title: Bias-Exchange Metadynamics Simulations: An Efficient Strategy for the Analysis of Conduction and Selectivity in Ion Channels.
abstract: Conduction through ion channels possesses two interesting features: (i) different ionic species are selected with high-selectivity and (ii) ions travel across the channel with rates approaching free-diffusion. Molecular dynamics simulations have the potential to reveal how these processes take place at the atomic level. However, analysis of conduction and selectivity at atomistic detail is still hampered by the short time scales accessible by computer simulations. Several algorithms have been developed to "accelerate" sampling along the slow degrees of freedom of the process under study and thus to probe longer time scales. In these algorithms, the slow degrees of freedom need to be defined in advance, which is a well-known shortcoming. In the particular case of ion conduction, preliminary assumptions about the number and type of ions participating in the permeation process need to be made. In this study, a novel approach for the analysis of conduction and selectivity based on bias-exchange metadynamics simulations was tested. This approach was compared with umbrella sampling simulations, using a model of a Na(+)-selective channel. Analogous conclusions resulted from both techniques, but the computational cost of bias-exchange simulations was lower. In addition, with bias-exchange metadynamics it was possible to calculate free energy profiles in the presence of a variable number and type of permeating ions. This approach might facilitate the definition of the set of collective variables required to analyze conduction and selectivity in ion channels.
authors.affiliation: Chemistry Research Laboratory, University of Oxford , 12 Mansfield Road, Oxford OX1 3TA, U.K.|Department of Chemistry, King's College London , Britannia House, 7 Trinity Street, London SE1 1DB, U.K.
authors.forename: Carmen
authors.initials: C
authors.lastname: Domene
authors.affiliation: Department of Medical Biotechnologies, University of Siena , viale Mario Bracci 16, I-53100, Siena, Siena, Italy.
authors.forename: Paolo
authors.initials: P
authors.lastname: Barbini
authors.affiliation: Department of Medical Biotechnologies, University of Siena , viale Mario Bracci 16, I-53100, Siena, Siena, Italy.
authors.forename: Simone
authors.initials: S
authors.lastname: Furini
mesh_terms: D000465:Algorithms; D020640:Arcobacter / Q000378:metabolism; D001426:Bacterial Proteins / Q000737:chemistry / Q000378:metabolism; D030562:Databases, Protein; D007477:Ions / Q000737:chemistry; D056004:Molecular Dynamics Simulation; D011188:Potassium / Q000737:chemistry; D012964:Sodium / Q000737:chemistry; D015222:Sodium Channels / Q000737:chemistry* / Q000378:metabolism; D013816:Thermodynamics
chemical_list: D001426:Bacterial Proteins; D007477:Ions; D015222:Sodium Channels; D012964:Sodium; D011188:Potassium