Clustering and Analyzing Ensembles of Residue Interaction Networks from Molecular Dynamics Simulations.

ABSTRACT: Network methods and molecular dynamics (MD) simulations have become essential tools for studying protein dynamics. However, applying network methods to MD simulations of flexible proteins is a major challenge, since the high conformational heterogeneity in such multistate systems can lead to vastly different network topologies across an ensemble. To address this, tools that can disentangle conformational ensembles on a network level are needed. Here, we propose a graph-based clustering framework that provides state-specific insight into the residue interactions of flexible proteins. The framework hinges on using the set of graph-theoretic closeness centralities of all amino acid residues as a structural fingerprint and input for unsupervised machine learning algorithms to perform dimension

SUBMITTER: Franke L 

PROVIDER: S-EPMC12570133 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature