Biomolecular Recognition and Binding Mechanisms

我们理解蛋白质在细胞中的相互作用的方法侧重于揭示、建模和定量控制微观和宏观宇宙的一般原理。这一直是生物学研究的一个重要组成部分，然而，最近实验技术的进步和这些新技术产生的前所未有的基因组规模实验数据的积累，现在允许在大规模上解决基本问题。这些内容涉及分子相互作用、双分子识别原理和信号传播机制。细胞的功能需要多种分子间的相互作用，包括蛋白质、蛋白质dna、蛋白质rna、激素、多肽、小分子、脂质等等。生物分子协同工作以提供特定功能，而分子间通信通道的扰动往往导致细胞功能障碍和疾病。充分理解相互作用组需要深入掌握个体相互作用的生物物理原理以及它们在细胞网络中的组织。现象可以用不同的抽象层次来描述。计算和系统生物学通过使用跨学科工具整合和分析来自多个实验来源的复杂数据，努力模拟细胞过程。因此，可以发现变量之间的因果关系和系统的一般特征，即使不知道潜在机制的细节，也可以提出假设并预测系统对扰动的响应行为。这就是提供控制和预测能力的计算机模型的优势所在。与此同时，单个元素和分子的复杂性可以通过分子生物物理学、物理生物学和结构生物学领域来解决，这些领域侧重于潜在的物理化学原理，并可能解释细胞功能的分子机制。在这个框架内，我们已经解决了转录因子（tf）沿着增强子DNA特异性组装的机制是什么？ifn - β增强体提供了一个很好的模型系统：它很小；其组成部分的晶体结构是可用的；还有生化和细胞数据。在ifn - β增强体中，即使连续的DNA反应元件（REs）重叠，也很少有蛋白质-蛋白质相互作用。我们对来自增强体的不同基序组合的分子动力学（MD）模拟表明，协同性是通过RE的独特组织实现的：一个TF的特异性结合可以增强另一个TF与相邻RE的结合，并通过RE的重叠限制其他TF；re的顺序可以决定形成何种配合物；共识REs和非共识REs的交替可以通过优化伙伴之间的相互作用来调节结合特异性。我们的观察结果解释了尽管增强子DNA上邻近tf之间的相互作用有限，但如何获得特异性和协同性。迄今为止，当研究选择性TF结合时，注意力主要集中在RE序列上。然而，DNA上转录因子的顺序和它们之间间隔物的长度可能是转录因子在增强子上特定组合组装的关键因素，从而影响其功能。我们的结果强调通过RE结合位点组织的协同性。在另一个场所，sumoylation是小泛素样修饰物（SUMO）与靶蛋白的共价连接。与其他泛素样途径类似，有三种酶类型连续起作用：激活酶（E1），结合酶（E2）和连接酶（E3）。迄今为止，与其他泛素样机制不同，目标RanGAP1 （target (RanGAP1)）的sumoylation并不绝对需要系统的E3， RanBP2 (E3(RanBP2))，因为E2（E2(Ubc9)）的存在足以sumoylation target （RanGAP1）。然而，在E3存在的情况下，聚合化更有效。为了了解E3（RanBP2）和E2（Ubc9）靶向特异性的作用，我们对E2(Ubc9)-SUMO-Target（RanGAP1）在有和没有E3（RanBP2）连接酶的情况下的结构进行了分子动力学模拟。对E2(Ubc9)-SUMO-Target（RanGAP1）在E3（RanBP2）缺失和存在时的动力学分析表明，两个不同的变弹性位点调节连接酶的活性：(i)在E3（RanBP2）存在时，E2（Ubc9）的环2；（ii）在缺乏E3（RanBP2）的情况下，SUMO的Leu65-Arg70区域。这些结果首次揭示了E3（RanBP2）是如何作为E2（Ubc9）的内在E3的，以及为什么在没有它的情况下，E2(Ubc9)-SUMO-Target（RanGAP1）的活性仍然可以维持，尽管效率较低。蛋白质可以以大量围绕其天然状态的构象存在，这些构象可以通过能量景观来表征。景观显示了个别的山谷，这是构象的亚态。从功能的角度来看，有两个关键点：第一，所有与功能相关的子态都是预先存在的；其次，景观是动态的，亚状态的相对种群将随着变构事件而变化。变构事件扰乱了结构，能量应变传播并改变了种群。这可能导致目标结合位点的形状和性质发生变化。我们介绍了动态构象集成的概述，重点关注信号传导中的变构事件，并提出将平衡波动概念与基因组筛选相结合可以帮助药物发现。

Our approaches to understand protein interactions across the cell focus on uncovering, modeling and quantitating the general principles governing the micro and macro universe. This has always been an important component of biological research, however recent advances in experimental techniques and the accumulation of unprecedented genome-scale experimental data produced by these novel technologies now allow for addressing fundamental questions on a large scale. These relate to molecular interactions, principles of bimolecular recognition, and mechanisms of signal propagation. The functioning of a cell requires a variety of intermolecular interactions including proteinprotein, proteinDNA, proteinRNA, hormones, peptides, small molecules, lipids and more. Biomolecules work together to provide specific functions and perturbations in intermolecular communication channels often lead to cellular malfunction and disease. A full understanding of the interactome requires an in-depth grasp of the biophysical principles underlying individual interactions as well as their organization in cellular networks. Phenomena can be described at different levels of abstraction. Computational and systems biology strive to model cellular processes by integrating and analyzing complex data from multiple experimental sources using interdisciplinary tools. As a result, both the causal relationships between the variables and the general features of the system can be discovered, which even without knowing the details of the underlying mechanisms allow for putting forth hypotheses and predicting the behavior of the systems in response to perturbation. And here lies the strength of in silico models which provide control and predictive power. At the same time, the complexity of individual elements and molecules can be addressed by the fields of molecular biophysics, physical biology and structural biology, which focus on the underlying physico-chemical principles and may explain the molecular mechanisms of cellular function. Within this framework, we have addressed the question of what is the mechanism through which transcription factors (TFs) assemble specifically along the enhancer DNA? The IFN-beta enhanceosome provides a good model system: it is small; its components' crystal structures are available; and there are biochemical and cellular data. In the IFN-beta enhanceosome, there are few protein-protein interactions even though consecutive DNA response elements (REs) overlap. Our molecular dynamics (MD) simulations on different motif combinations from the enhanceosome illustrate that cooperativity is achieved via unique organization of the REs: specific binding of one TF can enhance the binding of another TF to a neighboring RE and restrict others, through overlap of REs; the order of the REs can determine which complexes will form; and the alternation of consensus and non-consensus REs can regulate binding specificity by optimizing the interactions among partners. Our observations offer an explanation of how specificity and cooperativity can be attained despite the limited interactions between neighboring TFs on the enhancer DNA. To date, when addressing selective TF binding, attention has largely focused on RE sequences. Yet, the order of the REs on the DNA and the length of the spacers between them can be a key factor in specific combinatorial assembly of the TFs on the enhancer and thus in function. Our results emphasize cooperativity via RE binding sites organization. On another venue, sumoylation is the covalent attachment of small ubiquitin-like modifier (SUMO) to a target protein. Similar to other ubiquitin-like pathways, three enzyme types are involved that act in succession: an activating enzyme (E1), a conjugating enzyme (E2), and a ligase (E3). To date, unlike other ubiquitin-like mechanisms, sumoylation of the target RanGAP1 (Target(RanGAP1)) does not absolutely require the E3 of the system, RanBP2 (E3(RanBP2)), since the presence of E2 (E2(Ubc9)) is enough to sumoylate Target(RanGAP1). However, in the presence of E3, sumoylation is more efficient. To understand the role of the target specificity of E3(RanBP2) and E2(Ubc9), we carried out molecular dynamics simulations for the structure of E2(Ubc9)-SUMO-Target(RanGAP1) with and without the E3(RanBP2) ligase. Analysis of the dynamics of E2(Ubc9)-SUMO-Target(RanGAP1) in the absence and presence of E3(RanBP2) revealed that two different allosteric sites regulate the ligase activity: (i) in the presence of E3(RanBP2), the E2(Ubc9)'s loop 2; (ii) in the absence of E3(RanBP2), the Leu65-Arg70 region of SUMO. These results provide a first insight into the question of how E3(RanBP2) can act as an intrinsic E3 for E2(Ubc9) and why, in its absence, the activity of E2(Ubc9)-SUMO-Target(RanGAP1) could still be maintained, albeit at lower efficiency. Proteins can exist in a large number of conformations around their native states that can be characterized by an energy landscape. The landscape illustrates individual valleys, which are the conformational substates. From the functional standpoint, there are two key points: first, all functionally relevant substates pre-exist; and second, the landscape is dynamic and the relative populations of the substates will change following allosteric events. Allosteric events perturb the structure, and the energetic strain propagates and shifts the population. This can lead to changes in the shapes and properties of target binding sites. We presented an overview of dynamic conformational ensembles focusing on allosteric events in signaling, and proposed that combining equilibrium fluctuation concepts with genomic screens could help drug discovery.