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.
Highlights
Specialized metabolites define the chemical signature of a living organism
The initial hypothesis of this work is that the attribution of a score reflecting the taxonomic distance between the biological source of the queried analyte and the one of the candidate structures, is a valuable input for a metabolite annotation process
The metabolite annotation process, which can be resumed to the comparison of attributes of the queried features against attributes of the candidate structures can benefit from information complementary to the classically used Mass spectrometry (MS)/MS fragmentation
Summary
Specialized metabolites define the chemical signature of a living organism. Plants, sponges and corals, and microorganisms (bacteria and fungi), are known to biosynthesize a wealth of such chemicals, which can play a role as defense or communication agents (Brunetti et al, 2018). Developments in metabolite profiling by mass spectrometry (MS) grant access to large volumes of high-quality spectral data from minimal amount of samples and appropriate data analysis workflows allow to efficiently mine such data (Wolfender et al, 2019) Initiatives such as the Global Natural Products Social (GNPS) molecular networking (MN) project offer both a living MS repository and the possibility to establish MN organizing MS data (Wang et al, 2016). Metabolite annotation can be applied at a higher throughput and offers an effective proxy for the chemical characterization of complex matrices This process includes dereplication (the annotation of previously described molecules prior to any physical isolation process) and allows focusing isolation and metabolite identification efforts on potentially novel compounds only (Gaudêncio and Pereira, 2015)
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