Integration of Molecular Networking and In-Silico MS/MS Fragmentation for Natural Products Dereplication

Abstract

Dereplication represents a key step for rapidly identifying known secondary metabolites in complex biological matrices. In this context, liquid-chromatography coupled to high resolution mass spectrometry (LC-HRMS) is increasingly used and, via untargeted data-dependent MS/MS experiments, massive amounts of detailed information on the chemical composition of crude extracts can be generated. An efficient exploitation of such data sets requires automated data treatment and access to dedicated fragmentation databases. Various novel bioinformatics approaches such as molecular networking (MN) and in-silico fragmentation tools have emerged recently and provide new perspective for early metabolite identification in natural products (NPs) research. Here we propose an innovative dereplication strategy based on the combination of MN with an extensive in-silico MS/MS fragmentation database of NPs. Using two case studies, we demonstrate that this combined approach offers a powerful tool to navigate through the chemistry of complex NPs extracts, dereplicate metabolites, and annotate analogues of database entries.

Illuminating a plant’s tissue-specific metabolic diversity using computational metabolomics and information theory

“…Notably, the potential of mass spectrometry (MS)-based metabolomics and of the large-scale acquisition of tandem MS (MS/MS) spectra for as many metabolites as possible within a metabolic profile is severely constrained by the absence of straightforward classification and visualization pipelines that enable facile pathway interpretations. Metabolite annotation and identification are the obvious bottlenecks that thwart the metabolomics analysis of secondary metabolism (13,14). Ideally, we need approaches that combine the strengths of state-of-the-art statistical methods currently emerging from the genomics field with the recent advances in metabolomics data mining, such as the method of MS/MS molecular networking, which allow unknown metabolites to be readily classified based solely on their fragmentation patterns.…”

Current and Future Perspectives on the Structural Identification of Small Molecules in Biological Systems

“…Regular competitions through the Critical Assessment of Small Molecule Identification (CASMI) [114] drive the development and testing of new methodologies and approaches. Recently, for example using CFM and spectral matching (Tremolo), an in silico fragmented database of more than 220,000 natural products [115] was used to enhance the precision of annotations in a dereplication workflow where only molecular formula and taxonomy cross searched assignments are used.…”

“…In particular, it will require the development of sophisticated computational methods that can predict or interpret MS/MS and EI-MS spectra using known (or predicted) chemical structures. There are more than 300,000 natural products that have had their structures described [115] and more than 60 million synthetic compounds that have been synthesized and have had their structures deposited in PubChem [151]. Rather than trying to isolate, resynthesize or re-acquire these compounds (which would cost billions of dollars), it would be far easier to predict their MS/MS or EI-MS spectra or to see if observed MS/MS or EI-MS spectra fit to existing structures.…”

“…These predictive tools are now being exploited to perform novel metabolite identification. In one particularly impressive example, MS/MS spectra were predicted via CFM-ID for all of the compounds in the Dictionary of Natural Products [115]. By constructing a spectral similarity network and using sophisticated spectral matching comparisons it was possible to use this network of predicted MS/MS spectra to identify a number of previously unidentified compounds [155].…”

Antibiotic Drug Discovery

“…For example, solutions such as MAGMa (MS annotation based on in silico generated metabolites) allow matching of multistage fragmentation data against candidate molecules substructures and were successfully applied on complex extracts (van der Hooft et al, 2012;Ridder et al, 2014;Allard et al, 2016). Among these new approaches, molecular networking (MN) is a particularly effective one to organize MS/MS fragmentation spectra.…”