An algorithm-driven intelligent mining and identification strategy for natural product mass spectrometry.

Area of Science:

• Natural Product Chemistry
• Analytical Chemistry
• Computational Chemistry

Background:

• Identifying new compounds from complex plant extracts using MS/MS data is challenging due to structural diversity.
• Existing data post-processing techniques for MS/MS data lack specificity and precision, making structure annotation time-consuming.

Purpose of the Study:

• To introduce MS-SMART, an innovative strategy for efficient mining and identification of new natural products.
• To demonstrate the feasibility of MS-SMART for rapid discovery of novel compounds, using berberine-type alkaloids as a case study.

Main Methods:

• MS-SMART integrates three intelligent algorithms: automatic diagnostic ion mining, rapid alkaloid filtration from untargeted MS/MS data, and structural recommendations.
• Diagnostic ions were extracted and validated against reference data.
• Berberine-type compounds were filtered, and their structures were recommended using building blocks from known berberines.

Main Results:

• MS-SMART efficiently identified a large number of berberine-type alkaloids across diverse plant species.
• A significant proportion of the identified compounds were novel, with many confirmed by reference standards.
• Specifically, 103, 198, 60, 80, and 51 berberines were identified in Stephaniae Epigaeae Radix, Coptidis Rhizoma, Phellodendri Chinensis Cortex, Phellodendri Amurensis Cortex, and Corydalis Decumbentis Rhizoma, respectively.

Conclusions:

• MS-SMART offers a novel research paradigm for the rapid discovery and identification of new compounds in complex natural product samples.
• The strategy significantly enhances the efficiency and accuracy of natural product identification from MS/MS data.