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Targeted Dereplication of Microbial Natural Products by High-Resolution MS and Predicted LC Retention Time
EP34435
Poster Title: Targeted Dereplication of Microbial Natural Products by High-Resolution MS and Predicted LC Retention Time
Submitted on 05 Nov 2020
Author(s): Chervin J,1 Stierhof M,1 Tong MH,1 Peace D,1 Hansen KØ,2 Urgast DS,1 Andersen JH,2 Yu Y,3 Ebel R,1 Kyeremeh K,4 Paget V,5 Cimpan G,5 Van Wyk A,5 McKee M,5 Deng H,1 Jaspars M,1 Tabudravu JN1
Affiliations: 1The Marine Discovery Centre, Department of Chemistry, 2Marbio, UiT The Arctic University of Norway, 3Key Laboratory of Combinatory Biosynthesis and Drug Discovery, School of Pharmaceutical Sciences, Wuhan University, 4Marine and Plant Research Laboratory of Ghana, Department of Chemistry, University of Ghana, 5Advanced Chemistry Development, Inc., University of Aberdeen
Poster Views: 206
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Poster Information
Abstract: An alternative to standard natural product dereplication methods, where an unknown’s LC/HRMS retention time (tR) is compared to that of known standards, is the use of predicted tR data.
Specifically, tR predictions can be coupled with HRMS as an extra filter in high-throughput screening strategies. This has the potential to allow rapid identification of known natural products, improving methods for isolation of novel compounds.
Our study goal was to rapidly identify known natural products from Streptomyces sp. so we could focus structural analysis on novel compounds. We built a database of compounds from Streptomyces (StrepDB) which contains structure, molecular formula, and predicted tR data. We also constructed a database which contains NMR, LC/MS, MS/MS, and UV data for 665 natural products (MbcDB) to provide spectral information for structure elucidation. We tested this combined database approach by investigating the screening, isolation, and characterization of new pyrrolidine alkaloids in a mutant Streptomyces extract.
Summary: We detail a new strategy to identify known compounds in Streptomyces extracts that can be applied to natural product discovery. We use a high-throughput LC/MS data processing algorithm to screen a database of 5555 natural products (StrepDB), filtering via HRMS data and predicted LC tR values, for rapid identification of known compounds. A database containing HRESIMS, MS/MS, UV, and NMR spectral data for 665 natural products (MbcDB) is then used for structure elucidation.References: 1. Blunt JW, et al., Eds. 2006. AntiMarin Database; U. Canterbury, U. Göttingen.
2. Enzo Life Sciences. http://www.enzolifesciences.com/ (accessed 6/9/18).
3. Zou KH, et al. (2004). Acad Radiol., 11(2): 178-89.
4. Huang S et al. (2015). Angew Chem Int Ed., 54(43): 12697-701.
5. Putri SP, et al. (2009). J. Nat. Prod., 72(8): 1544-46.
6. Williams A, et al. (2016). Mod. NMR Appr. Struct. Elucidat. Nat. Prod. Williams A et al. (Ed.), United Kingdom.
7. Elyashberg M, et al. (2011). Cont. Comp-Assisted Approaches to Mol. Struc. Eluc. Price W et al (Eds.), United Kingdom.
8. Rateb M, et al. (2016). In Ramesh V (Ed.), Nuclear Magnetic Resonance: Volume 45 (240-68). United Kingdom.
9. Elyashberg M, et al. (2010). Nat. Prod. Rep., 27(9): 1296.
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