ProSNEx

ProSNEx constructs and analyzes Protein Structure Networks (PSNs) to represent residues as nodes and to characterize residue-level interactions, flexibility, energetics, dynamical correlations, sequence conservation, and variant annotations.

Key Features:

• Network Construction: Protein Structure Networks (PSNs) represent residues as nodes and edges are defined by interaction distance cutoffs for carbon-alpha, carbon-beta, or atom-pair contacts.
• Protein Energy Networks (PEN): Use residue-residue interaction energies formatted by gRINN to weight network edges.
• Dynamical Cross Correlations: Derive dynamical cross correlations from a coarse-grained Normal Mode Analysis (NMA) to inform weighted networks.
• Interaction Strength-Based Networks: Provide interaction strength-based weighting of edges to reflect contact intensities.
• Network Metrics and Pathways: Compute node centralities, determine shortest paths between residues, and identify k-cliques within the network.
• Integration with Biological Data: Associate per-residue conservation scores and mutation or natural variant annotations with network-derived metrics.

Scientific Applications:

• Protein flexibility and stability analysis: Relate network metrics and dynamical correlations to residue flexibility and contributions to stability.
• Sequence conservation studies: Map conservation scores onto PSNs to investigate conserved network positions across species or protein families.
• Mutation impact analysis: Assess how mutations and natural variants affect residue interactions, shortest paths, and network topology.
• Comparative annotation analyses: Compare network-derived residue metrics with traditional biological annotations to identify functionally relevant positions.

Methodology:

Construct PSNs by representing residues as nodes and defining edges via carbon-alpha, carbon-beta, or atom-pair distance cutoffs; generate weighted networks from gRINN-formatted residue-residue energies, coarse-grained NMA-derived dynamical cross correlations, or interaction strength metrics; compute node centralities, shortest paths, and k-cliques; and link per-residue conservation scores and mutation/variant annotations to network metrics.