Nuclear magnetic resonance database techniques

Nuclear magnetic resonance spectroscopy is a powerful technique for investigating structure of biomolecules. The 1H- and 13C-NMR spectra of L-a-amino acids have been compiled (Wiithrich, 1986) and can be retrieved from SDBS. Design a database for 1H- or 13C-NMR data that can be used in the identification of amino acids. 

From the analysis of the data in the LIPID AT database (41), more than 150 different methods and method modifications have been used to collect data related to the lipid phase transitions. Almost 90% of the data is accounted for by less than 10 methods. Differential scaiming calorimetry strongly dominates the field with two thirds of all phase transition records. From the other experimental techniques, various fluorescent methods account for 10% of the information records. X-ray diffraction, nuclear magnetic resonance (NMR), Raman spectroscopy, electron spin resonance (ESR), infrared (IR) spectroscopy, and polarizing microscopy each contribute to about or less than 2-3% of the phase transition data records in the database. Especially useful in gaining insight into the mechanism and kinetics of lipid phase transitions has been time-resolved synchrotron X-ray diffraction (62,78-81). 

In the pharmaceutical industry, the techniques are being used to examine off-target effects particularly for the early identification of toxicity. MOA can be studied through metabolomics and can also be used as a quality control tool for complex mixtures such as foods or herbal medicines. Similarly, the tools and expertise of natural products chemists are essential in metabolomics, particularly in biomarker discovery (see also Volume 9). Biomarker discovery via untargeted metabolomics can lead to metabolite signatures (nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry (MS), etc.) that are not present in current metabolomics databases. This is particularly true for plant secondary metabolism studies and nonmammalian metabolites. Structure elucidation then becomes critical to understanding the metabolomics results and for biomarker development. 

Nuclear magnetic resonance (NMR) spectroscopy is the most informative analytical technique and is widely applied in combinatorial chemistry. However, an automated interpretation of the NMR spectral results is difficult (3,4). Usually the interpretation can be supported by use of spectrum calculation (5-18) and structure generator programs (8,12,18-21). Automated structure validation methods rely on NMR signal comparison using substructure/ subspectra correlated databases or shift prediction methods (8,15,22,23). We have recently introduced a novel NMR method called AutoDROP (Automated Definition and Recognition of Patterns) to rapidly analyze compounds libraries (24-29). The method is based on experimental data obtained from the measured ID or 2D iH,i C correlated (HSQC) spectra. 

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