Predicting Binding Pockets with DoGSite3

Predicting Binding Pockets with DoGSite3

What is DoGSite3? 

DoGSite3 is part of the ProteinsPlus web server collection developed at the University of Hamburg. It works by using a geometry-based method to scan the surface of a protein for cavities. It then analyzes these pockets, calculates their properties (like volume, surface area, and Depth), and visualizes them in an interactive 3D viewer. It's an excellent tool for beginners and experts alike.

What you need before starting

• A protein (or nucleic acid) structure in PDB format, or the PDB ID for a public structure.

• (Optional) Small molecules/ligands in SDF format if you want to use the ligand-bias option.

• A modern web browser.

• (Optional) PyMOL or UCSF Chimera/ChimeraX to view the downloaded pocket files.

DoGSite3 will consider macromolecular chains with >50 atoms (very small chains are ignored). If your structure has missing loops or side chains, consider modelling them first for a more accurate pocket search.

Step-by-step: using the ProteinsPlus web server

1. Open ProteinsPlus

Go to https://proteins.plus. (Figure 1)

2. Define your target structure.

You have two main options:

• Enter a PDB ID (e.g., 8fv4) to use a structure from the Protein Data Bank (Figure 2).

• Upload a PDB file from your computer (for a model or modified structure).

3. Click on "go" button (Figure 3)

4. Select the DoGSite3 Binding site detection option (Figure 4).

5. Chain selection: choose which chains to include (e.g., only the protein chain(s) of interest) (Figure 5).

6. Submit the job

Click on calculate. The job is queued on the ProteinsPlus server.

7. Results

Click here (Figure 7) to visualize the pocket.

When the job finishes, you will see a results page with:

Pocket table — each row = a predicted (sub)pocket. The table shows key descriptors: Name, volume (ų), surface area (Ų), depth (Å).

Download — a download button provides data files (pocket coordinates and a text file with calculated pocket properties).

You can visualize the binding pocket using PyMOL, Chimera, or any visualization tools.

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