We present an approach that, by integrating structural data with Direct Coupling Analysis, is able to pinpoint most of the interaction hotspots (i.e. key residues for the biological activity) across very sparse protein families in a single run. An application to the Class A G-protein coupled receptors (GPCRs), both in their active and inactive states, demonstrates the predictive power of our approach. The latter can be easily extended to any other kind of protein family, where it is expected to highlight most key sites involved in their functional activity.

中文翻译：

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基于协同进化数据的相互作用网络强调了跨蛋白质家族的关键残基：以G蛋白偶联受体为例。

我们提出了一种方法，该方法通过将结构数据与直接耦合分析相集成，能够在一次运行中查明非常稀疏的蛋白质家族中的大多数相互作用热点（即生物活性的关键残基）。无论是处于活跃状态还是处于非活跃状态，对A类G蛋白偶联受体（GPCR）的应用都证明了我们方法的预测能力。后者可以很容易地扩展到任何其他种类的蛋白质家族，在该家族中有望突出显示涉及其功能活性的大多数关键部位。