Taxonomically informed scoring enhances confidence in natural products annotation

Abstract

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

Surface sensing triggers a broad‐spectrum antimicrobial response in Pseudomonas aeruginosa

“…The HR/MSMS datasets were organized as a molecular network (MN), which provides tandem fragmentation spectra according to their spectral similarity (Wang et al ., 2016). The experimental fragmentation dataset in the MN was annotated by comparison with a database of simulated spectra of natural products obtained by in silico MS/MS fragmentation (Allard et al ., 2016) and following a taxonomically informed metabolite annotation process (Rutz et al ., 2019). In a MN, a cluster of nodes is generally indicative of a family of structurally related molecules.…”

“…The converted files were treated using the MZMine software suite v. 2.39 (Pluskal et al ., 2010). The dereplication strategy consisted of a combination of molecular networking and an in silico generated fragmentation database spectral matching, informed by taxonomic information (Rutz et al ., 2019). Dereplication results were then visualized as chemical structures, and their relative abundance in both samples was estimated as previously described (Allard et al ., 2016).…”

Assigning confidence to structural annotations from mass spectra with COSMIC

“…Furthermore, metascores tend to prefer highly cited “blockbuster metabolite” candidates; hence, evaluation results, which are carried out using mainly such “blockbuster metabolites”, are often exaggerated. Similar limitations are associated with metascores based on taxonomy 57 as again, this information is not available for “truly novel” structures. Thus, we ignored metascore methods in our evaluations.…”

“…Finally, some tools use networks for structure annotations; networks may be based on spectral similarity in the LC-MS/MS run, or structural similarity in the metabolite database [57][58][59][60] .…”

The LOTUS Initiative for Open Natural Products Research: Knowledge Management through Wikidata

“…data and thus open exciting perspectives in the fields of dereplication and NPs annotation. We previously demonstrated that taxonomically-informed metabolite annotation critically improves the NPs annotation process (Rutz et al, 2019). The availability of an open repository linking chemical objects to both their spectral information and biological occurrences will facilitate and improve such applications.…”