Computational studies of transport in ion channels using metadynamics

Simone Furini, Carmen Domene Nunez

Research output: Contribution to journalArticlepeer-review

31 Citations (SciVal)


Molecular dynamics simulations have played a fundamental role in numerous fields of science by providing insights into the structure and dynamics of complex systems at the atomistic level. However, exhaustive sampling by standard molecular dynamics is in most cases computationally prohibitive, and the time scales accessible remain significantly shorter than many biological processes of interest. In particular, in the study of ion channels, realistic models to describe permeation and gating require accounting for large numbers of particles and accurate interaction potentials, which severely limits the length of the simulations. To overcome such limitations, several advanced methods have been proposed among which is metadynamics. In this algorithm, an external bias potential to accelerate sampling along selected collective variables is introduced. This bias potential discourages visiting regions of the configurational space already explored. In addition, the bias potential provides an estimate of the free energy as a function of the collective variables chosen once the simulation has converged. In this review, recent contributions of metadynamics to the field of ion channels are discussed, including how metadynamics has been used to search for transition states, predict permeation pathways, treat conformational flexibility that underlies the coupling between gating and permeation, or compute free energy of permeation profiles. This article is part of a Special Issue entitled: Membrane Proteins edited by J.C. Gumbart and Sergei Noskov.
Original languageEnglish
Pages (from-to)1733-1740
Number of pages8
JournalBiochimica Et Biophysica Acta-Biomembranes
Issue number7 Pt B
Early online date15 Feb 2016
Publication statusPublished - 1 Jul 2016


  • membrane proteins, free energy, K+ channels, Na+ channels, enhanced sampling, molecular dynamics, simulations


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