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CASMI 2016: A manual approach for dereplication of natural products using tandem mass spectrometry.

ABSTRACT: The Critical Assessment of Small Molecule Identification (CASMI) contest is an initiative designed as an unbiased test of manual and automated strategies for the identification of small molecules from raw mass spectrometric data. In this contest, the participants are provided a set of high resolution MS and MS/MS data and asked to identify the unknown structure. CASMI 2016 is the fourth round of this contest in which the author participated in Category 1: Best Identification of Natural Products using a manual approach. The provided high resolution mass spectrometric data were interpreted manually using a combination of fragment and neutral loss analysis, literature consultation, manual database searches, deductive logic and experience. Out of 18 challenges, the author submitted correct structures as lead candidates for 14 challenges and 2nd ranked candidate for four challenges and was declared the winner of this category Pitfalls and challenges encountered during data interpretation are discussed.

SUBMITTER: Nikolic D

PROVIDER: S-EPMC5613954 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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CASMI 2016: A manual approach for dereplication of natural products using tandem mass spectrometry.

Nikolić Dejan D

Phytochemistry letters 20170204

The Critical Assessment of Small Molecule Identification (CASMI) contest is an initiative designed as an unbiased test of manual and automated strategies for the identification of small molecules from raw mass spectrometric data. In this contest, the participants are provided a set of high resolution MS and MS/MS data and asked to identify the unknown structure. CASMI 2016 is the fourth round of this contest in which the author participated in Category 1: Best Identification of Natural Products ...[more]

PMID: 28966697

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Project description:Diffusion-ordered NMR spectroscopy (DOSY) can be used to analyze mixtures of compounds since resonances deriving from different compounds are distinguished by their diffusion coefficients (D). Previously, DOSY has mostly been used for organometallic and polymer analysis, we have now applied DOSY to investigate diffusion coefficients of structurally diverse organic compounds such as natural products (NP). The experimental Ds derived from 55 diverse NPs has allowed us to establish a power law relationship between D and molecular weight (MW) and therefore predict MW from experimental D. We have shown that D is also affected by factors such as hydrogen bonding, molar density and molecular shape of the compound and we have generated new models that incorporate experimentally derived variables for these factors so that more accurate predictions of MW can be calculated from experimental D. The recognition that multiple physicochemical properties affect D has allowed us to generate a polynomial equation based on multiple linear regression analysis of eight calculated physicochemical properties from 63 compounds to accurately correlate predicted D with experimental D for any known organic compound. This equation has been used to calculate predicted D for 217 043 compounds present in a publicly available natural product database (DEREP-NP) and to dereplicate known NPs in a mixture based on matching of experimental D and structural features derived from NMR analysis with predicted D and calculated structural features in the database. These models have been validated by the dereplication of a mixture of two known sesquiterpenes obtained from Tasmannia xerophila and the identification of new alkaloids from the bryozoan Amathia lamourouxi. These new methodologies allow the MW of compounds in mixtures to be predicted without the need for MS analysis, the dereplication of known compounds and identification of new compounds based solely on parameters derived by DOSY NMR.

Dataset Information

CASMI 2016: A manual approach for dereplication of natural products using tandem mass spectrometry.

Publications

CASMI 2016: A manual approach for dereplication of natural products using tandem mass spectrometry.

Similar Datasets

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