Coarse-graining protein structures into their dynamic communities with DCI, a dynamic community identifier

Area of Science:

• Biophysics
• Computational Biology
• Structural Biology

Background:

• Proteins function through coordinated movements of residue groups, termed communities.
• Understanding these dynamic communities is crucial for deciphering protein function, allostery, and the impact of mutations or ligand binding.

Purpose of the Study:

• To introduce a novel computational tool, the dynamic community identifier (DCI).
• To identify and visualize protein residue communities based on dynamic cross-correlations.

Main Methods:

• Utilizes Gaussian elastic network models (ENMs) to calculate protein residue dynamic cross-correlations.
• Identifies clusters of residues exhibiting correlated motions as dynamic communities.

Main Results:

• Demonstrates the application of DCI across diverse protein types, including G protein-coupled receptors (GPCRs).
• Visualizes protein "moving parts" by highlighting identified residue communities.
• Shows how DCI can clarify functional dynamics, enzyme activity, membrane transport, and effects of mutations or ligand binding.

Conclusions:

• DCI provides a simplified and clarified view of a protein's essential functional components.
• The method aids in understanding protein dynamics, allostery, and the functional consequences of structural changes.
• The DCI server is publicly accessible for broader research applications.