Abstract：
Correlation analysis and its close variant principal component analysis are tools widely applied to predict the biological functions of macromolecules in terms of the relationship between fluctuation dynamics and structural properties. However, since this kind of analysis does not necessarily imply causation links among the elements of the system, its results run the risk of being biologically misinterpreted. By using as a benchmark the structure of ubiquitin, we report a critical comparison of correlation-based analysis with the analysis performed using two other indicators, response function and transfer entropy, that quantify the causal dependence. The use of ubiquitin stems from its simple structure and from recent experimental evidence of an allosteric control of its binding to target substrates. We discuss the ability of correlation, response and transfer-entropy analysis in detecting the role of the residues involved in the allosteric mechanism of ubiquitin as deduced by experiments. To maintain the comparison as much as free from the complexity of the modeling approach and the quality of time series, we describe the fluctuations of ubiquitin native state by the Gaussian network model which, being fully solvable, allows one to derive analytical expressions of the observables of interest. Our comparison suggests that a good strategy consists in combining correlation, response and transfer entropy, such that the preliminary information extracted from correlation analysis is validated by the two other indicators in order to discard those spurious correlations not associated with true causal dependencies.
摘要：
相关分析及其紧密变量主成分分析是广泛用于根据波动动力学与结构性质之间的关系来预测大分子生物学功能的工具。然而,由于这种分析不一定意味着系统要素之间的因果关系，其结果存在被生物学误解的风险。＆＃xD;通过使用泛素的结构作为基准，我们报告了基于相关性的分析与使用其他两个指标进行的分析的关键比较，响应函数和传递熵，量化因果依赖性。泛素的使用源于其简单的结构以及最近的变构控制其与靶底物结合的实验证据。
我们讨论了相关性的能力，响应和转移熵分析在检测涉及泛素变构机制的残基的作用中，如实验推导的。＆＃xD;为了保持比较尽可能不受建模方法的复杂性和时间序列的质量，我们用高斯网络模型描述泛素天然态的波动，完全可解决，允许人们推导出感兴趣的可观测值的解析表达式。
我们的比较表明，一个好的策略在于结合相关性，响应和传递熵，这样，从相关性分析中提取的初步信息被其他两个指标验证，以丢弃那些与真正的因果相关性无关的虚假相关性。
