Botanical drugs: a new strategy for structure-based target prediction

Area of Science:

• Computational chemistry and drug discovery
• Structural biology and bioinformatics
• Pharmacology and natural product research

Background:

• Accurate small molecule target identification is crucial for drug discovery, particularly for botanical medicines.
• Understanding ligand-protein interactions is key to drug repurposing and identifying off-targets.
• Current methods face challenges in precisely defining these interactions.

Purpose of the Study:

• To develop and validate a novel computational strategy for identifying small molecule targets.
• To analyze ligand-protein interactions from a large dataset of crystal structures.
• To predict candidate targets for compounds found in traditional botanical drugs.

Main Methods:

• Collected 9063 ligand-protein crystal structures from the Protein Data Bank (PDB).
• Defined 5133 ligand-protein interaction pairs and clustered them using Bayesian Gaussian Mixture Model (BGMM).
• Validated interaction patterns by comparing docked poses to native structures and combined with molecular docking.

Main Results:

• Identified significant interaction patterns based on Gaussian distributions with sufficient ligand atoms.
• Predicted 51 candidate targets for Semen Strychni compounds (brucine, strychnine, icajine).
• Predicted 8 candidate targets for Astragalus compounds (astragaloside-IV, formononetin, calycosin-7-glucoside).

Conclusions:

• The developed strategy enhances the accuracy of small molecule target identification.
• Predicted targets align with the known therapeutic effects of Semen Strychni and Astragalus in cancer and chronic pain.
• This approach facilitates the discovery and development of botanical drugs.