Figure 1: Gaussian network model (GNM) representation of the nucleosome core particle (PDB id: 1KX4). The beads/nodes represent the residues (amino acids, gray; and nucleotides at their P (orange), C4‘- and C2-atoms (white). The nodes are connected by elastic springs (light-gray for protein intramolecular, yellow for DNA/RNA intramolecular, and cyan (protein-DNA intermolecular).
The Gaussian network model (GNM) is a representation of a biological macromolecule as an elastic mass-and-spring network to study, understand, and characterize the mechanical aspects of its long-time large-scale dynamics. The model has a wide range of applications from small proteins such as enzymes composed of a single domain, to large macromolecular assemblies such as a ribosome or a viral capsid. Protein domain dynamics plays key roles in a multitude of molecular recognition and cell signalling processes.
Protein domains, connected by intrinsically disordered flexible linker domains, induce long-range allostery via protein domain dynamics.
The resultant dynamic modes cannot be generally predicted from static structures of either the entire protein or individual domains.
The Gaussian network model is a minimalist, coarse-grained approach to study biological molecules. In the model, proteins are represented by nodes corresponding to α-carbons of the amino acid residues. Similarly, DNA and RNA structures are represented with one to three nodes for each nucleotide. The model uses the harmonic approximation to model interactions. This coarse-grained representation makes the calculations computationally inexpensive.
At the molecular level, many biological phenomena, such as catalytic activity of an enzyme, occur within the range of nano- to millisecond timescales. All atom simulation techniques, such as molecular dynamics simulations, rarely reach microsecond trajectory length, depending on the size of the system and accessible computational resources. Normal mode analysis in the context of GNM, or elastic network (EN) models in general, provides insights on the longer-scale functional dynamic behaviors of macromolecules. Here, the model captures native state functional motions of a biomolecule at the cost of atomic detail. The inference obtained from this model is complementary to atomic detail simulation techniques.
Another model for protein dynamics based on elastic mass-and-spring networks is the Anisotropic Network Model.
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