COMPUTATIONAL TOOLS FOR MASS SPECTROMETRY-BASED INTERACTOME DATA

摘要 蛋白质复合物和相互作用网络的分析以及它们的动力学行为是至关重要的 在生物学研究中。亲和纯化与质谱联用（AP-MS）现在被广泛用于蛋白质分析。 交互作用分析我们的工作解决了迫切需要开发强大的计算方法和工具 对于AP-MS数据，以及所有类型的鸟枪蛋白质组学MS数据。我们以前开发了 SAINT框架的统计分析和一套工具，用于对蛋白质相互作用进行评分， AP-MS研究。我们领导了一个国际财团，对非特定约束力进行全面编目， AP-MS实验中观察到的蛋白质，建立亲和纯化的污染物储存库 （CRAPome）。作为该项目的一部分开发的这些和其他工具现在被数百个实验室使用 国际吧在这些进展的基础上，我们最近开始制定一项全面的 计算资源REPRINT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相互作用，途径， 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 用于将用户上传的实验AP-MS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， 串联质谱的综合分析。我们将开发一个全面的计算工作流程， 将有助于照亮蛋白质组学的“暗物质”，使肽的无限制鉴定成为可能。 在AP-MS数据集中具有不同的化学和翻译后修饰（PTM）。因此，这项工作将增加 一个新的PTM维度分析AP-MS相互作用组数据。这将改善AP-MS数据的分析 通过允许更准确的量化和检测相互作用的伙伴。它还将允许检测 高度富集的诱饵蛋白及其关键相互作用物上的生物学重要PTM（包括罕见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， 相互作用体我们将继续提供我们广泛使用的计算工具和数据资源， 社区，沿着基准数据集，以进一步开发其他 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.