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ELIHKSIR网络服务器:与响应调节因子相互作用的组氨酸激酶传感器的进化联系推断

ELIHKSIR Web Server: Evolutionary Links Inferred for Histidine Kinase Sensors Interacting with Response Regulators.

作者信息

Sinner Claude, Ziegler Cheyenne, Jung Yun Ho, Jiang Xianli, Morcos Faruck

机构信息

Department of Biological Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.

Center for Systems Biology, University of Texas at Dallas, Richardson, TX 75080, USA.

出版信息

Entropy (Basel). 2021 Jan 30;23(2):170. doi: 10.3390/e23020170.

Abstract

Two-component systems (TCS) are signaling machinery that consist of a histidine kinases (HK) and response regulator (RR). When an environmental change is detected, the HK phosphorylates its cognate response regulator (RR). While cognate interactions were considered orthogonal, experimental evidence shows the prevalence of crosstalk interactions between non-cognate HK-RR pairs. Currently, crosstalk interactions have been demonstrated for TCS proteins in a limited number of organisms. By providing specificity predictions across entire TCS networks for a large variety of organisms, the ELIHKSIR web server assists users in identifying interactions for TCS proteins and their mutants. To generate specificity scores, a global probabilistic model was used to identify interfacial couplings and local fields from sequence information. These couplings and local fields were then used to construct Hamiltonian scores for positions with encoded specificity, resulting in the specificity score. These methods were applied to 6676 organisms available on the ELIHKSIR web server. Due to the ability to mutate proteins and display the resulting network changes, there are nearly endless combinations of TCS networks to analyze using ELIHKSIR. The functionality of ELIHKSIR allows users to perform a variety of TCS network analyses and visualizations to support TCS research efforts.

摘要

双组分系统(TCS)是由组氨酸激酶(HK)和响应调节因子(RR)组成的信号传导机制。当检测到环境变化时,HK会使其同源的响应调节因子(RR)磷酸化。虽然同源相互作用被认为是正交的,但实验证据表明非同源HK-RR对之间存在串扰相互作用。目前,仅在有限数量的生物体中证明了TCS蛋白的串扰相互作用。通过为多种生物体的整个TCS网络提供特异性预测,ELIHKSIR网络服务器可帮助用户识别TCS蛋白及其突变体的相互作用。为了生成特异性分数,使用全局概率模型从序列信息中识别界面耦合和局部场。然后,这些耦合和局部场被用于构建具有编码特异性的位置的哈密顿分数,从而得到特异性分数。这些方法被应用于ELIHKSIR网络服务器上可用的6676种生物体。由于能够对蛋白质进行突变并显示由此产生的网络变化,使用ELIHKSIR可以分析几乎无穷无尽的TCS网络组合。ELIHKSIR的功能允许用户进行各种TCS网络分析和可视化,以支持TCS研究工作。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ba5/7911359/86f86994461f/entropy-23-00170-g0A1.jpg

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