4. Related Methodologies / Techniques

molecular dynamics

Definition

Molecular dynamics (MD) is a computational simulation technique that models the time-dependent behavior of molecular systems by calculating the trajectories of atoms and molecules based on Newton's equations of motion. MD simulations track atomic positions and velocities over time, typically at femtosecond intervals, allowing researchers to observe conformational changes, binding events, protein folding, and molecular interactions at atomic resolution. This method is crucial for understanding dynamic biological processes that are difficult to capture experimentally, predicting drug-target interactions, and exploring the energetic landscapes of biomolecular systems. MD provides insights into protein flexibility, allosteric mechanisms, and the thermodynamic stability of molecular complexes.

Visualize molecular dynamics in Nodes Bio

Researchers can visualize MD simulation results as dynamic protein-protein interaction networks, mapping conformational states as nodes and transitions as edges. Nodes Bio enables analysis of allosteric communication pathways, identification of key residues involved in signal propagation, and visualization of how ligand binding affects network topology. Time-series data from MD trajectories can be converted into evolving network representations showing how interaction strengths change across simulation timeframes.

Visualization Ideas:

  • Residue interaction networks showing communication pathways within proteins
  • Time-evolving networks depicting conformational state transitions during MD trajectories
  • Allosteric pathway networks identifying key residues for signal propagation
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Example Use Case

A pharmaceutical team investigating resistance mechanisms to a kinase inhibitor uses MD simulations to study conformational changes in mutant versus wild-type protein structures. The 500-nanosecond simulation reveals that a specific mutation disrupts an allosteric network connecting the ATP-binding site to a regulatory domain. By analyzing residue interaction networks derived from MD trajectories, researchers identify alternative binding pockets that remain stable across both variants, guiding the design of next-generation inhibitors that overcome resistance.

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