Joint BSC-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, 08034 Barcelona, Spain.
Bioinformatics. 2013 Sep 15;29(18):2363-4. doi: 10.1093/bioinformatics/btt402. Epub 2013 Jul 10.
We introduce pyRMSD, an open source standalone Python package that aims at offering an integrative and efficient way of performing Root Mean Square Deviation (RMSD)-related calculations of large sets of structures. It is specially tuned to do fast collective RMSD calculations, as pairwise RMSD matrices, implementing up to three well-known superposition algorithms. pyRMSD provides its own symmetric distance matrix class that, besides the fact that it can be used as a regular matrix, helps to save memory and increases memory access speed. This last feature can dramatically improve the overall performance of any Python algorithm using it. In addition, its extensibility, testing suites and documentation make it a good choice to those in need of a workbench for developing or testing new algorithms.
The source code (under MIT license), installer, test suites and benchmarks can be found at https://pele.bsc.es/ under the tools section.
Supplementary data are available at Bioinformatics online.
我们介绍了 pyRMSD，这是一个开源的独立 Python 包，旨在提供一种综合且高效的方法，用于对大量结构执行均方根偏差（RMSD）相关的计算。它专门针对快速集体 RMSD 计算进行了优化，例如实现了三种著名的叠加算法的两两 RMSD 矩阵。pyRMSD 提供了自己的对称距离矩阵类，除了可以用作常规矩阵之外，它还有助于节省内存并提高内存访问速度。最后这一特性可以极大地提高使用它的任何 Python 算法的整体性能。此外，其可扩展性、测试套件和文档使其成为开发或测试新算法的工作台的不错选择。
源代码（MIT 许可证）、安装程序、测试套件和基准测试可在 https://pele.bsc.es/ 下的工具部分找到。
补充数据可在 Bioinformatics 在线获得。
