SenseNet, a tool for analysis of protein structure networks obtained from molecular dynamics simulations.

ABSTRACT: Computational methods play a key role for investigating allosteric mechanisms in proteins, with the potential of generating valuable insights for innovative drug design. Here we present the SenseNet ("Structure ENSEmble NETworks") framework for analysis of protein structure networks, which differs from established network models by focusing on interaction timelines obtained by molecular dynamics simulations. This approach is evaluated by predicting allosteric residues reported by NMR experiments in the PDZ2 domain of hPTP1e, a reference system for which previous computational predictions have shown considerable variance. We applied two models based on the mutual information between interaction timelines to estimate the conformational influence of each residue on its local environment. In t

SUBMITTER: Schneider M 

PROVIDER: S-EPMC8929561 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature