Subpocket analysis method for fragment-based drug discovery

Area of Science:

• Computational chemistry
• Structural biology
• Drug discovery

Background:

• Binding sites can share similar subpockets despite overall dissimilarity.
• Information on shared subpockets is valuable for fragment-based drug design (FBDD).
• Existing FBDD methods may not efficiently identify or align these crucial subpockets.

Purpose of the Study:

• To introduce SubCav, a novel computational method for subpocket similarity searching and alignment.
• To enable the identification of shared subpockets across diverse protein structures.
• To facilitate the suggestion of new fragments and optimization of existing compounds in drug design.

Main Methods:

• Development of SubCav, a method utilizing pharmacophoric fingerprints.
• Implementation of a subpocket alignment algorithm within SubCav.
• Application of SubCav to a Protein Data Bank (PDB)-wide database for similarity searching.

Main Results:

• SubCav effectively aligns subpockets even with low sequence similarity.
• The method successfully retrieves relevant subpockets from large, diverse structural databases.
• SubCav demonstrates utility in analyzing subpockets within protein families.

Conclusions:

• SubCav is an effective tool for identifying shared subpockets, crucial for fragment-based drug design.
• The method aids in suggesting novel fragments and rationalizing compound selectivity.
• SubCav enhances the capabilities of drug discovery by leveraging structural information of binding sites.