Borisov Nikolay M, Markevich Nick I, Hoek Jan B, Kholodenko Boris N
Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA.
Biophys J. 2005 Aug;89(2):951-66. doi: 10.1529/biophysj.105.060533. Epub 2005 May 27.
After activation, many receptors and their adaptor proteins act as scaffolds displaying numerous docking sites and engaging multiple targets. The consequent assemblage of a variety of protein complexes results in a combinatorial increase in the number of feasible molecular species presenting different states of a receptor-scaffold signaling module. Tens of thousands of such microstates emerge even for the initial signal propagation events, greatly impeding a quantitative analysis of networks. Here, we demonstrate that the assumption of independence of molecular events occurring at distinct sites enables us to approximate a mechanistic picture of all possible microstates by a macrodescription of states of separate domains, i.e., macrostates that correspond to experimentally verifiable variables. This analysis dissects a highly branched network into interacting pathways originated by protein complexes assembled on different sites of receptors and scaffolds. We specify when the temporal dynamics of any given microstate can be expressed using the product of the relative concentrations of individual sites. The methods presented here are equally applicable to deterministic and stochastic calculations of the temporal dynamics. Our domain-oriented approach drastically reduces the number of states, processes, and kinetic parameters to be considered for quantification of complex signaling networks that propagate distinct physiological responses.
激活后,许多受体及其衔接蛋白充当支架,展示众多对接位点并结合多个靶点。由此形成的各种蛋白质复合物组合导致呈现受体 - 支架信号模块不同状态的可行分子种类数量呈组合式增加。即使对于初始信号传播事件,也会出现数以万计的此类微状态,极大地阻碍了对网络的定量分析。在此,我们证明,假设在不同位点发生的分子事件相互独立,使我们能够通过对单独结构域状态的宏观描述,即对应于实验可验证变量的宏观状态,来近似所有可能微状态的机制图景。该分析将一个高度分支的网络分解为在受体和支架的不同位点组装的蛋白质复合物产生的相互作用途径。我们明确了何时任何给定微状态的时间动态可以用各个位点相对浓度的乘积来表示。这里介绍的方法同样适用于时间动态的确定性和随机性计算。我们的面向结构域的方法极大地减少了量化传播不同生理反应的复杂信号网络时要考虑的状态、过程和动力学参数的数量。