Bioactive Natural Products Prioritization Using Massive Multi-informational Molecular Networks.

Area of Science:

• Natural product chemistry
• Bioinformatics
• Drug discovery

Background:

• Natural products are vital for drug discovery due to their complex structures and bioactivity.
• Identifying bioactive natural products is challenging due to complex biological matrices and purification efforts.
• Existing bioinformatics tools primarily focus on compound identification, not bioactivity prediction before isolation.

Purpose of the Study:

• To develop an integrated strategy for prioritizing bioactive natural products from crude extracts.
• To utilize massive molecular networks with bioactivity and taxonomic data to identify promising scaffolds.
• To accelerate and rationalize the isolation of high-value natural products for drug discovery.

Main Methods:

• Developed an integrated analysis strategy for bioactive natural product prioritization.
• Employed massive molecular networks integrating bioactivity and taxonomic data.
• Applied the workflow to crude extracts from Bocquillonia nervosa and Neoguillauminia cleopatra, targeting Wnt signaling and chikungunya virus.
• Used metabolite annotation tools for compound identification and isolation.

Main Results:

• Successfully highlighted potentially bioactive scaffolds within complex chemical diversity.
• Isolated a daphnane diterpene orthoester and four 12-deoxyphorbols with Wnt signaling inhibition.
• Identified compounds exhibiting potent antiviral activity against chikungunya virus (CHIKV).
• Demonstrated the efficiency of the prioritization pipeline in identifying and isolating active metabolites.

Conclusions:

• The integrated strategy effectively prioritizes bioactive natural products, accelerating drug discovery.
• This approach rationalizes the isolation process, focusing on high-potential molecules.
• The method significantly speeds up the identification and isolation of novel therapeutic agents from natural sources.