Assisting 13C NMR and MS/MS joint data annotation through on-demand databases

Compound identification in complex mixtures by NMR and MS is best achieved through experimental databases (DB) mining. Experimental DB frequently show limitations regarding their completeness, availability or data quality, thus making predicted database (e.g., ISDB, PNMRNP) of increasing common use [1]. Querying large databases may lead to select unlikely structure candidates. Two approaches to dereplication are thus possible: taxonomical filtering (either biological or chemical) of the DB before search or taxonomical scoring of the results after a large-scale search [2]. The present work relies on the former approach. The corresponding dereplication tool involves the selection of the structure set of interest from the largest available structural DB and the prediction of the associated NMR and MS spectral data ([Fig. 1]).

Fig. 1 On-demand natural product databases.

As far as we know, NMRshiftDB2 is the only open-source 13C NMR chemical shift predictor that can be freely operated in batch mode [3]. CFM-ID 4.0 is one of the best-performing open-source tools for ESI-MS/MS spectra prediction [4]. LOTUS is a freely usable and comprehensive collection of secondary metabolites [5]. It can select compounds according to substructure, chemical class, or taxonomical source. Integrating the open-source database and software LOTUS, CFM-ID, and NMRShiftDB2 in a dereplication workflow requires presently programming skills, owing to the diversity of data encoding and processing procedures. A graphical user interface that integrates seamlessly database building and spectral data prediction still does not exist, to the best of our knowledge.

The present work proposes a coherent software tool that assists secondary metabolites specialists to identify mixture components in a simple way.

 

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• References

• 1 Lianza M. et al. The Three Pillars of Natural Product Dereplication. Alkaloids from the Bulbs of Urceolina peruviana (C. Presl) J.F. Macbr. as a Preliminary Test Case. Molecules 2021; 26: 637
• 2 Rutz A. et al. Taxonomically Informed Scoring Enhances Confidence in Natural Products Annotation. Frontiers in Plant Science 2019; 10
• 3 Steinbeck C, Kuhn S. NMRShiftDB – Compound identification and structure elucidation support through a free community-built web database. Phytochemistry 2004; 19: 2711-2717
• 4 Wang F. et al. CFM-ID 4.0: More Accurate ESI-MS/MS Spectral Prediction and Compound Identification. Analytical Chemistry 2021; 34: 11692-11700
• 5 Rutz A. et al. The LOTUS Initiative for Open Natural Products Research: Knowledge Management through Wikidata bioRxiv 2021. 02.28.433265.

Publication History

Article published online:
12 December 2022