Combining an Elastic Network With a Coarse-Grained Molecular Force Field: Structure, Dynamics, and Intermolecular Recognition

Structure-based and physics-based coarse-grained molecular force fields have become attractive approaches to gain mechanistic insight into the function of large biomolecular assemblies. Here, we study how both approaches can be combined into a single representation, that we term ELNEDIN. In this rep...

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Veröffentlicht in:Journal of chemical theory and computation 2009-09, Vol.5 (9), p.2531-2543
Hauptverfasser: Periole, Xavier, Cavalli, Marco, Marrink, Siewert-Jan, Ceruso, Marco A
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Sprache:eng
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Zusammenfassung:Structure-based and physics-based coarse-grained molecular force fields have become attractive approaches to gain mechanistic insight into the function of large biomolecular assemblies. Here, we study how both approaches can be combined into a single representation, that we term ELNEDIN. In this representation an elastic network is used as a structural scaffold to describe and maintain the overall shape of a protein and a physics-based coarse-grained model (MARTINI-2.1) is used to describe both inter- and intramolecular interactions in the system. The results show that when used in molecular dynamics simulations ELNEDIN models can be built so that the resulting structural and dynamical properties of a protein, including its collective motions, are comparable to those obtained using atomistic protein models. We then evaluate the behavior of such models in (1) long, microsecond time-scale, simulations, (2) the modeling of very large macromolecular assemblies, a viral capsid, and (3) the study of a protein−protein association process, the reassembly of the ROP homodimer. The results for this series of tests indicate that ELNEDIN models allow microsecond time-scale molecular dynamics simulations to be carried out readily, that large biological entities such as the viral capsid of the cowpea mosaic virus can be stably modeled as assemblies of independent ELNEDIN models, and that ELNEDIN models show significant promise for modeling protein−protein association processes.
ISSN:1549-9618
1549-9626
DOI:10.1021/ct9002114