TarFisDock: a web server for identifying drug targets with docking approach.

Area of Science:

• Computational chemistry
• Drug discovery
• Bioinformatics

Background:

• Automating the identification of small molecule-protein interactions is crucial for drug discovery.
• Existing methods for ligand-protein docking are often computationally intensive and time-consuming.
• There is a need for efficient tools to screen large protein databases for potential drug targets.

Purpose of the Study:

• To introduce TarFisDock, a web-based tool for automating small molecule-protein interaction searches.
• To present the Potential Drug Target Database (PDTD) with 698 protein structures across 15 therapeutic areas.
• To validate the utility of reverse ligand-protein docking for target identification.

Main Methods:

• TarFisDock utilizes a reverse ligand-protein docking approach to screen the PDTD.
• Input small molecules in mol2 format are docked against protein structures in the database.
• The DOCK program's ligand-protein interaction energy terms are used for ranking potential targets.

Main Results:

• TarFisDock successfully identified known targets for vitamin E and 4H-tamoxifen.
• Top predictions for vitamin E covered 30-50% of reported targets within the top 2-10% of candidates.
• Top predictions for 4H-tamoxifen covered 30-50% of confirmed targets within the top 2-5% of candidates.

Conclusions:

• TarFisDock is a reliable and efficient tool for drug target identification.
• The server facilitates mechanism studies for existing drugs and natural product probes.
• TarFisDock and PDTD are publicly accessible for research use.