论文标题
蛋白质中的关键相互作用模式通过分区功能的群集扩展揭示
Key interaction patterns in proteins revealed by cluster expansion of the partition function
论文作者
论文摘要
结构化蛋白质的天然构象通过复杂的相互作用网络稳定。我们分析了构成这种网络的基本模式，并根据它们在塑造蛋白质序列设计中的重要性进行排名。为了实现这一目标，我们在序列的空间中采用了分区函数的群集扩展，并通过数值评估了每个群集的统计重要性。此过程的一个重要特征是将其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偶数节点的循环，而集团通常是有害的。每个群集还为序列熵提供了贡献，这是倍数的进化可设计性的度量。我们将与不同相互作用模式相关的熵与它们在真实蛋白质的天然结构中的丰度进行了比较。
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.
