| Affiliation: | 1. Department of Biology, University of Copenhagen, , Copenhagen, 2200 Denmark;2. Department of Astronomy and Theoretical Physics, University of Lund, , Lund, SE‐223 62 Sweden;3. Center for Bioinformatics, University of Hamburg, , Hamburg, 20146 Germany;4. Scuola Internazionale Superiore di Studi Avanzati, , Trieste, 34136 Italy;5. Department of Biomedical Engineering, DTU Elektro, , Kongens Lyngby, 2800 Denmark;6. Niels Bohr Institute, University of Copenhagen, , Copenhagen, 2100 Denmark;7. Computational Biology Unit, Uni Computing, Uni Research, , Norway;8. Department of Chemistry, University of Copenhagen, , Copenhagen, 2100 Denmark;9. BILS, Science for Life Laboratory, , Solna, 171 21 Sweden |
| 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. |
