Information Processing by Post-translational Modification

总结/摘要 生物体中的大多数蛋白质通过化学基团共价连接到 特定的氨基酸残基。这种“翻译后修饰”（PTM）允许蛋白质功能改变， 在生理时间尺度上进行调节。PTM参与调节大多数细胞过程 并且在重大疾病中经常紊乱。一种蛋白质具有多种类型的结构并不罕见。 修饰，因为这些修饰发生在蛋白质的多个位点上，并且对于不同位点上的修饰， 结合位点相互作用以影响蛋白质功能。这可能会造成爆炸， 组合修饰状态。肿瘤抑制基因p53，也就是本研究的重点， 100多个修饰位点，创造了1030多个组合的可能性， 在这个单一的蛋白质上的修饰状态。这些国家中很少有人会在任何时候出现， 留下了开放的问题，哪些国家存在，以及他们如何在不同的细胞变化， 条件我们以前的工作为解决这些问题奠定了基础。我们有 介绍了“modforms”和“modform分布”的定量语言，它们提供了一个 严格的生物物理基础，描述PTM状态在体内。我们创造了数学方法 用于分析可以测量这种分布的质谱（MS）方法， 决定了它们的基本极限我们已经表明，必须将“自下而上”和 “中间向下”MS，其中蛋白质首先以肽的形式消化，总体上具有“自上而下”MS proteins.这些不同类型的MS数据将蛋白质的模态分布限制在高- 三维“变形区”。我们已经开发了可公开访问的软件， 地区我们发现p53整合了其脉动表达动力学和PTM状态， 作为一个中央蜂窝枢纽和协调多个下游途径，以响应多个 上游条件。在这里，我们以这些发现为基础，建立了一个经过测试的多学科小组， 他们的专业知识涵盖数学、计算、细胞生物学和质谱分析。 我们的目标是开发MS方法来估计典型细胞蛋白质的modform区域，并使用 这些方法揭示了p53如何结合动力学和modforms来处理信息。通过 专注于这样一个具有挑战性的范例，我们希望了解更多关于p53本身的信息， 开发可以广泛应用于其他细胞蛋白的概念和方法，其中PTM 发挥核心作用。

Summary/Abstract Most proteins in an organism are reversibly modified by covalent attachment of chemical groups to specific amino-acid residues. Such “post-translational modification” (PTM) allows protein function to be modulated on a physiological time-scale. PTMs are involved in regulating most cellular processes and frequently disordered in major diseases. It is not uncommon for a protein to have many types of modification, for these to occur on multiple sites across the protein and for modifications on different sites to interact combinatorially to influence protein function. This can create an explosion of combinatorial modification states. The tumor suppressor p53, on which this proposal will focus, has more than 100 sites of modification, creating the potential for more than 1030 combinatorial modification states on this single protein. Very few of these states will be present at any time but that leaves open the question of which states are present and how they vary under different cellular conditions. Our previous work has laid a foundation for addressing these questions. We have introduced the quantitative language of “modforms” and “modform distributions”, which provide a rigorous biophysical basis for describing PTM states in vivo. We have created mathematical methods for analyzing the mass-spectrometry (MS) approaches which can measure such distributions and have determined their fundamental limits. We have shown the necessity of combining “bottom-up” and “middle-down” MS, in which proteins are first digested in peptides, with “top-down” MS on whole proteins. These different types of MS data constrain a protein's modform distribution within a high- dimensional “modform region”. We have developed publicly-accessible software for estimating such regions. We have discovered that p53 integrates its pulsatile expression dynamics and its PTM state to act as a central cellular hub and orchestrate multiple downstream pathways in response to multiple upstream conditions. Here, we build on these findings with a tested multi-disciplinary group of collaborators, whose expertise spans mathematics, computation, cell biology and mass spectrometry. Our goal is to develop MS methods to estimate modform regions of typical cellular proteins and to use those methods to unravel how p53 combines dynamics and modforms to process information. By focusing on such a challenging exemplar, we expect to learn a great deal about p53 itself while developing concepts and methods that can be widely applied to other cellular proteins in which PTMs play a central role.