMR verification for collections of submitted compounds. The benefits achieved from automated processing and interpretation of results produced enhanced confidence in our compound library and released the chemists from the tedium of manipulating large amounts of data. This allows scientists to focus more of their attention to the drug discovery process.

Automated structure verification based on a combination of 1D (1)H NMR and 2D (1)H - (13)C HSQC spectra.

A method for structure validation based on the simultaneous analysis of a 1D (1)H NMR and 2D (1)H - (13)C single-bond correlation spectrum such as HSQC or HMQC is presented here. When compared with the validation of a structure by a 1D (1)H NMR spectrum alone, the advantage of including a 2D HSQC spectrum in structure validation is that it adds not only the information of (13)C shifts, but also which proton shifts they are directly coupled to, and an indication of which methylene protons are diastereotopic. The lack of corresponding peaks in the 2D spectrum that appear in the 1D (1)H spectrum, also gives a clear picture of which protons are attached to heteroatoms. For all these benefits, combined NMR verification was expected and found by all metrics to be superior to validation by 1D (1)H NMR alone. Using multiple real-life data sets of chemical structures and the corresponding 1D and 2D data, it was possible to unambiguously identify at least 90% of the correct structures. As part of this test, challenging incorrect structures, mostly regioisomers, were also matched with each spectrum set. For these incorrect structures, the false positive rate was observed as low as 6%.

Major structural components in freshwater dissolved organic matter.

Dissolved organic matter (DOM) contains a complex array of chemical components that are intimately linked to many environmental processes, including the global carbon cycle, and the fate and transport of chemical pollutants. Despite its importance, fundamental aspects, such as the structural components in DOM remain elusive, due in part to the molecular complexity of the material. Here, we utilize multidimensional nuclear magnetic resonance spectroscopy to demonstrate the major structural components in Lake Ontario DOM. These include carboxyl-rich alicyclic molecules (CRAM), heteropolysaccharides, and aromatic compounds, which are consistent with components recently identified in marine dissolved organic matter. In addition, long-range proton-carbon correlations are obtained for DOM, which support the existence of material derived from linear terpenoids (MDLT). It is tentatively suggested that the bulk of freshwater dissolved organic matter is aliphatic in nature, with CRAM derived from cyclic terpenoids, and MDLT derived from linear terpenoids. This is in agreement with previous reports which indicate terpenoids as major precursors of DOM. At this time it is not clear in Lake Ontario whether these precursors are of terrestrial or aquatic origin or whether transformations proceed via biological and/ or photochemical processes.

The application of 1H high-resolution magic-angle spinning NMR for the study of clay-organic associations in natural and synthetic complexes.

The preferential sorption of model compounds to calcium-exchanged montmorillonite surfaces was investigated using 1H high-resolution magic-angle spinning (HR-MAS) and liquid-state NMR. Synthetic mixtures, representing the major structural categories abundant in natural organic matter (NOM), and two soil extracts were sorbed to montmorillonite. The NMR spectra indicate that, of the organic components observable by 1H HR-MAS NMR, aliphatic components preferentially sorb to the clay surface, while carbohydrates and amino acids mainly remain in the supernatant. These results may help explain the highly aliphatic nature of organic matter associated with clay fractions in natural soils and sediments. Investigations using the synthetic mixtures demonstrate a specific interaction between the clay surface and the polar region in 1-palmitoyl-3-stearoyl-rac-glycerol. Similar observations were obtained with natural soil extracts. The results presented have important implications for understanding the role of organoclay complexes in natural processes, and provides preliminary evidence that HR-MAS NMR is a powerful analytical technique for the investigation of organoclay complex structure and conformation.

Automated structure verification based on 1H NMR prediction.

A unique opportunity exists when an experimental NMR spectrum is obtained for which a specific chemical structure is anticipated. A process of Verification--the confirmation of a postulated structure--is now possible, as opposed to Elucidation-the de novo determination of a structure. A method for automated structure verification is suggested, which compares the chemical shifts, intensities and multiplicities of signals in an experimental 1H NMR spectrum with those from a predicted spectrum for the proposed structure. A match factor (MF) is produced and used to classify the spectrum-structure match into one of three categories, correct, ambiguous, or incorrect. The verification result is also augmented by the spectrum assignment obtained as part of the verification process. This method was tested on a set of synthetic spectra and several sets of experimental spectra, all of which were automatically prepared from raw data. Taking into account even the most problematic structures, with many labile protons present and poor prediction accuracy, 50% of all spectra can still be automatically verified without any false positives or negatives. In a blind test on a typical set of data, it is shown that fewer than 31% of the structures would need manual evaluation. This means that a system is possible whereby 69% of the spectra are prepared and evaluated automatically, and never need to be seen or evaluated by a human.

Assessing the organic composition of urban surface films using nuclear magnetic resonance spectroscopy.

Recently it has been shown that urban surfaces are covered with a thin film which mediates the fate, distribution and accumulation of semi-volatile organic compounds in the environment. In this study we apply a combination of solution, semi-solids, and solid state nuclear magnetic resonance (NMR) methods to provide a general overview of the organic constituents. In surface film collected from 30 m2 of outside windows over an area of 12 km2 in downtown Toronto, we roughly estimate that the organic carbon is approximately 35% carbohydrate, approximately 35% aliphatics, approximately 20% aromatics, and approximately 10% carbonyl groups. Various aliphatic groups can be identified including a number of acids, alcohols, alkanes, and alkenes. Also, numerous intact aliphatic esters are apparent that have not been observed before, as well as carbohydrates. The aromatic species include a small portion that appears to be derived from a polymer of styrene, in addition a larger fraction is consistent with polyhydroxylated PAH derived material, although this assignment is tentative and based solely on 1-D NMR data only. In addition, signals from polybutadiene are present and while accurate quantification is not possible, it appears that this polymer may be up to a few percents by weight of the total organic material.

Structure elucidation from 2D NMR spectra using the StrucEluc expert system: detection and removal of contradictions in the data.

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.

Identifying residues in natural organic matter through spectral prediction and pattern matching of 2D NMR datasets.

This paper describes procedures for the generation of 2D NMR databases containing spectra predicted from chemical structures. These databases allow flexible searching via chemical structure, substructure or similarity of structure as well as spectral features. In this paper we use the biopolymer lignin as an example. Lignin is an important and relatively recalcitrant structural biopolymer present in the majority of plant biomass. We demonstrate how an accurate 2D NMR database of approximately 600 2D spectra of lignin fragments can be easily constructed, in approximately 2 days, and then subsequently show how some of these fragments can be identified in soil extracts through the use of various search tools and pattern recognition techniques. We demonstrate that once identified in one sample, similar residues are easily determined in other soil extracts. In theory, such an approach can be used for the analysis of any organic mixtures.