MS/MS-Based Molecular Networking: An Efficient Approach for Natural Products Dereplication

Abstract

Natural products (NPs) have historically played a primary role in the discovery of small-molecule drugs. However, due to the advent of other methodologies and the drawbacks of NPs, the pharmaceutical industry has largely declined in interest regarding the screening of new drugs from NPs since 2000. There are many technical bottlenecks to quickly obtaining new bioactive NPs on a large scale, which has made NP-based drug discovery very time-consuming, and the first thorny problem faced by researchers is how to dereplicate NPs from crude extracts. Remarkably, with the rapid development of omics, analytical instrumentation, and artificial intelligence technology, in 2012, an efficient approach, known as tandem mass spectrometry (MS/MS)-based molecular networking (MN) analysis, was developed to avoid the rediscovery of known compounds from the complex natural mixtures. Then, in the past decade, based on the classical MN (CLMN), feature-based MN (FBMN), ion identity MN (IIMN), building blocks-based molecular network (BBMN), substructure-based MN (MS2LDA), and bioactivity-based MN (BMN) methods have been presented. In this paper, we review the basic principles, general workflow, and application examples of the methods mentioned above, to further the research and applications of these methods.

Discovery and engineering of ribosomally synthesized and post-translationally modified peptide (RiPP) natural products

“…Incorporation of MS-based dereplication methods into the bioactivity guided process could be useful to ensure a more accurate and efficient discovery of novel RiPPs. [181][182][183][184][185][186] The expanding wealth of knowledge regarding RiPP biosynthesis has propelled the genome mining strategy to an increasingly significant role in RiPP discovery. When specific details about a RiPP biosynthetic gene cluster are identified, the conserved and vital PTM enzymes within it can be utilized for comprehensive gene mining within the same RiPP family.…”

Molecular Networking-Guided Isolation of a Phenolic Constituent from Prunus mume Seed and Its Antioxidant and Anti-Inflammatory Activities

“…To circumvent this issue, artificial intelligence technology was developed in NPs research. MS/MS-based GNPS molecular networking can produce visual networks consisting of chemically associated compounds and enables the identification of known compounds, structural class, and analogs [ 44 ]. This study revealed that a combination of bioactivity evaluation with an MS/MS-based GNPS approach can allow us to prioritize various Prunus mume extracts in the early fractionation process and improve the accuracy of target compound prediction.…”

Introducing Molecular Hypernetworks for Discovery in Multidimensional Metabolomics Data

“…Critically, while the literature reviewed for this work mentions the suitability of MNs for analysis by graph/network theory algorithms, such appli-cations have been limited to minimum path length queries. 4,17 In future works, we intend to further develop MHN functionality to facilitate a “network analysis tool suite”, prospectively easing the process by which data-informed (bio)chemical interpretations are made.…”