PDF(Pubmed)
Abstract：
In this study, we utilize Protein Residue Networks (PRNs), constructed using Local Spatial Pattern (LSP) alignment, to explore the dynamic behavior of Catabolite Activator Protein (CAP) upon the sequential binding of cAMP. We employed the Degree Centrality of these PRNs to investigate protein dynamics on a sub-nanosecond time scale, hypothesizing that it would reflect changes in CAP\'s entropy related to its thermal motions. We show that the binding of the first cAMP led to an increase in stability in the Cyclic-Nucleotide Binding Domain A (CNBD-A) and destabilization in CNBD-B, agreeing with previous reports explaining the negative cooperativity of cAMP binding in terms of an entropy-driven allostery. LSP-based PRNs also allow for the study of Betweenness Centrality, another graph-theoretical characteristic of PRNs, providing insights into global residue connectivity within CAP. Using this approach, we were able to correctly identify amino acids that were shown to be critical in mediating allosteric interactions in CAP. The agreement between our studies and previous experimental reports validates our method, particularly with respect to the reliability of Degree Centrality as a proxy for entropy related to protein thermal dynamics. Because LSP-based PRNs can be easily extended to include dynamics of small organic molecules, polynucleotides, or other allosteric proteins, the methods presented here mark a significant advancement in the field, positioning them as vital tools for a fast, cost-effective, and accurate analysis of entropy-driven allostery and identification of allosteric hotspots.
摘要：
在这项研究中,我们利用蛋白质残基网络(PRN)，使用局部空间模式(LSP)对齐构建，探讨代谢物激活蛋白(CAP)与cAMP序贯结合的动力学行为。我们采用这些PRN的程度中心性来研究亚纳秒级时间尺度上的蛋白质动力学，假设它将反映与热运动相关的CAP熵的变化。我们表明，第一个cAMP的结合导致环核苷酸结合域A（CNBD-A）的稳定性增加和CNBD-B的不稳定，与以前的报告一致，这些报告解释了cAMP结合在熵驱动变形法方面的负协同性。基于LSP的PRN还允许研究中间性中心性，PRN的另一个图论特征，提供对CAP内全球残留物连通性的见解。使用这种方法，我们能够正确鉴定在介导CAP变构相互作用中起关键作用的氨基酸.我们的研究和以前的实验报告之间的协议验证了我们的方法，特别是关于度中心性作为与蛋白质热动力学相关的熵的代理的可靠性。因为基于LSP的PRN可以很容易地扩展到包括有机小分子的动力学，多核苷酸,或其他变构蛋白，这里提出的方法标志着该领域的重大进步，将它们定位为快速的重要工具，成本效益高,熵驱动变构的准确分析和变构热点的识别。
