Mass spectrometry (MS) offers unrivalled sensitivity for the metabolite profiling of complex biological matrices encountered in natural products (NP) research. The massive and complex sets of spectral data generated by such platforms require computational approaches for their interpretation. Within such approaches, computational metabolite annotation automatically links spectral data to candidate structures via a score, which is usually established between the acquired data and experimental or theoretical spectral databases (DB). This process leads to various candidate structures for each MS features. However, at this stage, obtaining high annotation confidence level remains a challenge notably due to the extensive chemodiversity of specialized metabolomes. The design of a metascore is a way to capture complementary experimental attributes and improve the annotation process. Here, we show that integrating the taxonomic position of the biological source of the analyzed samples and candidate structures enhances confidence in metabolite annotation. A script is proposed to automatically input such information at various granularity levels (species, genus, and family) and complement the score obtained between experimental spectral data and output of available computational metabolite annotation tools (ISDB-DNP, MS-Finder, Sirius). In all cases, the consideration of the taxonomic distance allowed an efficient re-ranking of the candidate structures leading to a systematic enhancement of the recall and precision rates of the tools (1.5- to 7-fold increase in the F1 score). Our results clearly demonstrate the importance of considering taxonomic information in the process of specialized metabolites annotation. This requires to access structural data systematically documented with biological origin, both for new and previously reported NPs. In this respect, the establishment of an open structural DB of specialized metabolites and their associated metadata, particularly biological sources, is timely and critical for the NP research community.
Carbohydrates are complex structures that still challenge analysts today because of their different levels of isomerism, notably the anomerism of the glycosidic bond. It has been shown recently that anomerism is preserved upon gas-phase fragmentation and that high-resolution ion mobility (IMS) can distinguish anomers. However, these concepts have yet to be applied to complex biological products. We have used high-resolution IMS on a cyclic device to characterize the reaction products of Uhgb_MS, a novel mannoside synthase of the GH130 family. We designed a so-called IMS n sequence consisting of (i) separating and isolating specific IMS peaks, (ii) ejecting ions to a pre-array store cell depending on their arrival time, (iii) inducing collisional activation upon reinjection, and (iv) performing multistage IMS analysis of the fragments. First, we applied IMS2 sequences to purely linked α1,2- and β1,2-mannooligosaccharides, which provided us with reference drift times for fragments of known conformation. Then, we performed IMS n analyses of enzymatically produced mannosides and, by comparison with the references, we succeeded in determining the intrachain anomerism of a α1,2-mannotriose and a mix-linked β/α1,2-mannotetraosea first for a crude biological medium. Our results show that the anomerism of glycosides is maintained through multiple stages of collisional fragmentation, and that standalone high-resolution IMS and IMS n can be used to characterize the intrachain anomerism in tri- and tetrasaccharides in a biological medium. This is also the first evidence that a single carbohydrate-active enzyme can synthesize both α- and β-glycosidic linkages.
25 Mass spectrometry (MS) hyphenated to liquid chromatography (LC)-MS offers unrivalled sensitivity 26 for metabolite profiling of complex biological matrices encountered in natural products (NP) research. 27 With advanced platforms LC, MS/MS spectra are acquired in an untargeted manner on most detected 28 features. This generates massive and complex sets of spectral data that provide valuable structural 29 information on most analytes. To interpret such datasets, computational methods are mandatory. To 30 this extent, computerized annotation of metabolites links spectral data to candidate structures. When 31 profiling complex extracts spectra are often organized in clusters by similarity via Molecular 32 Networking (MN). A spectral matching score is usually established between the acquired data and 33 experimental or theoretical spectral databases (DB). The process leads to various candidate structures 34 for each MS features. At this stage, obtaining high annotation confidence level remains a challenge 35 notably due to the high chemodiversity of specialized metabolomes. 36
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