Application of Mass Spectrometry Molecular Networks in the Study of Natural Product Structures
The structural analysis of compounds is crucial for the discovery of natural products, especially for revealing the material basis of rare or precious Chinese medicinal materials. However, the analysis of highly diverse compound structures requires a significant amount of time and manpower, which is one of the biggest challenges in characterizing natural products. Therefore, mass spectrometry molecular network technology is applied to the structural research of natural products.

Liquid chromatography tandem mass spectrometry (LC-MS/MS) is one of the most commonly used analytical methods in metabolomics, however, interpreting these complex data is a major challenge in natural product research. In recent years, the emergence of new bioinformatics methods such as molecular networks has provided new ideas and perspectives for the recognition of known compounds in complex matrices. The Global Natural Products Social Molecular Networking (GNPS) is an open database of tandem mass spectrometry data, currently the only public foundational platform capable of implementing molecular networks and analyzing datasets generated by LC-MS/MS. The visualization of molecular networks in GNPS represents each spectrum as a node, and the arrangement of spectra to spectra represents the connections between nodes. Relevant molecular spectra can be visualized as molecular networks online in GNPS. GNPS consists of a total of 235850 spectra and 22644 compounds. GNPS not only enables the identification of known compounds, analogues, and automatic analysis of compounds in molecular networks, but also has the ability to replicate, link, and store secondary mass spectrometry data from multiple sources.

Molecular networking (MN) is a platform for organizing and visualizing MS/MS data. Each mass spectrum is considered a vector and compared to all other mass spectra using cosine similarity. When the similarity between two mass spectra exceeds a threshold, they are connected together in the molecular network. In 2012, Professor Pieter first proposed molecular network technology. Mass spectrometry molecular network is a method based on tandem mass spectrometry analysis (MS/MS), suitable for the analysis of various natural products. Its purpose is to quickly identify known compounds and determine various unknown natural products. In short, this method involves collecting tandem mass spectrometry data and constructing a network based on fragment similarity. This technology has been widely applied in fields such as natural products, metabolomics, and drug discovery.

Currently, Trivella et al. and Fox Ramos et al. have reported that MN has been proven to be a highly effective tool for quickly identifying natural products in complex mixtures and aiding in the discovery of new natural products; Nothias Esposito et al. reported that MN is an effective tool for characterizing the generation of specific metabolites in plants in metabolomics based on mass spectrometry; Kang et al. used MN to classify secondary metabolites into similar clusters, which has been used in natural product research to screen and isolate targets; Lei et al. observed differences in the content of unprocessed and processed aconite using MN; In addition, Tian et al. reported that MN can clearly observe the differences in chemical composition of tea from different varieties and origins, thus enabling qualitative and quantitative evaluation of tea; By analyzing the types of fragment ions and the relative content changes of fragment ions, the changes in the molecular structure of natural products and the transformation of their functional groups can be inferred to determine whether natural products are generated or transformed.

 

In the past few years, MN has been proposed as an emerging tool for simplifying the structural research of natural products, and has been successfully applied to the discovery, separation, preparation, structural identification, and quantitative analysis of chemical and active ingredients in natural products. MN is suitable for studying the in vivo composition and metabolites of natural products, which can better elucidate their pharmacological substance basis and metabolic processes.

By using MN to compare characteristic fragment ions and analyze their types and relative content changes, we can infer the molecular structure changes and functional group transformations of natural products, which can be used to infer the biosynthetic pathways of natural products. However, literature research has found that there are not many relevant reports at home and abroad, which may be a new direction for MN to be used in natural product research.

In summary, so far, MN has mainly been applied in the structural identification of natural products. When combined with other technologies, it is an efficient and fast method for the structural research of natural products, especially for the discovery of novel structures and active ingredients. There is relatively little research on metabolism, quantitative analysis, and biosynthetic pathways in the body, which is a direction that researchers can focus on in the future.