Potential natural product discovery from microbes through a diversity-guided computational framework.

Area of Science:

• Microbiology
• Cheminformatics
• Bioinformatics
• Drug Discovery

Background:

• Natural compound occurrence is linked to microbial distribution and evolution, impacting biological and ecological relevance.
• Effective drug discovery requires integrating chemical, geographical, and phylogenetic data to source microbes.
• Current methods lack a unified framework to identify microbial producers of diverse bioactive compounds and their locations.

Purpose of the Study:

• To present GIST (Geospatial Integrated Species, sites and bioactive compound relationships Tracking tool), a computational framework for analyzing microbial biodiversity.
• To enable researchers to identify which microorganisms produce structurally diverse bioactive compounds and where they are located.
• To improve drug discovery success rates by providing tools for evaluating microbial biodiversity patterns.

Main Methods:

• Development of the GIST computational framework.
• Exploitation of bioactive diversity (BD) and phylogenetic diversity (PD) measures.
• Derivation of BD from bioactive dendrograms and PD from phylogenetic trees.

Main Results:

• GIST describes and compares variations in chemical and genetic diversity among microbes across different geographical areas.
• The framework quanties bioactive and phylogenetic diversity to reveal patterns in microbial communities.
• Demonstrates the utility of BD and PD for assessing microbial resources for drug discovery.

Conclusions:

• GIST provides a novel analytical framework for integrating diverse data types relevant to drug discovery.
• The framework aids in identifying and locating microorganisms with high potential for producing valuable natural products.
• GIST enhances the strategic sourcing of microbes, thereby increasing the efficiency and success rate of drug discovery efforts.