oGNM

oGNM computes equilibrium dynamics of biomolecular structures using the Gaussian Network Model (GNM) to predict normal modes and residue fluctuation patterns for proteins, oligonucleotides, and their complexes.

Key Features:

• High-Throughput Analysis: Computes normal modes and dynamics significantly faster than traditional methods (5–6 orders of magnitude), enabling large-scale studies.
• Elastic Network Representation: Represents biomolecules as networks of nodes (residues or atoms) with edges based on spatial proximity within a specified cutoff distance.
• Versatility Across Biomolecules: Applies to proteins, oligonucleotides, and their complexes to analyze cooperative fluctuations under native-state conditions.
• Nucleic Acid Modeling: Uses three nodes per nucleotide and uniform interaction ranges across complex components to represent nucleic acid interactions accurately.

Scientific Applications:

• Protein Dynamics: Analyzes large sets of Protein Data Bank (PDB) structures (over 20,000) to identify residues involved in cooperative motions and link dynamics to catalytic activity.
• Correlation with Experimental Data: Predicts thermal fluctuations that align with X-ray crystallographic data across various inter-residue interaction distances.
• Nucleic Acid Complexes: Models dynamics of DNA/RNA-containing complexes and provides rapid assessments that correlate with experimental observations.

Methodology:

Implements an elastic network GNM with interactions defined between closely located alpha-carbon pairs (≤ 7.0 Å), a single-parameter harmonic potential, a Kirchhoff adjacency matrix representing spatial proximity, and uses the matrix inverse to obtain auto-correlations, cross-correlations, and predictions of alpha-carbon temperature factors.