Computational models for the signaling of tumor necrosis factor receptor on cell surfaces

项目摘要 先天免疫系统构成了宿主防御的第一道防线。外来势力的入侵 病原体导致炎症反应，包括临床症状，如肿胀。 在炎症期间，细胞因子从受损的细胞中释放出来。当它们招募白细胞来 到达损伤部位，清除外来病原体。超家族中的蛋白质 肿瘤坏死因子是这类细胞因子中的一大类。它们与细胞结合在一起 被称为肿瘤坏死因子受体的表面蛋白。肿瘤坏死因子和受体之间的结合触发了 细胞内信号通路，如作为细胞必不可少的调节因子的核因子-κB通路 生死存亡。由于这一在免疫反应中的关键作用，肿瘤坏死因子受体与其 配基正在紧张的研究中。然而，这些研究大多分离出肿瘤坏死因子受体。 与它们通常的生物环境不同。在活细胞中，肿瘤坏死因子受体锚定在 质膜表面。肿瘤坏死因子受体的膜限制导致 对它们的功能产生重大影响。例如，肿瘤坏死因子配体齐聚提供了 与受体的局部结合亲和力高。此外，肿瘤坏死因子受体可以聚集成高水平的 对配基结合后的簇进行排序。这些现象背后的机制并不完全 由于目前的实验限制，我们无法理解。计算建模可以达到 目前在实验室中无法接近的维度。因此，这一行动的目标是 建议分解肿瘤坏死因子可溶配体之间结合动力学的复杂性 和细胞表面结合的受体。我们已经开发了不同的计算方法 蛋白质之间的结合亲和力和模拟蛋白质在 分子和更低分辨率水平。通过将这些方法应用于 细胞表面肿瘤坏死因子受体结合的特殊问题，以及正在进行的 试验性合作，我们特别有兴趣回答以下两个问题 问题：肿瘤坏死因子配体的寡聚是如何调节受体结合的？ 肿瘤坏死因子受体聚集性在调节配体结合方面有哪些功能作用。在……里面 为了研究这两个问题，我们构造了一种新的基于域的刚体模型 并进一步开发了多尺度建模框架，用于定量计算 多价配体与细胞表面多个受体结合的动力学。 我们的长期目标是进一步阐明肿瘤坏死因子介导的信号转导在 调节炎症反应。综上所述，这项研究将有助于揭示 肿瘤坏死因子受体与细胞表面结合的机制。

Project Summary The innate immune system constitutes the first line of host defense. The invasion of external pathogens leads into inflammatory responses, including the clinical signs such as swelling. During inflammation, cytokines are released from injured cells.  They recruit leukocytes to reach the site of injury and remove the foreign pathogens. Proteins in the superfamily of tumor necrosis factor (TNF) are one major class of these cytokines. They bind to the cell surface proteins called TNF-receptors. The binding between TNF and receptors triggers the intracellular signaling pathways, such as NF-κB pathway that is an essential regulator of cell survival. Due to this critical role in immune responses, binding of TNF receptors with their ligands is under intense study. However, most of these studies isolate the TNF receptors from their usual biological surrounding. In living cells, TNF receptors are anchored on surfaces of plasma membrane. The membrane confinement of TNF receptors causes significant impacts on their functions. For instance, the TNF ligand oligomerization provides high local binding avidity to receptors. Moreover, TNF receptors can aggregate into high- order clusters upon ligand binding. Mechanisms underlying these phenomena are not fully understood due to current experimental limitations. Computational modeling can reach dimensions that are currently unapproachable in the laboratory. Thus, the objective of this proposal is to decompose the complexity of binding kinetics between TNF soluble ligands and cell-surface-bound receptors. We have developed different methods for calculating binding affinities between protein and simulating protein binding kinetics on the molecular and lower-resolution levels. Through the application of these methods to the specific problem of TNF receptor binding on cell surfaces, and the establishment of ongoing experimental collaborations, we are specifically interested in answering the following two questions: how does oligomerization of TNF ligands modulate receptor binding, and what are the functional roles of TNF receptor clustering in regulating ligand binding. In order to study these two problems, we construct a new domain-based rigid-body model and further develop a multiscale modeling framework to quantitatively calculate the kinetics of binding between multivalent ligands and multiple receptors on cell surfaces. Our long-term goal is to further elucidate the functional roles of TNF-mediated signaling in regulating the inflammatory responses. In summary, this study will shed light on the basic mechanisms of TNF receptor binding on cell surface.