Computational tools for mass spectrometry-based interactome data

描述（由申请人提供）：蛋白质复合物和相互作用网络的分析，以及它们作为时间和细胞状态函数的动态行为，在生物学研究中是至关重要的。近年来的技术进步使亲和纯化和质谱法（AP/MS）成为一种高通量和广泛应用的技术。然而，AP/MS数据的计算工具的发展滞后。虽然已经开发了许多基于拓扑的交互网络分析方法，但这些方法仅针对非常特定类型的AP/MS数据进行了优化，并且在大多数实验中并不普遍适用。因此，本建议解决了当前所生成的数据类型与处理这些数据的适当计算工具的可用性之间存在的关键不匹配。为此，我们最近证明了可以从AP/MS数据中提取的无标记定量蛋白质信息（如光谱计数）的巨大效用。在这项工作的基础上，我们将开发一个强大的计算框架，通过对多种纯化的诱饵和猎物蛋白质的定量特征进行统计建模，对AP/MS研究中单个蛋白质-蛋白质相互作用进行显著性分析。所提出的方法将允许组合和比较不同实验室和实验平台的蛋白质相互作用数据。此外，这项工作将能够从AP/MS数据中更准确地重建蛋白质复合物，以及分析作为细胞状态函数或响应外部扰动的网络变化。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的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。作为这项工作的结果，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。通过与生物学家的合作，我们将把我们的方法应用于生物学研究的几个关键领域，这些领域通过它们对细胞信号传导的基本理解的重要性而联系在一起。它将涉及人类蛋白激酶、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。我们还将为蛋白质组学社区提供一套开源和免费的计算工具，以及正交验证的参考数据集，用于基准测试和进一步开发AP/MS数据的计算方法。

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. PUBLIC HEALTH RELEVANCE: The proposed computational work will enable statistically robust and quantitative analysis of protein-protein interactions and protein complexes using affinity purification - mass spectrometry (AP/MS) approach. The bioinformatics methods will allow establishing a computational framework for quality assessment, analysis, modeling, and cross-laboratory comparison of AP/MS data. The tools and methods will be of great utility for both large collaborative interactome projects and small scale studies. All computational tools developed as a part of this proposal will be made freely available to the research community.