Computational tools for mass spectrometry-based interactome data

描述(由申请人提供)：分析蛋白质复合体和相互作用网络，以及它们作为时间和细胞状态的函数的动态行为，在生物学研究中具有核心重要性。近年来的技术进步使亲和纯化和质谱学(AP/MS)成为一种高通量和广泛应用的技术。然而，AP/MS数据计算工具的开发却相对滞后。虽然已经开发了许多基于拓扑的交互网络分析方法，但这些方法是针对非常特定类型的AP/MS数据进行优化的，在大多数实验中并不普遍适用。因此，这项提议解决了目前正在生成的数据类型与处理这些数据的适当计算工具之间存在的严重不匹配问题。为此，我们最近展示了无标记定量蛋白质信息的巨大用途，例如可以从AP/MS数据中提取的光谱计数。在这项工作的基础上，我们将开发一个强大的计算框架，通过对多个纯化过程中诱饵和猎物蛋白质的定量图谱进行统计建模，在AP/MS研究中对单个蛋白质-蛋白质相互作用的重要性进行分析。建议的方法将允许组合和比较不同实验室和实验平台上的蛋白质相互作用数据。此外，这项工作将使从AP/MS数据更准确地重建蛋白质复合体，以及作为细胞状态的函数或响应外部扰动的网络变化的分析。通过将AP/MS数据得到的相互作用概率与基于功能基因组学预测等更高层次的信息相结合，我们将进一步提高检测蛋白质相互作用的灵敏度。通过这项工作，我们将更好地了解假阳性蛋白质相互作用的来源，这反过来将有助于设计未来的实验。在与生物学家的合作中，我们将在生物学研究的几个关键领域应用我们的方法，这些领域通过它们对细胞信号基础理解的重要性而联系在一起。它将涉及对人类蛋白激酶、磷酸酶和其他信号蛋白及其相互作用的大规模分析，包括测量相互作用组的动态变化。我们还将向蛋白质组学社区提供一套开源和免费可用的计算工具，以及用于AP/MS数据基准和进一步开发计算方法的正交化验证参考数据集。

DESCRIPTION (provided by applicant): The analysis of protein complexes and interaction networks, and their dynamic behavior as a function of time and cell state, are of central importance in biological research. The recent technological advances have made affinity purification and mass spectrometry (AP/MS) a high-throughput and widely used technique. However, the development of computational tools for AP/MS data has lagged behind. While a number of approaches have being developed for topology-based analysis of interaction networks, these methods were optimized for very specific types of AP/MS data, and are not generally applicable in most experiments. Thus, this proposal addresses the critical mismatch that currently exists between the type of data being generated and the availability of appropriate computational tools for processing these data. To this end, we have recently demonstrated the great utility of label-free quantitative protein information such as spectral counts that can be extracted from AP/MS data. Building upon this work, we will develop a robust computational framework for significance analysis of individual protein-protein interactions in AP/MS studies via statistical modeling of quantitative profiles of bait and prey proteins across multiple purifications. The proposed method will allow combining and comparing protein interaction data across different laboratories and experimental platforms. Furthermore, this work will enable more accurate reconstruction of protein complexes from AP/MS data, as well as the analysis of changes in the networks as a function of the cell states or in response to an external perturbation. By integrating the interaction probabilities derived from AP/MS data with the higher level information such as functional genomics-based predictions, we will further improve the sensitivity of detecting protein interactions. As a result of this work, we will gain a better understanding of the sources of false positive protein interactions, which in turn will help in designing future experiments. In collaboration with biologists, we will apply our methods in several key areas of biological research linked through their significance for fundamental understanding of cell signaling. It will involve large-scale analysis of human protein kinases, phosphatases, and other signaling proteins and their interactions, including measuring dynamic changes in the interactome. We will also provide the proteomic community with a set of open source and freely available computational tools, as well as orthogonally validated reference datasets for benchmarking and further development of computational methods for AP/MS data.