Efficient Sampling of High-Dimensional Free-Energy Landscapes with Parallel Bias Metadynamics

Metadynamics accelerates sampling of molecular dynamics while reconstructing thermodynamic properties of selected descriptors of the system. Its main practical difficulty originates from the compromise between keeping the number of descriptors small for efficiently exploring their multidimensional f...

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Vydáno v:Journal of chemical theory and computation Ročník 11; číslo 11; s. 5062
Hlavní autoři: Pfaendtner, Jim, Bonomi, Massimiliano
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 10.11.2015
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ISSN:1549-9626, 1549-9626
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Abstract Metadynamics accelerates sampling of molecular dynamics while reconstructing thermodynamic properties of selected descriptors of the system. Its main practical difficulty originates from the compromise between keeping the number of descriptors small for efficiently exploring their multidimensional free-energy landscape and biasing all of the slow motions of a process. Here we illustrate on a model system and on the tryptophan-cage miniprotein parallel bias metadynamics, a method that overcomes this issue by simultaneously applying multiple low-dimensional bias potentials.
AbstractList Metadynamics accelerates sampling of molecular dynamics while reconstructing thermodynamic properties of selected descriptors of the system. Its main practical difficulty originates from the compromise between keeping the number of descriptors small for efficiently exploring their multidimensional free-energy landscape and biasing all of the slow motions of a process. Here we illustrate on a model system and on the tryptophan-cage miniprotein parallel bias metadynamics, a method that overcomes this issue by simultaneously applying multiple low-dimensional bias potentials.
Metadynamics accelerates sampling of molecular dynamics while reconstructing thermodynamic properties of selected descriptors of the system. Its main practical difficulty originates from the compromise between keeping the number of descriptors small for efficiently exploring their multidimensional free-energy landscape and biasing all of the slow motions of a process. Here we illustrate on a model system and on the tryptophan-cage miniprotein parallel bias metadynamics, a method that overcomes this issue by simultaneously applying multiple low-dimensional bias potentials.Metadynamics accelerates sampling of molecular dynamics while reconstructing thermodynamic properties of selected descriptors of the system. Its main practical difficulty originates from the compromise between keeping the number of descriptors small for efficiently exploring their multidimensional free-energy landscape and biasing all of the slow motions of a process. Here we illustrate on a model system and on the tryptophan-cage miniprotein parallel bias metadynamics, a method that overcomes this issue by simultaneously applying multiple low-dimensional bias potentials.
Author Bonomi, Massimiliano
Pfaendtner, Jim
Author_xml – sequence: 1
  givenname: Jim
  surname: Pfaendtner
  fullname: Pfaendtner, Jim
  organization: Department of Chemical Engineering, University of Washington , Seattle, Washington 98195, United States
– sequence: 2
  givenname: Massimiliano
  surname: Bonomi
  fullname: Bonomi, Massimiliano
  organization: Department of Chemistry, University of Cambridge , Lensfield Road, Cambridge CB2 1EW, United Kingdom
BackLink https://www.ncbi.nlm.nih.gov/pubmed/26574304$$D View this record in MEDLINE/PubMed
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Snippet Metadynamics accelerates sampling of molecular dynamics while reconstructing thermodynamic properties of selected descriptors of the system. Its main practical...
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SubjectTerms Algorithms
Models, Biological
Molecular Dynamics Simulation
Thermodynamics
Tryptophan - chemistry
Title Efficient Sampling of High-Dimensional Free-Energy Landscapes with Parallel Bias Metadynamics
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