COMPUTATIONAL TOOLS FOR MASS SPECTROMETRY-BASED INTERACTOME DATA

摘要 我们的首要目标是满足对强大的计算方法和工具的迫切需求 基于光谱(MS)的蛋白质组学数据。在这里，我们将重点关注生物学研究的三个关键领域：1) “相互作用”的MS表征，即蛋白质-蛋白质相互作用网络和复合体的分析 采用亲和纯化-质谱仪(AP-MS)及相关技术。2)翻译后 修饰(PTM)，它对无数的细胞过程有深远的影响，包括蛋白质如何 与其他蛋白质相互作用，组装成多功能复合体。3)关键的单细胞蛋白质组学 以获得对细胞异质性的更完整的见解(除了单细胞转录组学之外)。 此外，近年来蛋白质组学领域见证了MS技术和 样品制备方案，包括将囚禁离子迁移率分离(TIMS)维度添加到时间- 飞行中的MS(TimsTOF技术)、新的碎裂机制、新的化学标记策略以及 改进的多重定量技术。在这笔赠款下，我们开发了 与污染物储存库一起用于亲和净化的Interactomes(SAINT)框架 (CRAPome)，形成了广泛使用的蛋白质相互作用评估资源(再版)的基础。 我们的DIA-UMERY和IonQuant算法推动了无标记蛋白质定量领域的发展。这部小说 MSFragger的索引算法使多肽的计算速度提高了100倍 从MS谱图中进行鉴定，使新的“开放搜索”和“质量抵消”策略能够全面 PTMS的识别。我们将继续创新工作，开发新的功能和算法 我们的互动分析资源，包括将重印过渡到我们基于MSFragger的新管道， 进一步扩展CRAPome的非特定背景蛋白储存库，并改进相互作用组 得分。利用我们最近的计算进步，我们将开发新的工具来识别大型PTM- 扩展基于MS的互动组数据，并搜索与整个互动组中的变化相关的PTM 不同的条件。在我们新的MSFragger本地化感知开放搜索算法的基础上，我们将开发 用于综合PTM图谱和化学蛋白质组学的新算法。此外，还与以下机构密切合作 该领域的领先者，我们将开发新的算法、工具和基准策略来分析 基于MS的单细胞蛋白质组学数据。我们将继续提供我们广泛使用的计算工具和数据 向生物界提供资源。

ABSTRACT Our overarching aim is to address the critical need for robust computational methods and tools for mass spectrometry (MS)-based proteomics data. Here, we are focusing on three key areas of biological research: 1) MS-based characterization of “interactomes”, i.e., analysis of protein-protein interaction networks and complexes using affinity purification – mass spectrometry (AP-MS) and related technologies. 2) Post-translational modifications (PTMs), which have a profound effect on a myriad of cellular process, including how proteins interact with other proteins and assemble into multi-functional complexes. 3) Single-cell proteomics that is critical for obtaining more complete (in addition to single-cell transcriptomics) insights into cellular heterogeneity. Furthermore, in recent years the field of proteomics has witness tremendous advances in MS technologies and sample preparation protocols, including addition of the trapped ion mobility separation (TIMS) dimension to time- of-flight MS (timsTOF technology), new fragmentation mechanisms, new chemical labeling strategies, and improved multiplex quantitative technologies. Under this grant, we have developed the Statistical Analysis of Interactomes (SAINT) framework which, together with the Contaminant Repository for Affinity Purification (CRAPome), formed the basis for a widely used Resource for Evaluation of PRotein INTeractions (REPRINT). Our DIA-Umpire and IonQuant algorithms have advanced the field of label-free protein quantification. The novel indexing algorithm of MSFragger has led to a 100-fold increase in the computational speed of peptide identification from MS spectra, enabling the new “open search” and “mass offset” strategies for comprehensive identification of PTMs. We will continue our innovative work by developing new functionalities and algorithms in our interactome analysis resources, including transitioning REPRINT to our new MSFragger-based pipeline, further expansion of the CRAPome repository of non-specific background proteins, and improved interactome scoring. Leveraging our recent computational advances, we will develop novel tools to identify PTMs in large- scale MS-based interactome data, and search for PTMs that correlate with changes in the interactomes across different conditions. Building on our new localization-aware open search algorithm of MSFragger, we will develop new algorithms for comprehensive PTM profiling and chemical proteomics. Furthermore, working closely with the leaders in the field, we will develop new algorithms, tools, and benchmarking strategies for the analysis of MS-based single-cell proteomics data. We will continue providing our widely used computational tools and data resources to the biological community.