Extracting dynamical pairwise correlations and identifying key residues from large molecular dynamics trajectories or normal-mode analysis of coarse-grained models are important for explaining various processes like ligand binding, mutational effects, and long-distance interactions. Efficient and flexible tools to perform this task can provide new insights about residues involved in allosteric regulation and protein function. In addition, combining and comparing dynamical coupling information with sequence coevolution data can help to understand better protein function. To this aim, we developed a Python package called correlationplus to calculate, visualize, and analyze pairwise correlations. In this way, the package aids to identify key residues and interactions in proteins. The source code of correlationplus is available under LGPL version 3 at https://github.com/tekpinar/correlationplus. The current version of the package (0.2.0) can be installed with common installation methods like conda or pip in addition to source code installation. Moreover, docker images are also available for usage of the code without installation.

中文翻译：

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通过相关性加法提取蛋白质中变构通讯的动态相关性和识别关键残基

从大分子动力学轨迹或粗粒度模型的正常模式分析中提取动态成对相关性和识别关键残基对于解释配体结合、突变效应和长距离相互作用等各种过程非常重要。执行此任务的有效和灵活的工具可以提供有关变构调节和蛋白质功能所涉及的残基的新见解。此外，将动态耦合信息与序列协同进化数据结合和比较有助于更好地了解蛋白质功能。为此，我们开发了一个名为correlationplus的Python 包来计算、可视化和分析成对相关性。通过这种方式，该软件包有助于识别蛋白质中的关键残基和相互作用。的源代码在 https://github.com/tekpinar/correlationplus 的 LGPL 版本 3 下，correlationplus可用。当前版本的包（0.2.0）除了源代码安装外，还可以使用conda或pip等常用安装方法进行安装。此外，还可以使用 docker 镜像，无需安装即可使用代码。