PHAISTOS: A framework for Markov chain Monte Carlo simulation and inference of protein structure

Wouter Boomsma, Jes Frellsen, Tim Harder, Sandro Bottaro, Kristoffer E. Johansson, Pengfei Tian, Kasper Stovgaard, Christian Andreetta, Simon Olsson, Jan B. Valentin, Lubomir D. Antonov, Anders Christensen, Mikael Borg, Jan H. Jensen, Kresten Lindorff‐Larsen, Jesper Ferkinghoff-Borg, Thomas Hamelryck

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

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.
Original languageEnglish
JournalJournal of Computational Chemistry
Volume34
Issue number19
Pages (from-to)1697-1705
ISSN0192-8651
DOIs
Publication statusPublished - 2013

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