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An Expanded Database of High-Resolution MS/MS Spectra for Lichen Derived Natural Products

ABSTRACT:

The history of lichen compound identification has long relied on techniques such as spot tests and TLC, which have been surpassed in sensitivity and accuracy by modern metabolomic techniques such as high-resolution MS/MS. In 2019, Olivier-Jimenez et al. released the Lichen DataBase (LDB), a library containing the Q-TOF MS/MS spectra of 251 metabolites on the GNPS and MetaboLights platforms, that has been widely used for the identification of lichen-derived unknowns. To increase the compound coverage, we have generated the Orbitrap MS/MS spectra of a further 534 lichen-derived compounds from the metabolite library of Jack Elix, housed at the CANB herbarium (Canberra, Australia). This included 399 unique metabolites that are not in the LDB, bringing the total number combined to 650. Technical validation was achieved by investigating the compounds in three Australian lichen extracts using the Library Search and Molecular Networking tools on the GNPS platform. This update provides a much larger database for lichen compound identification, which we envisage will allow refining of the chemotaxonomy framework and contribute to compound discovery.

INSTRUMENT(S): Liquid Chromatography MS -

PROVIDER: MTBLS8109 | MetaboLights | 2024-10-09

REPOSITORIES: MetaboLights

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Project description:Large-scale identification of N-linked intact glycopeptides by liquid chromatography coupled tandem mass spectrometry (LC-MS/MS) in human serum is challenging due to the wide dynamic range of serum protein abundances, the lack of a complete serum N-Glycan database and the existence of non-specifically digested peptides and numerous modifications. In this regard, a spectral library search method was presented for serum N-linked intact glycopeptides identification with target-decoy and motif-specific false discovery rate (FDR) control. Low-abundant glycoproteins were firstly separated from high-abundant proteins by acetonitrile (ACN) precipitation. After digestion, the N-linked intact glycopeptides were enriched by hydrophilic interaction liquid chromatography (HILIC) column and a portion of the enriched N-linked intact glycopeptides were processed by N-Glycosidase F (PNGase F) to generate de-glycopeptides. Both N-linked intact glycopeptides and de-glycopeptides were analyzed by LC-MS/MS. From N-linked de-glycopeptides datasets, 764 N-linked glycoproteins, 1,699 N-linked glycosites and 3,328 unique N-linked de-glycopeptides were identified. The spectra of these N-linked de-glycopeptides were utilized for N-linked de-glycopeptides library construction and identification of N-linked intact glycopeptides. Four types of N-linked glycosylation motifs (NXS/T/C/V, X≠P) were used to recognize the N-linked de-glycopeptides, and two different N-Glycan mass databases including 27 modified N-Glycan masses and 712 unmodified N-Glycan masses were combined and utilized during spectral library search for identification of N-linked intact glycopeptides. In total, from the N-linked intact glycopeptides datasets, 526 N-linked glycoproteins, 1,036 N-linked glycosites, 22,677 N-linked intact glycopeptides and 738 N-Glycan masses were identified under 1% FDR, representing the most in-depth serum N-glycoproteome identified by LC-MS/MS at N-linked intact glycopeptide level and N-linked de-glycopeptides level.

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An Expanded Database of High-Resolution MS/MS Spectra for Lichen Derived Natural Products

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