protein-docking-configurator

What This Skill Does

The protein-docking-configurator is a specialized AI agent skill designed to bridge the gap between structural biology data and molecular docking simulations. At its core, the skill automates the most tedious part of the docking workflow: the definition of the search space. By parsing PDB files, it identifies binding pockets based on user-provided active site residues or reference ligands, calculates precise grid box centers, and generates the necessary configuration files for AutoDock Vina and AutoDock4. This eliminates manual coordinate estimation, reducing human error and significantly speeding up the setup phase of high-throughput virtual screening or structural studies.

Installation

To integrate this tool into your OpenClaw environment, ensure you have Python 3.8+ and the numpy library installed on your local system or container environment. You can install the skill by executing the following command in your terminal: clawhub install openclaw/skills/skills/aipoch-ai/protein-docking-configurator Once installed, the DockingConfigurator class becomes available for import in your Python scripts, and the main.py entry point provides full CLI accessibility.

Use Cases

• Virtual Screening Setup: Automatically configure thousands of receptor-ligand docking runs by dynamically determining grid boxes from known active site residues.
• Binding Pocket Refinement: Use a co-crystallized reference ligand to define a search box that perfectly encapsulates the binding site with custom padding.
• Automation Pipelines: Integrate the module into existing structural bioinformatics pipelines where dynamic grid box generation is required for diverse protein targets.
• Manual Optimization: Precisely set grid dimensions for complex proteins where standard pockets are irregularly shaped or require manual intervention.

Example Prompts

1. "I have a protein file named 1hsg.pdb and I want to perform a docking simulation using AutoDock Vina. Please calculate the grid box based on residues A:25, A:27, and A:110, and output the configuration to vina_config.txt."
2. "Configure a docking grid for my receptor using reference ligand inhibitor.pdb. Add a padding of 6 angstroms around the ligand and save the output for AutoDock4."
3. "Set up a 20x20x20 grid box centered at (15.0, 10.0, 5.0) for receptor protein_target.pdb using the Vina software configuration."

Tips & Limitations

• Precision: While the automatic calculation is robust, always visually inspect the grid box using tools like PyMOL or UCSF Chimera to ensure it covers the intended binding cavity.
• Dependencies: This tool relies on local file access for PDB parsing; ensure your file paths are absolute or correctly referenced from the working directory.
• Software Specifics: Be aware that AutoDock4 requires additional ligand prep (pdbqt conversion) which must be handled before running the generated .gpf file.
• Active Site Accuracy: Providing accurate residue indices is critical; ensure your PDB numbering matches the target protein's structural file.