Building Block Extractor: An MS/MS Data Mining Tool for Targeted Discovery of Natural Products with Specified Features.

The utilization of a building-block-based molecular network is an efficient approach to investigate the unknown chemical space of natural products. However, structure-based automated MS/MS data mining remains challenging. This study introduces building block extractor, a user-friendly MS/MS data mining program that automatically extracts user-defined specified features. In addition to the characteristic product ions and neutral losses, this program integrates the abundance of the product ions and sequential neutral loss features as building blocks for the first time. The discovery of nine undescribed sesquiterpenoid dimers from Artemisia heptapotamica highlights the power of this tool. One of these dimers, artemiheptolide I (9), exhibited in vitro inhibition of influenza A/Hongkong/8/68 (H3N2) with an IC50 of 8.01 ± 6.19 µM. Furthermore, two known guaianolide derivatives (16 and 17) possessed remarkable antiviral activity against influenza A/Puerto Rico/8/1934 H1N1, H3N2, and influenza B/Lee/40 with IC50 values ranging from 3.46 to 11.77 µM. In addition to the efficient discovery of novel natural products, this strategy can be generally applied to grab derivatives with specific fragments and enhance the annotation power of LC-MS/MS analysis.

Efficient discovery of potential inhibitors for SARS-CoV-2 3C-like protease from herbal extracts using a native MS-based affinity-selection method.

The 3C-like protease (3CLpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential to the virus life cycle and is supposed to be a potential target for the treatment of coronaviral infection. Traditional Chinese medicines (TCMs) have played an impressive role in the treatment of COVID-19 in China. The effectiveness of TCM formulations prompts scientists to take continuous effort on searching for bioactive small molecules from the ancient resources. Herein, we developed a native mass spectrometry-based affinity-selection method for rapid screening of active small molecules from crude herbal extracts applied for COVID-19 therapy. Six common herbs named Lonicera japonica, Scutellaria baicalensis, Forsythia suspensa, Glycyrrhiza uralensis, Cirsium japonicum, and Andrographis paniculata were investigated. After preliminary separation of the crude extracts, the fractions were incubated with 3CLpro. A native MS-based affinity screening assay was then conducted to search for the protein-ligand complexes. A UHPLC-Q/TOF-MS with UNIFI data acquisition and data processing software was applied to identify the hit compounds. Standard compounds were used to verify the outcomes. Among the 16 hits, three flavonoids, baicalein, scutellarein and ganhuangenin, were identified as potential noncovalent inhibitors against 3CLpro with IC50 values of 0.94, 3.02, and 0.84 µM, respectively. Their binding affinities were further characterized by native MS, with Kd values being 1.43, 3.85, and 1.09 µM, respectively. Overall, we established an efficient native MS-based strategy for discovering 3CLpro ligands from crude mixtures, which supplies a potential strategy of small molecule lead discovery from TCMs.