The application of statistical methods to comparatively framed questions about the molecular dynamics (MD) of proteins can potentially enable investigations of biomolecular function beyond the current sequence and structural methods in bioinformatics. However, the chaotic behavior in single MD trajectories requires statistical inference that is derived from large ensembles of simulations representing the comparative functional states of a protein under investigation. Meaningful interpretation of such complex forms of big data poses serious challenges to users of MD. Here, we announce Detecting Relative Outlier Impacts from Molecular Dynamic Simulation (DROIDS) 3.0, a method and software package for comparative protein dynamics that includes maxDemon 1.0, a multimethod machine learning application that trains on large ensemble comparisons of concerted protein motions in opposing functional states generated by DROIDS and deploys learned classifications of these states onto newly generated MD simulations. Local canonical correlations in learning patterns generated from independent, yet identically prepared, MD validation runs are used to identify regions of functionally conserved protein dynamics. The subsequent impacts of genetic and/or drug class variants on conserved dynamics can also be analyzed by deploying the classifiers on variant MD simulations and quantifying how often these altered protein systems display opposing functional states. Here, we present several case studies of complex changes in functional protein dynamics caused by temperature, genetic mutation, and binding interactions with nucleic acids and small molecules. We demonstrate that our machine learning algorithm can properly identify regions of functionally conserved dynamics in ubiquitin and TATA-binding protein (TBP). We quantify the impact of genetic variation in TBP and drug class variation targeting the ATP-binding region of Hsp90 on conserved dynamics. We identify regions of conserved dynamics in Hsp90 that connect the ATP binding pocket to other functional regions. We also demonstrate that dynamic impacts of various Hsp90 inhibitors rank accordingly with how closely they mimic natural ATP binding.

中文翻译：

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DROIDS 3.0 - 检测遗传和药物类别变异对保守蛋白质结合动力学的影响

将统计方法应用于有关蛋白质分子动力学 (MD) 的相对框架问题，可以潜在地超越生物信息学中当前的序列和结构方法来研究生物分子功能。然而，单个 MD 轨迹中的混沌行为需要统计推断，该推断来自代表所研究蛋白质的比较功能状态的大型模拟集合。对如此复杂形式的大数据进行有意义的解释，对MD的用户提出了严峻的挑战。在这里，我们宣布检测分子动力学模拟 (DROIDS) 3.0 的相对异常值影响，这是一种用于比较蛋白质动力学的方法和软件包，包括 maxDemon 1.0，一种多方法机器学习应用程序，该应用程序在 DROIDS 生成的相反功能状态下对协同蛋白质运动的大型整体比较进行训练，并将这些状态的学习分类部署到新生成的 MD 模拟中。从独立但相同准备的 MD 验证运行生成的学习模式中的局部规范相关性用于识别功能保守的蛋白质动力学区域。遗传和/或药物类别变体对保守动力学的后续影响也可以通过在变体 MD 模拟中部署分类器并量化这些改变的蛋白质系统显示相反功能状态的频率来分析。在这里，我们介绍了由温度、基因突变、以及与核酸和小分子的结合相互作用。我们证明我们的机器学习算法可以正确识别泛素和 TATA 结合蛋白 (TBP) 中功能保守的动力学区域。我们量化了 TBP 中遗传变异和靶向 Hsp90 ATP 结合区域的药物类别变异对保守动力学的影响。我们确定了 Hsp90 中将 ATP 结合口袋连接到其他功能区域的保守动力学区域。我们还证明了各种 Hsp90 抑制剂的动态影响与它们模拟天然 ATP 结合的密切程度相应地排名。我们量化了 TBP 中遗传变异和靶向 Hsp90 ATP 结合区域的药物类别变异对保守动力学的影响。我们确定了 Hsp90 中将 ATP 结合口袋连接到其他功能区域的保守动力学区域。我们还证明了各种 Hsp90 抑制剂的动态影响与它们模拟天然 ATP 结合的密切程度相应地排名。我们量化了 TBP 中遗传变异和靶向 Hsp90 ATP 结合区域的药物类别变异对保守动力学的影响。我们确定了 Hsp90 中将 ATP 结合口袋连接到其他功能区域的保守动力学区域。我们还证明了各种 Hsp90 抑制剂的动态影响与它们模拟天然 ATP 结合的密切程度相应地排名。