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 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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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. |
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| 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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| Title | Efficient Sampling of High-Dimensional Free-Energy Landscapes with Parallel Bias Metadynamics |
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