TMFF-A Two-Bead Multipole Force Field for Coarse-Grained Molecular Dynamics Simulation of Protein

Min Li, Fengjiao Liu, John Zhang

Research output: Contribution to journalArticle

Abstract

Coarse-grained (CG) models are desirable for studying large and complex biological systems. In this paper, we propose a new two-bead multipole force field (TMFF) in which electric multipoles up to the quadrupole are included in the CG force field. The inclusion of electric multipoles in the proposed CG force field enables a more realistic description of the anisotropic electrostatic interactions in the protein system and, thus, provides an improvement over the standard isotropic two-bead CG models. In order to test the accuracy of the new CG force field model, extensive molecular dynamics simulations were carried out for a series of benchmark protein systems. These simulation studies showed that the TMFF model can realistically reproduce the structural and dynamical properties of proteins, as demonstrated by the close agreement of the CG results with those from the corresponding all-atom simulations in terms of root-mean-square deviations (RMSDs) and root-mean-square fluctuations (RMSFs) of the protein backbones. The current two-bead model is highly coarse-grained and is 50-fold more efficient than all-atom method in MD simulation of proteins in explicit water.

Original languageEnglish (US)
Pages (from-to)6147-6156
Number of pages10
JournalJournal of Chemical Theory and Computation
Volume12
Issue number12
DOIs
StatePublished - Dec 13 2016

Fingerprint

beads
multipoles
field theory (physics)
Molecular dynamics
molecular dynamics
proteins
Proteins
Computer simulation
simulation
Atoms
Biological systems
Coulomb interactions
atoms
quadrupoles
inclusions
electrostatics
deviation
Water
water
interactions

ASJC Scopus subject areas

  • Computer Science Applications
  • Physical and Theoretical Chemistry

Cite this

TMFF-A Two-Bead Multipole Force Field for Coarse-Grained Molecular Dynamics Simulation of Protein. / Li, Min; Liu, Fengjiao; Zhang, John.

In: Journal of Chemical Theory and Computation, Vol. 12, No. 12, 13.12.2016, p. 6147-6156.

Research output: Contribution to journalArticle

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