LC-HRMS-Database Screening Metrics for Rapid Prioritization of Samples to Accelerate the Discovery of Structurally New Natural Products

Jioji N. Tabudravu* (Corresponding Author), Léonie Pellissier, Alan James Smith, Karolina Subko, Caroline Autréau, Klaus Feussner, David Hardy, Daniel Butler, Richard Kidd, Edward J Milton, Hai Deng, Rainer Ebel, Marika Salonna, Carmela Gissi, Federica Montesanto, Sharon M Kelly, Bruce F Milne, Gabriela Cimpan, Marcel Jaspars* (Corresponding Author)

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)
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In order to accelerate the isolation and characterization of structurally new or novel secondary metabolites, it is crucial to develop efficient strategies that prioritize samples with greatest promise early in the workflow so that resources can be utilized in a more efficient and cost-effective manner. We have developed a metrics-based prioritization approach using exact LC-HRMS, which uses data for 24 618 marine natural products held in the PharmaSea database. Each sample was evaluated and allocated a metric score by a software algorithm based on the ratio of new masses over the total (sample novelty), ratio of known masses over the total (chemical novelty), number of peaks above a defined peak area threshold (sample complexity), and peak area (sample diversity). Samples were then ranked and prioritized based on these metric scores. To validate the approach, eight marine sponges and six tunicate samples collected from the Fiji Islands were analyzed, metric scores calculated, and samples targeted for isolation and characterization of new compounds. Structures of new compounds were elucidated by spectroscopic techniques, including 1D and 2D NMR, MS, and MS/MS. Structures were confirmed by computer-assisted structure elucidation methods (CASE) using the ACD/Structure Elucidator Suite.

Original languageEnglish
Pages (from-to)211-220
Number of pages10
JournalJournal of Natural Products
Issue number2
Early online date8 Feb 2019
Publication statusPublished - 2019

Bibliographical note

Acknowledgments: The research leading to these results received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 312184 “PharmaSea” to M.J., R.E., J.T., H.D., R.K., and G.C. J.T. wishes to thank V. Paget and the ACD/Laboratories Software Development Team for software assistance and G. McGibbon of ACD/Laboratories for constructive discussions. M.S., C.G., and F.M. wish to thank Francesco Mastrototaro, Department of Biology - LRU CoNISMa, University of Bari, Via Orabona 4, 70125, Bari, Italy, for help with ascidian identification. J.T. and M.J. wish to thank R. Gray of the Marine Biodiscovery Centre, University of Aberdeen, for 2D NMR spectroscopic data. B.F.M. thanks the Laboratory for Advanced Computing (LCA) of the University of Coimbra for computing time, and the Portuguese Foundation for Science and Tehnology for financial support under project POCI-01-0145-FEDER-032229.


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