COMPUTATIONAL TOOLS FOR MASS SPECTROMETRY-BASED INTERACTOME DATA

抽奖 蛋白质复合体和相互作用网络及其动态行为的分析是至关重要的 在生物学研究中。亲和纯化-质谱联用(AP-MS)是目前广泛应用于蛋白质分析的方法 交互作用分析。我们的工作满足了开发健壮的计算方法和工具的迫切需要 用于AP-MS数据，以及所有类型的鸟枪式蛋白质组学MS数据。我们之前已经开发了 INTERACTOMES(INTERACTOMES)框架和一套用于评估蛋白质相互作用的工具 AP-MS研究。我们已经领导了一个国际财团对非特定约束进行全面编目 AP-MS实验中观察到的蛋白质，建立亲和纯化的污染库 (CRAPome)。作为该项目的一部分开发的这些工具和其他工具现在被数百个实验室使用 全世界。在这些进展的基础上，我们最近启动了一项全面的 计算资源重印，允许生物学家处理他们自己的AP-MS数据，并可视化和 交互式探索在先前已知的交互、路径 和功能类别。我们将进一步开发这一资源，包括实施先进的网络 用户上传的AP-MS实验数据与外部数据集成的可视化选项和方法 信息。此外，我们最近开发了一种新的数据索引算法，该算法能够实现超快和 串联质谱学的综合分析。我们将开发一个全面的计算工作流， 通过不受限制地鉴定多肽，将有助于照亮蛋白质组学的“暗物质” 在AP-MS数据集中具有不同的化学修饰和翻译后修饰(PTM)。因此，这项工作将添加 分析AP-MS交互作用组数据的新的PTM维度。这将改进AP-MS数据的分析 通过允许对互动伙伴进行更准确的量化和检测。它还将允许检测 高度浓缩的诱饵蛋白及其关键相互作用上的生物重要的PTM(包括稀有的PTM)， 这反过来将有助于揭示这些PTM在动态和特定条件下的作用 互动。我们将继续为生物科学提供我们广泛使用的计算工具和数据资源 社区，以及基准数据集，供其他机构进一步开发计算方法 科学家。

ABSRACT The analysis of protein complexes and interaction networks, and their dynamic behavior are of central importance in biological research. Affinity purification coupled with mass spectrometry (AP-MS) is now widely used for protein interaction analysis. Our work addresses the critical need to develop robust computational methods and tools for AP-MS data, and all types of shotgun proteomics MS data in general. We have previously developed the Statistical Analysis of INTeractomes (SAINT) framework and a suite of tools for scoring protein interactions in AP-MS studies. We have led an international consortium to comprehensively catalogue the non-specific binding proteins observed in AP-MS experiments, establishing the Contaminant Repository for Affinity Purification (CRAPome). These and other tools developed as part of this project are now used by hundreds of laboratories worldwide. Building upon these advances, we have recently initiated the development of a comprehensive computational resource REPRINT that allows biologist to process their own AP-MS data and to visualize and interactive explore the resulting interaction networks in the context of previously known interactions, pathways, and functional categories. We will further develop this resource, including implementation of advanced network visualization options and methods for integration of user-uploaded experimental AP-MS data with external information. Furthermore, we have recently developed a new data indexing algorithm that enables ultrafast and comprehensive analysis of tandem mass spectra. We will develop a comprehensive computational workflow that will help to shine the light on the “dark matter” of proteomics by enabling unrestricted identification of peptides with different chemical and post-translational modifications (PTMs) in AP-MS datasets. Thus, this work will add a new PTM dimension to the analysis of AP-MS interactome data. This will improve the analysis of AP-MS data by allowing more accurate quantification and detection of interacting partners. It will also allow detection of biologically important PTMs (including rare PTMs) on highly enriched bait proteins and their key interactors, which in turn will assist with uncovering the role of those PTMs on the dynamic and condition-specific interactomes. We will continue providing our widely used computational tools and data resources to the biological community, along with benchmark datasets for further development of computational methods by other scientists.