J. Nat. Prod.: new development of "smart" tools to help uncover natural products

Professor William H gerwick of the University of California, San Diego, published an article on the application of smart tools in natural product analysis on J NAT Prod The author first takes "the face of a molecule" as an example to explain how to understand the "first impression" of a newly discovered compound through analytical instruments in the field of natural product research Pieter dorrestein has helped revolutionize the use of MS 2 fragment data, enabling automated methods to match unknown compounds to a family of compounds As an expert in X-ray crystallography, Jon crady can get insight into the structural details of the compound crystal through its diffraction pattern David Sherman will study the coding of biosynthetic gene clusters of natural products, feel the unique arrangement of PKS and NRPS characteristics in an exciting way, and modify β branch and halogenase, so as to obtain the cognition of molecular structure Robert Jacobs, a pioneering drug discoverer of natural products, may see electrophysiological features in the sciatic nerve of frogs and understand the pharmacological appearance of some molecules Using MS 2 method to help match compounds (source: J NAT Prod.) natural product chemists need effective methods to understand and define the characterization characteristics of newly discovered natural products Every molecule is unique Scientists need to see the "face" of every molecule clearly from the vast "molecular sea" The analytical data can be used to identify the compounds automatically For example, there are so many literatures based on MS 2 molecular network, which can be used for reference in the development of similar NMR database methods The idea of using NMR data is based on the fact that the 1h-13 chsqc method is very powerful in indicating structure, has the characteristics of non overlapping data and relatively easy to quickly accumulate For the latter point, the author team has been trying to accumulate these data in a faster way, such as through non-uniform sampling or using the exact-asp protocol Using MS 2 method and smart method to identify new compounds (source: J NAT Prod.) the author finally uses an artificial intelligence method to analyze, and then provides clustering algorithm for the dataset This method uses a kind of conjoined structure in the deep convolution neural network, which is very suitable for image recognition and lays the foundation for the best method of current face recognition software Therefore, the real molecular "face" is observed through the pattern formed by its HSQC spectrum, and is called small molecule precise recognition technology (SMART) Different analysis methods are integrated into the overview of visual "faceof a molecule" to enhance the discovery of new compounds (source: J NAT Prod.) Finally, the author takes two completely different marine cyanobacteria as an example, and analyzes them through the combination of MS 2 and smart system Firstly, the sample was pretreated by standard method and characterized by LC-MS it was found that the metabolites were related to the characterized Vieques mides Then the metabolites were further purified and analyzed by HSQC The 10 dimensional HSQC map created by smart automatically matches metabolites to related peptides Then the metabolites were separated and their structures were established by NMR and MS Through the combination of MS 2 and HSQC technology to observe and identify these new metabolites, to provide effective and automatic distribution of molecular categories Finally, the author hopes to integrate the molecular network based on MS 2 and the smart analysis method based on HSQC seamlessly to provide a more robust and efficient composite recognition platform Paper link: http://pubs.acs.org/doi/10.1021/acs.jnatprod.7b00624 research group link: http://