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  • 标题:Scoria: a Python module for manipulating 3D molecular data
  • 本地全文:下载
  • 作者:Patrick Ropp ; Aaron Friedman ; Jacob D. Durrant
  • 期刊名称:Journal of Cheminformatics
  • 印刷版ISSN:1758-2946
  • 电子版ISSN:1758-2946
  • 出版年度:2017
  • 卷号:9
  • 期号:1
  • 页码:52
  • DOI:10.1186/s13321-017-0237-8
  • 语种:English
  • 出版社:BioMed Central
  • 摘要:Third-party packages have transformed the Python programming language into a powerful computational-biology tool. Package installation is easy for experienced users, but novices sometimes struggle with dependencies and compilers. This presents a barrier that can hinder the otherwise broad adoption of new tools. We present Scoria, a Python package for manipulating three-dimensional molecular data. Unlike similar packages, Scoria requires no dependencies, compilation, or system-wide installation. One can incorporate the Scoria source code directly into their own programs. But Scoria is not designed to compete with other similar packages. Rather, it complements them. Our package leverages others (e.g. NumPy, SciPy), if present, to speed and extend its own functionality. To show its utility, we use Scoria to analyze a molecular dynamics trajectory. Our FootPrint script colors the atoms of one chain by the frequency of their contacts with a second chain. We are hopeful that Scoria will be a useful tool for the computational-biology community. A copy is available for download free of charge (Apache License 2.0) at http://durrantlab.com/scoria/ . Graphical abstract .
  • 关键词:Molecular modeling ; Structural biology ; Computational biology ; Python
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