Bilateral BBSRC-NSF/BIO: Bayesian Quantitative Proteomics

双边 BBSRC-NSF/BIO：贝叶斯定量蛋白质组学

• 批准号: 2016487
• 负责人: Jeffrey Morris
• 金额: $ 9.81万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2016487&HistoricalAwards=false
• 关键词: Bilateral; BBSRC; NSF; BIO; Bayesian

The functional molecules in cells are proteins - the expression, activity and interactions of particular proteins in any given cell define its structure and what it is capable of doing. The technologies used to study proteins on a large scale are collectively called proteomics. The main method used in proteomics is mass spectrometry (MS), which can calculate the molecular weight and abundance of molecules. The majority of proteomics workflows perform a step of protein digestion prior to MS. The result of digestion is that all the proteins become broken up into small chains, called peptides. This step has become common, because peptides are easier to analyse by MS, due to their lower mass, producing simpler data to interpret. One challenge in this digestion step is that some proteins break down quickly whereas for others digestion is incomplete, producing unreliable quantification data that are not fully understood or compensated for by current analysis software. To overcome this problem, the University of Texas Anderson Cancer Center will collaborate with the University of Manchester in the United Kingdom to develop an integrated suite of analysis techniques using a powerful statistical technique called Bayesian modelling. These advances will be incorporated into a freely available software suite. Tandem Mass Spectrometry (MS/MS) coupled to Liquid Chromatography (LC) is the primary technique used in proteomics. The most common approach is LC separation of tryptic fragments derived from a proteome digestion, followed by tandem MS of the peptides. This entire workflow is conceived as a series of discrete steps, some chemical, some instrumental, some informatics and some statistical. Existing software concentrates on subcomponents of the workflow, and comprise a series of deterministic, self-contained steps. This project will translate the whole protein quantification pipeline into a rigorous statistical framework underpinned by Bayesian methodology. The new framework will integrate evidence across all experimentally acquired datasets, and borrow strength from unused structure within a proteomics workflow, including digestion dynamics. The proposed pipeline consists of three synergistic developments (1) Utilization of all unidentified (peptide) features, as well as identified features, to infer the most likely mixture of proteins present in a sample; (2) Differential quantification of complex mixtures of known proteoforms; (3) Discovery of unknown proteoforms and all modifications (PTMs) carried by their quantification signatures. These advancements will elicit a step-change in quantification sensitivity and interpretation at the proteoform level for the first time. The end-to-end analysis solution will be made available within the user-centric standards compliant ProteoSuite package, and as a Galaxy workflow for high-throughput pipelines.

细胞中的功能分子是蛋白质 - 任何给定细胞中特定蛋白质的表达，活性和相互作用都定义了其结构及其能够做什么。用于大规模研究蛋白质的技术集体称为蛋白质组学。蛋白质组学中使用的主要方法是质谱法（MS），它可以计算分子量和分子丰度。大多数蛋白质组学工作流程在MS之前执行蛋白质消化的步骤。消化的结果是，所有蛋白质都被分解成小链，称为肽。此步骤已经变得很普遍，因为肽由于质量较低而易于通过MS进行分析，从而产生了更简单的数据来解释。在此消化步骤中，一个挑战是，某些蛋白质会迅速分解，而对于其他蛋白质是不完整的，产生了不可靠的量化数据，这些数据未完全理解或通过当前的分析软件来理解或补偿。为了克服这个问题，德克萨斯大学安德森癌症中心将与英国的曼彻斯特大学合作，使用一种称为贝叶斯建模的强大统计技术开发一套综合分析技术。这些进步将纳入免费的软件套件中。 与液相色谱（LC）耦合的串联质谱（MS/MS）是蛋白质组学中使用的主要技术。最常见的方法是脱离蛋白质组消化的胰蛋白酶片段的LC分离，其次是肽的串联MS。整个工作流程被认为是一系列离散步骤，有些化学，有些工具，有些信息学和一些统计学。现有软件集中在工作流的子组件上，并包括一系列确定性，独立的步骤。该项目将将整个蛋白质定量管道转化为由贝叶斯方法论支撑的严格统计框架。新框架将在所有实验获得的数据集中整合证据，并从蛋白质组学工作流中的未使用结构（包括消化动力学）中借用强度。所提出的管道包括三个协同的发展（1）所有未鉴定（肽）特征以及已确定的特征，以推断出样品中最有可能的蛋白质混合物； （2）对已知蛋白质成型的复杂混合物的差异定量； （3）发现未知的蛋白质成型和所有修饰（PTM），其定量特征携带。这些进步将首次引起定量敏感性和解释的逐步变化。端到端分析解决方案将在符合用户标准的ProteoSuite软件包中提供，作为高通量管道的Galaxy工作流程。