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Molecular Docking and Protein-Ligand Graph

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About This MicroSim

This MicroSim shows two views of a protein-ligand docking interaction: a 2D binding pocket diagram (left) and its corresponding graph representation (right). The left panel shows a drug molecule in a protein binding pocket with interaction lines, while the right panel converts the same information into a graph where atoms/residues are nodes and interactions are typed edges.

Two Panels

  • Binding Pocket View (left) — The ligand sits in the protein's binding site with colored lines showing hydrogen bonds, hydrophobic contacts, and other interactions with specific amino acid residues
  • Graph Representation (right) — The same interactions as a graph: ligand atoms and protein residues become nodes, and non-covalent interactions become labeled edges

Interaction Types

  • Hydrogen bonds — Directional interactions between donor and acceptor groups
  • Hydrophobic contacts — Van der Waals interactions between nonpolar groups
  • Pi-stacking — Interactions between aromatic rings
  • Salt bridges — Electrostatic interactions between charged groups

Why This Matters

Graph representations of protein-ligand interactions are used in: - Machine learning for drug binding affinity prediction - Virtual screening of drug candidates - Understanding structure-activity relationships

How to Use

  1. Compare panels — See how the 2D binding pocket maps to the graph representation
  2. Identify interaction types — Different colors/styles represent different non-covalent interaction types
  3. Hover for details on specific atoms, residues, and interaction types

Iframe Embed Code

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Lesson Plan

Grade Level

College introductory bioinformatics

Duration

15-20 minutes

Prerequisites

  • Understanding of protein structure and binding sites
  • Basic knowledge of drug-target interactions
  • Concept of non-covalent molecular interactions

Activities

  1. Exploration (5 min): Examine the binding pocket view. Identify all interaction types by color. Then find the same interactions in the graph view.
  2. Graph Analysis (5 min): In the graph representation, which protein residue has the most interactions with the ligand? What does this suggest about its importance for drug binding?
  3. Discussion (5 min): If you mutated the most-connected residue to alanine, what would you predict would happen to drug binding affinity? How could this graph representation help in drug design?
  4. Assessment (3 min): Answer the reflection questions below.

Assessment

  1. Why is a graph representation useful for encoding protein-ligand interactions?
  2. What types of non-covalent interactions hold a drug in its binding pocket?
  3. How could machine learning on protein-ligand graphs predict binding affinity for new drug candidates?
  4. What is the advantage of representing docking results as a graph rather than just a docking score?

References

  1. Molecular docking — Wikipedia
  2. Protein-ligand docking — Wikipedia
  3. Non-covalent interaction — Wikipedia
  4. Drug design — Wikipedia