PHAISTOS: a framework for Markov chain Monte Carlo simulation and inference of protein structure
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PHAISTOS : a framework for Markov chain Monte Carlo simulation and inference of protein structure. / Boomsma, Wouter Krogh; Frellsen, Jes; Harder, Tim Philipp; Bottaro, Sandro; Johansson, Kristoffer Enøe; Tian, Pengfei; Stovgaard, Kasper; Andreetta, Christian; Olsson, Simon; Valentin, Jan; Antonov, Lubomir Dimitrov; Christensen, Anders Steen; Borg, Mikael; Jensen, Jan Halborg; Lindorff-Larsen, Kresten; Ferkinghoff-Borg, Jesper; Hamelryck, Thomas Wim.
In: Journal of Computational Chemistry, Vol. 34, No. 19, 2013, p. 1697-1705.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - PHAISTOS
T2 - a framework for Markov chain Monte Carlo simulation and inference of protein structure
AU - Boomsma, Wouter Krogh
AU - Frellsen, Jes
AU - Harder, Tim Philipp
AU - Bottaro, Sandro
AU - Johansson, Kristoffer Enøe
AU - Tian, Pengfei
AU - Stovgaard, Kasper
AU - Andreetta, Christian
AU - Olsson, Simon
AU - Valentin, Jan
AU - Antonov, Lubomir Dimitrov
AU - Christensen, Anders Steen
AU - Borg, Mikael
AU - Jensen, Jan Halborg
AU - Lindorff-Larsen, Kresten
AU - Ferkinghoff-Borg, Jesper
AU - Hamelryck, Thomas Wim
N1 - Copyright © 2013 Wiley Periodicals, Inc.
PY - 2013
Y1 - 2013
N2 - We present a new software framework for Markov chain Monte Carlo sampling for simulation, prediction, and inference of protein structure. The software package contains implementations of recent advances in Monte Carlo methodology, such as efficient local updates and sampling from probabilistic models of local protein structure. These models form a probabilistic alternative to the widely used fragment and rotamer libraries. Combined with an easily extendible software architecture, this makes PHAISTOS well suited for Bayesian inference of protein structure from sequence and/or experimental data. Currently, two force-fields are available within the framework: PROFASI and OPLS-AA/L, the latter including the generalized Born surface area solvent model. A flexible command-line and configuration-file interface allows users quickly to set up simulations with the desired configuration. PHAISTOS is released under the GNU General Public License v3.0. Source code and documentation are freely available from http://phaistos.sourceforge.net. The software is implemented in C++ and has been tested on Linux and OSX platforms. © 2013 Wiley Periodicals, Inc.
AB - We present a new software framework for Markov chain Monte Carlo sampling for simulation, prediction, and inference of protein structure. The software package contains implementations of recent advances in Monte Carlo methodology, such as efficient local updates and sampling from probabilistic models of local protein structure. These models form a probabilistic alternative to the widely used fragment and rotamer libraries. Combined with an easily extendible software architecture, this makes PHAISTOS well suited for Bayesian inference of protein structure from sequence and/or experimental data. Currently, two force-fields are available within the framework: PROFASI and OPLS-AA/L, the latter including the generalized Born surface area solvent model. A flexible command-line and configuration-file interface allows users quickly to set up simulations with the desired configuration. PHAISTOS is released under the GNU General Public License v3.0. Source code and documentation are freely available from http://phaistos.sourceforge.net. The software is implemented in C++ and has been tested on Linux and OSX platforms. © 2013 Wiley Periodicals, Inc.
KW - Biochemistry
KW - Monte Carlo
KW - Protein Folding
KW - Molecular Simulations
U2 - 10.1002/jcc.23292
DO - 10.1002/jcc.23292
M3 - Journal article
C2 - 23619610
VL - 34
SP - 1697
EP - 1705
JO - Journal of Computational Chemistry
JF - Journal of Computational Chemistry
SN - 0192-8651
IS - 19
ER -
ID: 45419305