标题： 蛋白质中关键相互作用模式通过划分函数的聚类展开揭示 显示英文标题

标题： Key interaction patterns in proteins revealed by cluster expansion of the partition function

摘要： 结构蛋白的天然构象由复杂的相互作用网络稳定。 我们分析了构成该网络的基本模式，并根据它们在塑造蛋白质序列设计中的重要性对其进行排序。 为了达到这个目标，我们在序列空间中采用了分区函数的聚类展开，并数值评估了每个聚类的统计重要性。 此过程的一个重要特点是它应用于密集的有限系统。 我们发现对分区函数贡献最大的模式是节点数为偶数的环，而团通常是有害的。 每个聚类也对序列熵做出贡献，这是衡量折叠进化设计能力的指标。 我们将与不同相互作用模式相关的熵与其在真实蛋白质天然结构中的丰度进行了比较。 显示英文摘要

摘要： The native conformation of structured proteins is stabilized by a complex network of interactions. We analyzed the elementary patterns that constitute such network and ranked them according to their importance in shaping protein sequence design. To achieve this goal, we employed a cluster expansion of the partition function in the space of sequences and evaluated numerically the statistical importance of each cluster. An important feature of this procedure is that it is applied to a dense, finite system. We found that patterns that contribute most to the partition function are cycles with even numbers of nodes, while cliques are typically detrimental. Each cluster also gives a contribute to the sequence entropy, which is a measure of the evolutionary designability of a fold. We compared the entropies associated with different interaction patterns to their abundances in the native structures of real proteins.