Department of Biology, University of Copenhagen, Copenhagen, 2200, Denmark.
J Comput Chem. 2013 Jul 15;34(19):1697-705. doi: 10.1002/jcc.23292. Epub 2013 Apr 26.
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.
我们提出了一个新的软件框架，用于马尔可夫链蒙特卡罗采样，用于蛋白质结构的模拟、预测和推理。该软件包包含了蒙特卡罗方法的最新进展的实现，例如高效的局部更新和从局部蛋白质结构的概率模型中进行采样。这些模型形成了广泛使用的片段和旋转体库的概率替代方案。与易于扩展的软件架构相结合，这使得 PHAISTOS 非常适合从序列和/或实验数据进行蛋白质结构的贝叶斯推断。目前，该框架中提供了两种力场：PROFASI 和 OPLS-AA/L，后者包括广义 Born 表面面积溶剂模型。灵活的命令行和配置文件接口允许用户快速使用所需的配置设置模拟。PHAISTOS 根据 GNU 通用公共许可证 v3.0 发布。源代码和文档可从 http://phaistos.sourceforge.net 免费获得。该软件是用 C++实现的，并在 Linux 和 OSX 平台上进行了测试。
