# Utilizing High-Resolution Mass Spectrometry Data Mining Strategy in R Programming Language for Rapid Annotation of Absorbed Prototypes and Metabolites of Gypenosides

## TL;DR

This study introduces a new method using R programming and mass spectrometry to quickly identify natural product compounds and their metabolites in mice serum.

## Contribution

A novel R-based strategy for rapid annotation of prototypes and metabolites of gypenosides using mass spectrometry data.

## Key findings

- 36 prototypes and 108 metabolites of gypenosides were annotated in mice serum.
- The prototype and eight metabolites of gypenoside LXXV were confirmed using the proposed strategy.

## Abstract

The rapid and accurate annotation of the complex compounds and metabolites in natural products remains a significant challenge. In this study, we developed an integrated strategy to efficiently and accurately profile both the prototypes and metabolites of natural products in vivo. And this was achieved by establishing a gypenosides constituent database and utilizing R programming language to combine sample selection, virtual metabolite database construction, polygon mass defect filtering, and Kendrick mass defect filtering. In addition, the annotation strategy was successfully applied to identify the prototypes and metabolites of gypenosides in mice serum. As a case study, gypenoside LXXV was used to validate the feasibility of this approach. The results demonstrated 36 prototypes and 108 metabolites were annotated from the serum by the established annotation strategy. The prototype and eight metabolites of gypenoside LXXV were further confirmed, indicating that the proposed strategy is available. This study provides a novel approach for the rapid and accurate identification of prototypes and metabolites of natural products and offers new insights into the metabolic processes of gypenosides in vivo.