Department of Pharmacology, University of California San Diego, La Jolla, CA 92093.
Lilly Biotechnology Center, Eli Lilly and Company, San Diego, CA 92121.
Proc Natl Acad Sci U S A. 2022 Nov 22;119(47):e2215420119. doi: 10.1073/pnas.2215420119. Epub 2022 Nov 14.
Topological analysis of protein residue networks (PRNs) is a common method that can help to understand the roles of individual residues. Here, we used protein kinase A as a study object and asked what already known functionally important residues can be detected by network analysis. Along several traditional approaches to weight edges in PRNs we used local spatial pattern (LSP) alignment that assigns high weights to edges only if CαCβ vectors for the corresponding residues retain their mutual positions and orientation. Our results show that even short molecular dynamic simulations of 10 to 20 ns can give convergent values for betweenness and degree centralities calculated from the LSP-based PRNs. Using these centralities, we were able to clearly distinguish a group of residues that are highly conserved in protein kinases and play important functional and regulatory roles. In comparison, traditional methods based on cross-correlation and linear mutual information were much less efficient for this particular task. These results call for reevaluation of the current methods to generate PRNs.
蛋白质残基网络（PRN）的拓扑分析是一种常见的方法，可以帮助理解单个残基的作用。在这里，我们以蛋白激酶 A 为研究对象，询问网络分析可以检测到哪些已经知道的功能重要残基。除了 PRN 中边缘权重的几种传统方法外，我们还使用局部空间模式（LSP）比对，仅当对应残基的 CαCβ 向量保持其相互位置和方向时，才为边缘分配高权重。我们的结果表明，即使是 10 到 20 纳秒的短分子动力学模拟，也可以为基于 LSP 的 PRN 计算的介数和度数中心度给出收敛值。使用这些中心度，我们能够清楚地区分一组在蛋白激酶中高度保守并发挥重要功能和调节作用的残基。相比之下，基于互相关和线性互信息的传统方法在这项特定任务中效率要低得多。这些结果呼吁重新评估当前生成 PRN 的方法。
