Correlate Motions Between Residue Pairs (with DCCM)

Overview and Methodology

What It Does

This script computes and visualizes a Dynamic Cross-Correlation Map (DCCM) to highlight correlated motions between residue pairs over a simulation trajectory.

How It Works

• Objective: Identify residues that move in a coordinated or opposing manner during the simulation.
• Process:
  • Collect Coordinates: Tracks atom positions over time.
  • Compute Correlations: Measures how residue displacements are related throughout the trajectory.
  • Visualize: Generates a heatmap of correlation coefficients between residue pairs.

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Configuration and Inputs

Prerequisites

• Requires a loaded trajectory.

Key Configuration Options

• Selections:

  • group_sel1, group_name1: Primary group (e.g., segid TOX).
  • group_sel2, group_name2: Optional second group (e.g., segid VSD and resid 155-165, VSD).
  • If only one group is given, the tool analyzes intra-group correlations.

• Atom Type:

  • 'CA', 'CB', 'backbone', or 'all' atoms per group.

• Correlation Parameters:

  • hide_weak: Whether to hide rows/columns with only weak correlations.
  • correlation_threshold: Absolute value cutoff for hiding weak correlations.

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Outputs

• Heatmap:

  • *_DCCM.png: Correlation matrix showing motion correlations from -1 to 1.

• Terminal Logs:

  • Atom selection details, number of residues processed, and progress updates.

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Interpreting the Results

Correlation Heatmap

• Positive Correlation (0 to 1): Indicates that the residues move in the same direction (e.g., both move closer or farther apart together) over the trajectory. Stronger positive values (closer to 1) suggest highly coordinated motion.
• Negative Correlation (-1 to 0): Indicates that the residues move in opposite directions (e.g., one moves closer while the other moves farther apart). Stronger negative values (closer to -1) suggest highly anti-correlated motion.

Correlations may reflect functionally important relationships, such as cooperative or antagonistic domain movements.

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Example Scenarios

Domain Synchronization in a Protein

• Scenario: A multidomain protein undergoes large-scale movement.
• Observation: Positive correlations between N-terminal and C-terminal domains.
• Interpretation: Suggests these regions move together during global transitions or functional rearrangements.

Antagonistic Loop Motions

• Scenario: Two loops near a binding pocket move in opposition during ligand engagement.
• Observation: Strong negative correlations between loop residues.
• Interpretation: These loops may act as a regulatory gate or clamp, stabilizing upon binding.

Protein–Ligand Coupling

• Scenario: A flexible peptide or ligand is included as a second group.
• Observation: Correlations between ligand and specific residues of the protein.
• Interpretation: Indicates physical or mechanical interaction, potentially validating binding modes or induced fit effects.

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How Dynamic Cross-Correlation Map (DCCM) Differs from Time‐Lagged Cross Correlation (TLCC)

Feature	DCCM	TLCC
Timing	Instantaneous	Time-delayed (uses lag analysis)
Matrix Type	Symmetric (e.g., A↔B = B↔A)	Asymmetric (A→B ≠ B→A if delay exists)
Captures Direction	No	Yes
Use Case	Detect global coordination	Detect signal propagation or time-lagged response
Interpretation	Who moves together	Who causes whom to move, and when

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Usage Tips

• Group Design:

  • One group → Intra-domain dynamics.
  • Two groups → Inter-group or domain interactions.

• Atom Type Choice:

  • Use 'CA' for general protein backbones, 'backbone' for more context, or 'all' for ligand-heavy systems.

• Highlighting Meaningful Signals:

  • Enable hide_weak=True and tune correlation_threshold (e.g., 0.3) to reduce noise.

• Trajectory Sampling:

  • Use a larger step for long simulations to speed up processing without losing large-scale trends.

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