Bilateral BBSRC-NSF/BIO: Bayesian Quantitative Proteomics

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、活性和相互作用决定了其结构及其功能。用于大规模研究蛋白质的技术统称为蛋白质组学。蛋白质组学中使用的主要方法是质谱（MS），它可以计算分子的分子量和丰度。大多数蛋白质组学工作流程在 MS 之前执行蛋白质消化步骤。消化的结果是所有蛋白质都分解成小链，称为肽。这一步骤已经变得很常见，因为肽质量较低，更容易通过 MS 进行分析，产生更容易解释的数据。此消化步骤中的一个挑战是某些蛋白质快速分解，而另一些蛋白质则消化不完全，产生不可靠的定量数据，当前的分析软件无法完全理解或补偿这些数据。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曼彻斯特大学合作，使用称为贝叶斯建模的强大统计技术开发一套集成的分析技术。这些进步将被纳入免费提供的软件套件中。 串联质谱 (MS/MS) 与液相色谱 (LC) 联用是蛋白质组学中使用的主要技术。最常见的方法是对蛋白质组消化产生的胰蛋白酶片段进行 LC 分离，然后对肽进行串联 MS。整个工作流程被认为是一系列离散的步骤，一些是化学步骤，一些是仪器步骤，一些是信息学步骤，一些是统计步骤。现有软件集中于工作流程的子组件，并包含一系列确定性的、独立的步骤。该项目将把整个蛋白质定量流程转化为以贝叶斯方法为基础的严格统计框架。新框架将整合所有实验获取的数据集的证据，并从蛋白质组学工作流程中未使用的结构中借用力量，包括消化动力学。拟议的管道包括三个协同发展（1）利用所有未识别（肽）特征以及已识别特征来推断样品中最可能存在的蛋白质混合物； (2) 已知蛋白质形式的复杂混合物的差异定量； (3) 发现未知的蛋白质组及其量化特征所携带的所有修饰 (PTM)。这些进步将首次在蛋白质组水平上引起定量灵敏度和解释的阶跃变化。端到端分析解决方案将在以用户为中心、符合标准的 ProteoSuite 软件包中提供，并作为高通量管道的 Galaxy 工作流程。

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.