AD-ENM

AD-ENM predicts and analyzes protein conformational changes using an elastic network model and normal mode analysis to identify dynamical correlations, hinge residues, and modes relevant to function.

Key Features:

• Elastic Network Modeling: Represents proteins as networks of nodes connected by springs to simulate large-scale macromolecular motions based on spatial proximity.
• Normal Mode Analysis (NMA): Computes low-frequency vibrational modes to identify the dominant collective motions associated with significant conformational transitions.
• Dynamical Correlations: Distinguishes fluctuation-based and density-based dynamical correlations to identify residues that coordinate domain movements, including hinge residues.
• Mode Decomposition: Decomposes dynamical correlations into individual normal modes to pinpoint specific modes responsible for functional transitions such as force generation or nucleotide binding.

Scientific Applications:

• Polymerase Dynamics: Analyzes open/closed transitions in DNA/RNA polymerases and identifies conserved residues involved in domain movements essential for catalysis.
• Motor Protein Analysis: Investigates myosins and kinesins to elucidate force generation mechanisms by identifying hinge residues and dominant normal modes that modulate inter-subdomain conformational changes.
• F1-ATPase Studies: Applied to F1-ATPases to examine conformational changes related to nucleotide binding and catalytic transitions.
• Structure-Function Relationships: Compares mechanistic differences between protein families, for example between kinesins and myosins, to relate dynamic modes to functional outcomes.

Methodology:

Proteins are modeled as elastic networks with residues as nodes connected by springs based on spatial proximity; low-frequency normal modes are calculated; dynamical correlations are analyzed to identify functionally relevant residues and modes; comparative analyses across proteins or states are performed.