Taxonomically Informed Scoring Enhances Confidence in Natural Products Annotation

Abstract

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.

MS-CleanR: A feature-filtering approach to improve annotation rate in untargeted LC-MS based metabolomics

“…In the case of multiple DB interrogation, the workflow allows compound annotations to be ranked based on MS-FINDER score only or by prioritizing certain DBs, depending on user choices. This latter function can greatly improve the annotation accuracy particularly when dealing with taxonomically defined extracts 30 . MS-CleanR can also prioritize compounds based on “Compound_level” column tuned by the user in external DBs used for MS-FINDER annotation.…”

Insights on the Structural and Metabolic Resistance of Potato (Solanum tuberosum) Cultivars to Tuber Black Dot (Colletotrichum coccodes)

“…Furthermore, taxonomically informed scoring was applied on the GNPS outputs using Solanum tuberosum as species, Solanum as genus, and Solanaceae as family, returning an attribute table which can be directly loaded in Cytoscape. The taxonomically informed metabolite annotation process has been previously described in detail (Rutz et al, 2019). The scripts are available online (taxo_scorer_user.Rmd) at https:// github.com/oolonek/taxo_scorer.…”

“…In addition, metabolite annotation was carried out by a combination of feature based molecular networking (FBMN) (Nothias et al, 2019) and dereplication against experimental data from the GNPS platform (http://gnps. ucsd.edu/) and against an in-silico fragmentation database (ISDB) weighted using taxonomical data ( Figure 5D) (Allard et al, 2016;Rutz et al, 2019). Furthermore, a selection of the annotated compounds was identified by comparison of the HRMS, MS/MS spectra and RT with authentic standards, which allowed the establishment of experimental anchor points and the confident propagation of annotations through the network.…”

“…Furthermore, metabolite annotation is performed using experimental data (www.gnps. ucsd.edu), an in silico mass spectral database (Allard et al, 2016) and taxonomical scoring (Rutz et al, 2019) that allow high confidence feature annotation.…”

Probabilistic Framework for Integration of Mass Spectrum and Retention Time Information in Small Molecule Identification

“…Sources of additional information include, e.g. RT (Ruttkies et al, 2016;Bach et al, 2018;Samaraweera et al, 2018), collision crosssection (Plante et al, 2019), or prior knowledge on the data generating process, such as the source organism's metabolic characteristics (Rutz et al, 2019). Retention time, that is, the time that a molecule takes to elute from the LC column, is readily available in all LC-MS pipelines, and is frequently used in aiding annotation (Stanstrup et al, 2015).…”