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Functional Approaches to Discover New Compounds via Enzymatic Modification: Predicted Data Mining Approach and Biotransformation-Guided Purification

Summary

Scientists are developing faster ways to discover new medicines from plants using two innovative methods. The first approach uses computer programs to predict which plant compounds can be chemically modified by enzymes to create new medicines with better properties. The second approach combines enzyme chemistry with traditional purification to directly isolate these modified compounds from plant extracts. These methods have successfully created new compounds with improved effectiveness against diseases like diabetes and cancer, often with much better solubility for medical use.

Background

Natural compounds from medicinal plants are valuable for drug discovery, but their isolation and characterization are traditionally time-consuming and laborious. Two innovative approaches have emerged to more efficiently discover bioactive substances: the predicted data mining approach (PDMA) and biotransformation-guided purification (BGP), which combine computational methods with enzymatic modification.

Objective

This review examines recent research employing PDMA or BGP for novel compound discovery from natural sources. The objective is to demonstrate how these approaches effectively discover novel bioactive molecules, enhance bioactivity and solubility of existing compounds, and develop alternatives to traditional methods.

Results

Both PDMA and BGP successfully identified novel compounds with enhanced properties. PDMA discovered compounds like 3′-hydroxyloureirin A/B, 3″-hydroxyisoxsuprine, and skullcapflavone II-6′-O-β-glucoside with improved antioxidant, anti-inflammatory, and anti-melanoma activities. BGP produced novel glycosides from Ganoderma, Baizhi, licorice, and Ha-Soo-Oh with significantly improved solubility and bioactivity.

Conclusion

Both PDMA and BGP represent powerful strategies for modifying and discovering bioactive natural products. Integration of advanced computational simulations and comprehensive databases will enhance PDMA’s predictive efficiency, while BGP’s cost-effectiveness and versatility make it promising for accelerating drug discovery from traditional medicines and natural resources.
  • Published in:Molecules,
  • Study Type:Review,
  • Source: PMID: 40430400, DOI: 10.3390/molecules30102228
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