MONSTER：从原子坐标数据推断大分子结构中的非共价相互作用。
MONSTER: inferring non-covalent interactions in macromolecular structures from atomic coordinate data.
作者信息
Salerno William J, Seaver Samuel M, Armstrong Brian R, Radhakrishnan Ishwar
机构信息
Department of Biochemistry, Molecular Biology and Cell Biology, Northwestern University, Evanston, IL 60208-3500, USA.
出版信息
Nucleic Acids Res. 2004 Jul 1;32(Web Server issue):W566-8. doi: 10.1093/nar/gkh434.
Abstract
A web application for inferring potentially stabilizing non-bonding interactions in macromolecular structures from input atomic coordinate data is described. The core software, called Monster, comprises a PERL wrapper that takes advantage of scripts developed in-house as well as established software in the public domain to validate atomic coordinate files, identify interacting residues and assign the nature of these interactions. The results are assembled and presented in an intuitive and interactive graphical format. Potential applications of Monster range from mining and validating experimentally determined structures to guiding functional analysis. Non-commercial users can perform Monster analysis free of charge at http://monster.northwestern.edu.
摘要
本文描述了一个网络应用程序，该程序可根据输入的原子坐标数据推断大分子结构中潜在的稳定非键相互作用。核心软件名为Monster，它包含一个PERL包装器，该包装器利用内部开发的脚本以及公共领域的现有软件来验证原子坐标文件、识别相互作用的残基并确定这些相互作用的性质。结果以直观且交互式的图形格式进行汇总和呈现。Monster的潜在应用范围从挖掘和验证实验确定的结构到指导功能分析。非商业用户可在http://monster.northwestern.edu上免费进行Monster分析。
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