drug-binding-site-definition

Compute docking box center and dimensions from ligand, residues, or JSON.

144|21|Updated Jan 8, 2026
One-click install
npx skills add https://github.com/learningmatter-mit/AtomisticSkills --skill drug-binding-site-definition
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: drug-binding-site-definition
Source: https://github.com/learningmatter-mit/AtomisticSkills/tree/main/.agents/skills/drug-binding-site-definition
Command: npx skills add https://github.com/learningmatter-mit/AtomisticSkills --skill drug-binding-site-definition

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires MDAnalysis, numpy, rdkit, pymol, and includes scripts (resource) components.

What problem does it solve?

Define the exact docking/search box for a protein binding site so downstream docking and docking-box-dependent workflows operate in the correct coordinate frame.

Core Features & Use Cases

  • Docking box generation in Angstroms: Computes standardized box center coordinates and box dimensions (size_x/size_y/size_z) that other drug-discovery skills can directly consume.
  • Multiple input modes: Supports defining the box from a co-crystal ligand (recommended), from known binding-site residues, or by reloading a previously generated JSON specification.
  • Practical validation hooks: Encourages sanity-checking pocket coverage and includes an optional visualization step to confirm the box overlays the intended pocket.

Quick Start

Use the skill to compute a docking box from your co-crystal ligand file by providing the ligand coordinates and choosing padding and minimum box size for Angstrom units.

Frequently Asked Questions about drug-binding-site-definition

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I define a docking box for virtual screening from protein binding site residues?▼

You can define a docking box from a co-crystal ligand by providing the ligand coordinates, allowing the Skill to extract atom positions and compute padded bounding-box geometry that outputs a validated JSON specification with box center and dimensions in Angstroms.

How do I calculate the center coordinates and box dimensions for molecular simulation docking?▼

The Skill calculates center coordinates and box dimensions for molecular simulation docking by parsing protein or ligand coordinate files, filtering alternate locations, computing padded bounding-box geometry, and validating the resulting JSON schema in Angstrom units for downstream virtual screening workflows.

Can I use RDKit and MDAnalysis to generate a JSON docking box specification for downstream workflows?▼

Yes, the Skill leverages dependencies including RDKit, MDAnalysis, numpy, and pymol to parse coordinate files and generate a validated JSON docking box specification containing center coordinates and size_x/size_y/size_z dimensions that downstream drug-discovery workflows can directly consume.

What is the best way to transfer a binding pocket from a homology model using ligand coordinates?▼

The best way to transfer a binding pocket using ligand coordinates is to input the co-crystal ligand file so the Skill can compute the bounding-box geometry, apply padding and minimum box size constraints, and output a JSON specification anchoring the homology-based docking region in Angstrom units.

Does the generated docking box JSON specification support visualization to confirm pocket coverage?▼

Yes, the generated docking box JSON specification supports an optional visualization step using pymol to sanity-check pocket coverage, ensuring the computed center coordinates and box dimensions correctly overlay the intended protein binding site before running downstream docking workflows.