title: New generation of elastic network models.
abstract: The intrinsic flexibility of proteins and nucleic acids can be grasped from remarkably simple mechanical models of particles connected by springs. In recent decades, Elastic Network Models (ENMs) combined with Normal Model Analysis widely confirmed their ability to predict biologically relevant motions of biomolecules and soon became a popular methodology to reveal large-scale dynamics in multiple structural biology scenarios. The simplicity, robustness, low computational cost, and relatively high accuracy are the reasons behind the success of ENMs. This review focuses on recent advances in the development and application of ENMs, paying particular attention to combinations with experimental data. Successful application scenarios include large macromolecular machines, structural refinement, docking, and evolutionary conservation.
authors.affiliation: Department of Biological Chemical Physics, Rocasolano Physical Chemistry Institute C.S.I.C., Serrano 119, 28006 Madrid, Spain.
authors.forename: José Ramón
authors.initials: JR
authors.lastname: López-Blanco
authors.affiliation: Department of Biological Chemical Physics, Rocasolano Physical Chemistry Institute C.S.I.C., Serrano 119, 28006 Madrid, Spain. Electronic address: pablo@chaconlab.org.
authors.forename: Pablo
authors.initials: P
authors.lastname: Chacón
mesh_terms: D004548:Elasticity; D008962:Models, Theoretical; D062105:Molecular Docking Simulation; D009696:Nucleic Acids / Q000737:chemistry; D011506:Proteins / Q000737:chemistry
chemical_list: D009696:Nucleic Acids; D011506:Proteins