Supra-molecular rules in signalling networks: A single molecule comparative study in cells and tissues

信号传导是蛋白质协调细胞内基本活动和细胞间通讯、调节细胞命运和允许多细胞生物发育的手段。为了在多细胞组织中实现细胞的内聚，一些蛋白质（受体）在细胞表面（质膜）被组织成组，作为天线来检测细胞外的化学信号。从它们在细胞表面的位置，受体检测到多个输入信号，它们将这些输入信号传导到质膜，并在细胞内部输出信号。这些信号通过胞内信号网络在细胞质中解码、放大和处理；一些随后被转导到细胞核，开始DNA转录、复制。然后净效应是决定细胞命运（生长、分化等）。了解蛋白质信号网络中信号的输入和输出是如何组织的是生物学中最令人着迷的问题之一。目前的梦想是找到一种方法，可以“观察”这些网络蛋白质的活动，并在原子分辨率下观察它们的结构细节。这需要在结构生物学中增加一个“时间”维度，以便可以详细描述每种蛋白质中所有原子的时空参数。这是一个巨大的挑战，在无细胞系统中已经开始通过动态实验结合分子模拟来部分解决。然而，在细胞中，特定结构基元的功能不仅受到布朗运动、能量景观和热力学的约束，还受到亚细胞区室中伙伴的局部可用性和细胞环境施加的边界约束，例如在质膜中，具有二维维度、局部曲率和电场。要了解蛋白质在细胞中的功能，必须在唯一与生理相关的“实验室”——细胞中进行观察。这给本已巨大的挑战增加了许多层次的复杂性。我们的工作计划是为了了解细胞在组织内生理环境中的复杂网络信号行为。我们的目的是描述基本的分子成分，信号通路和调节信号结果的超分子结构和时空规则。我们的方法将基于直接观察，“观察”相互作用的拓扑结构及其组成部分随时间的多种变化，并结合数学框架内原子分辨率的行为建模。利用分子生物学技术结合光学方法，我们现在可以对单个基因和基因产物进行注释，筛选蛋白质-蛋白质、蛋白质- dna和小分子相互作用，并量化动态变化。然而，目前只有基于单分子的成像技术能够在细胞中对低丰度蛋白质相互作用进行敏感的时空检测。通过允许实时定量观察结构细节、构象中间体、结合和解离常数、扩散速率和罕见事件，这开始弥合蛋白质结构和功能之间的差距。以前关于复杂蛋白质网络的信息通常来自高通量筛选和/或单细胞模型，使用集合（平均）技术，如生化提取，然后是质谱分析。因此，应用这些信息在分子水平上了解多细胞生物的动态正常生理和/或异质人群中各种疾病状态的发病基础是有限的。我们提出的方法提供了从微观分子组分之间复杂相互作用的变化和对扰动的响应中理解和预测细胞功能特性的方法。

Signalling is the means by which proteins orchestrate basic intra-cellular activities and cell-to-cell communication, to regulate cell fate and to allow the development of multi-cellular organisms. To achieve a cohesive cell fate within a multi-cellular tissue, some proteins (receptors) are organised into groups at the cell surface (the plasma membrane) to function as antennas to detect extracellular chemical cues. From their position at the cell surface the receptors detect multiple inputs which they transduce across the plasma membrane to output signals in the cell interior. These signals are decoded, amplified and processed in the cell cytoplasm by intracellular signalling networks; some are subsequently transduced to the nucleus to initiate DNA transcription, replication. Then net effect is the determination of cell fate (growth, differentiation, etc.). Understanding how signal inputs and outputs are organised in protein signalling networks is one of the most fascinating questions in biology. The current dream is to derive methods that would allow the 'watching' of these network proteins in action and at atomic resolution to see details of their structure. This requires the addition of a 'time' dimension to structural biology so that the spatio-temporal parameters of all atoms in each protein can be described in detail. This is a huge challenge that in cell-free systems has begun to be partially addressed through dynamic experiments combined with molecular simulations. However, in cells, the functions of particular structural motifs are not just constraint by Brownian motions, energy landscapes and thermodynamics, but also by the local availability of partners in subcellular compartments and the boundary constraints imposed by cell environments, for example in the plasma membrane, with its 2D dimensionality, local curvature and electric fields. To understand protein function in cells observations have to be made in the only physiologically-relevant 'Laboratory', the cell. This adds many levels of complexity to an already vast challenge. Our programme of work is geared to understanding the intricate network signalling behaviour of cells in their physiological environments within tissues. We aim at describing the basic molecular ingredients, the signalling pathways and the supra-molecular structural and spatio-temporal rules regulating signalling outcomes. Our methods will be based on direct observation, 'watching' the multiple changes in the topology of interactions and its components with time, in conjunction with the modelling of behaviour at atomic resolution within a mathematical framework. Using molecular biology techniques in combination with optical methods, we can now annotate individual genes and gene products, screen for protein-protein, protein-DNA and small molecule interactions, and quantify dynamic changes. However, only single molecule-based imaging currently offers sensitive spatio-temporal detection in cells for low abundance protein interactions. This is beginning to bridge the gap between protein structure and function by allowing real-time quantitative observations of structural details, conformational intermediates, association and dissociation constants, diffusion rates, and rare events. Previous information on complex protein networks has been derived generally from high-throughput screens and/or single cell models using ensemble (averaged) technologies such as biochemical extraction followed by mass spectrometric analysis. The application of this information to understand at the molecular level the dynamic normal physiology in multi-cellular organisms and/or the pathogenetic basis of various disease states, among the heterogeneous human population is therefore limited. The approach we propose offers the means to understand and predict functional properties of cells from the changes in complex interactions between their microscopic molecular components and in response to perturbations.