Abstract
BackgroundIn metabolomics experiments, spectral fingerprints of metabolites with no known structural identity are detected routinely. Computer-assisted structure elucidation (CASE) has been used to determine the structural identities of unknown compounds. It is generally accepted that a single 1D NMR spectrum or mass spectrum is usually not sufficient to establish the identity of a hitherto unknown compound. When a suite of spectra from 1D and 2D NMR experiments supplemented with a molecular formula are available, the successful elucidation of the chemical structure for candidates with up to 30 heavy atoms has been reported previously by one of the authors. In high-throughput metabolomics, usually 1D NMR or mass spectrometry experiments alone are conducted for rapid analysis of samples. This method subsequently requires that the spectral patterns are analyzed automatically to quickly identify known and unknown structures. In this study, we investigated whether additional existing knowledge, such as the fact that the unknown compound is a natural product, can be used to improve the ranking of the correct structure in the result list after the structure elucidation process.ResultsTo identify unknowns using as little spectroscopic information as possible, we implemented an evolutionary algorithm-based CASE mechanism to elucidate candidates in a fully automated fashion, with input of the molecular formula and 13C NMR spectrum of the isolated compound. We also tested how filters like natural product-likeness, a measure that calculates the similarity of the compounds to known natural product space, might enhance the performance and quality of the structure elucidation. The evolutionary algorithm is implemented within the SENECA package for CASE reported previously, and is available for free download under artistic license at http://sourceforge.net/projects/seneca/. The natural product-likeness calculator is incorporated as a plugin within SENECA and is available as a GUI client and command-line executable. Significant improvements in candidate ranking were demonstrated for 41 small test molecules when the CASE system was supplemented by a natural product-likeness filter.ConclusionsIn spectroscopically underdetermined structure elucidation problems, natural product-likeness can contribute to a better ranking of the correct structure in the results list.
Highlights
In metabolomics experiments, spectral fingerprints of metabolites with no known structural identity are detected routinely
Successful structure determination based on a limited set of spectroscopic information is important. 1D proton NMR is the most commonly used NMR method in metabolomics; in the present study, we based the structure elucidation on carbon-13 NMR because reliable open-source methods for proton prediction, including the coupling constants required to produce realistic 1D spectra, are not available
To test the enhancement in candidate ranking with the application of natural product (NP)-likeness, we performed two different evaluations for the 41 test cases
Summary
To identify unknowns using as little spectroscopic information as possible, we implemented an evolutionary algorithm-based CASE mechanism to elucidate candidates in a fully automated fashion, with input of the molecular formula and 13C NMR spectrum of the isolated compound. We tested how filters like natural product-likeness, a measure that calculates the similarity of the compounds to known natural product space, might enhance the performance and quality of the structure elucidation. The evolutionary algorithm is implemented within the SENECA package for CASE reported previously, and is available for free download under artistic license at http://sourceforge. The natural product-likeness calculator is incorporated as a plugin within SENECA and is available as a GUI client and command-line executable. Significant improvements in candidate ranking were demonstrated for 41 small test molecules when the CASE system was supplemented by a natural product-likeness filter
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