Institute of Physics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Instituto de Fìsica, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
PLoS Comput Biol. 2020 Mar 2;16(3):e1007630. doi: 10.1371/journal.pcbi.1007630. eCollection 2020 Mar.
In allosteric proteins, the binding of a ligand modifies function at a distant active site. Such allosteric pathways can be used as target for drug design, generating considerable interest in inferring them from sequence alignment data. Currently, different methods lead to conflicting results, in particular on the existence of long-range evolutionary couplings between distant amino-acids mediating allostery. Here we propose a resolution of this conundrum, by studying epistasis and its inference in models where an allosteric material is evolved in silico to perform a mechanical task. We find in our model the four types of epistasis (Synergistic, Sign, Antagonistic, Saturation), which can be both short or long-range and have a simple mechanical interpretation. We perform a Direct Coupling Analysis (DCA) and find that DCA predicts well the cost of point mutations but is a rather poor generative model. Strikingly, it can predict short-range epistasis but fails to capture long-range epistasis, in consistence with empirical findings. We propose that such failure is generic when function requires subparts to work in concert. We illustrate this idea with a simple model, which suggests that other methods may be better suited to capture long-range effects.
在变构蛋白中，配体的结合会在远处的活性位点改变功能。这种变构途径可以作为药物设计的靶点，因此从序列比对数据中推断它们引起了相当大的兴趣。目前，不同的方法导致了相互矛盾的结果，特别是在远距离介导变构的氨基酸之间是否存在长程进化耦合方面。在这里，我们通过研究突现及其在计算机进化的变构材料中执行机械任务的模型中的推断，解决了这个难题。我们在模型中发现了四种类型的突现（协同、信号、拮抗、饱和），它们可以是短程或长程的，并且具有简单的力学解释。我们进行了直接耦合分析（DCA），发现 DCA 很好地预测了点突变的代价，但它是一个相当差的生成模型。引人注目的是，它可以预测短程突现，但不能捕捉长程突现，这与经验发现一致。我们提出，当功能需要各部分协同工作时，这种失败是普遍的。我们用一个简单的模型来说明这个想法，这表明其他方法可能更适合捕捉长程效应。
