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

Jioji N. Tabudravu; 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

doi:10.1021/acs.jnatprod.8b00575

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

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Abstract

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 language	English
Pages (from-to)	211-220
Number of pages	10
Journal	Journal of Natural Products
Volume	82
Issue number	2
Early online date	8 Feb 2019
DOIs	https://doi.org/10.1021/acs.jnatprod.8b00575
Publication status	Published - 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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This output contributes to the following UN Sustainable Development Goals (SDGs)

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Accepted Manuscript
This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Natural Products, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://pubs.acs.org/doi/10.1021/acs.jnatprod.8b00575.
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Cite this

Tabudravu, J. N., Pellissier, L., Smith, A. J., Subko, K., Autréau, C., Feussner, K., Hardy, D., Butler, D., Kidd, R., Milton, E. J., Deng, H., Ebel, R., Salonna, M., Gissi, C., Montesanto, F., Kelly, S. M., Milne, B. F., Cimpan, G., & Jaspars, M. (2019). LC-HRMS-Database Screening Metrics for Rapid Prioritization of Samples to Accelerate the Discovery of Structurally New Natural Products. Journal of Natural Products, 82(2), 211-220. https://doi.org/10.1021/acs.jnatprod.8b00575

Tabudravu, JN, Pellissier, L, Smith, AJ, Subko, K, Autréau, C, Feussner, K, Hardy, D, Butler, D, Kidd, R, Milton, EJ, Deng, H , Ebel, R, Salonna, M, Gissi, C, Montesanto, F, Kelly, SM, Milne, BF, Cimpan, G & Jaspars, M 2019, 'LC-HRMS-Database Screening Metrics for Rapid Prioritization of Samples to Accelerate the Discovery of Structurally New Natural Products', Journal of Natural Products, vol. 82, no. 2, pp. 211-220. https://doi.org/10.1021/acs.jnatprod.8b00575

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

abstract = "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.",

author = "Tabudravu, {Jioji N.} and L{\'e}onie Pellissier and Smith, {Alan James} and Karolina Subko and Caroline Autr{\'e}au and Klaus Feussner and David Hardy and Daniel Butler and Richard Kidd and Milton, {Edward J} and Hai Deng and Rainer Ebel and Marika Salonna and Carmela Gissi and Federica Montesanto and Kelly, {Sharon M} and Milne, {Bruce F} and Gabriela Cimpan and Marcel Jaspars",

note = "Acknowledgments: The research leading to these results received funding from the European Union{\textquoteright}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.",

year = "2019",

doi = "10.1021/acs.jnatprod.8b00575",

language = "English",

volume = "82",

pages = "211--220",

journal = "Journal of Natural Products",

issn = "0163-3864",

publisher = "American Chemical Society",

number = "2",

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T1 - LC-HRMS-Database Screening Metrics for Rapid Prioritization of Samples to Accelerate the Discovery of Structurally New Natural Products

AU - Tabudravu, Jioji N.

AU - Pellissier, Léonie

AU - Smith, Alan James

AU - Subko, Karolina

AU - Autréau, Caroline

AU - Feussner, Klaus

AU - Hardy, David

AU - Butler, Daniel

AU - Kidd, Richard

AU - Milton, Edward J

AU - Deng, Hai

AU - Ebel, Rainer

AU - Salonna, Marika

AU - Gissi, Carmela

AU - Montesanto, Federica

AU - Kelly, Sharon M

AU - Milne, Bruce F

AU - Cimpan, Gabriela

AU - Jaspars, Marcel

N1 - 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.

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

U2 - 10.1021/acs.jnatprod.8b00575

DO - 10.1021/acs.jnatprod.8b00575

M3 - Article

C2 - 30735391

SN - 0163-3864

VL - 82

SP - 211

EP - 220

JO - Journal of Natural Products

JF - Journal of Natural Products

IS - 2

ER -

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

Abstract

Bibliographical note

UN SDGs

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