Bilateral NSF/BIO-BBSRC: Bayesian Quantitative Proteomics

研究细胞中的分子的尖端技术正在推动生命科学的研究向前发展。细胞中的功能分子是蛋白质--特定蛋白质在任何特定细胞中的表达、活性和相互作用决定了它的结构和它能做什么。例如，与正常细胞相比，我们通常对研究疾病细胞中存在什么蛋白质以及蛋白质的数量感兴趣，因为蛋白质的特性可能有助于我们理解疾病过程，并寻找新的药物靶点。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仪(MS)，它可以计算分子的相对分子质量和丰度。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被分解成小链，称为多肽。这一步骤已经变得很常见，因为多肽更容易被MS分析，因为它们的质量较低，产生的数据更容易解释。然后对这组多肽进行识别，并经常在不同的条件下(例如，疾病细胞与健康细胞)进行量化。我们通常知道一个多肽是从特定的亲本蛋白质中衍生出来的，因此我们可以使用该多肽的特性和量化作为我们感兴趣的样本中该蛋白质行为的替代测量，因此这些工作流程被称为“自下而上”。蛋白质消化的一个问题是，一些蛋白质比其他蛋白质分解得更快--因为一些蛋白质/肽消化不完整，产生了不可靠的量化数据，目前分析软件没有完全理解或弥补这一点。尽管自下而上的研究在该领域占据主导地位，但它们目前有几个显著的缺陷。蛋白质是在细胞中以多种不同的、相关的形式存在的分子，被称为蛋白质形式-通过编码蛋白质的基因以不同的方式被加工(或者剪接)，或者通过添加功能上重要的化学基团，称为翻译后修饰(PTM)。由于不同的蛋白质形式之间只有一个或几个肽是不同的，它们更具挑战性(或者用目前的技术不可能)进行准确的量化。目前的蛋白质组学实践普遍忽视了这个问题--丢失了大量关于系统中分子的真实性质的数据。有一些研究完整蛋白质及其蛋白质形式的MS技术(称为自上而下方法)，但目前这些技术不能在高通量模式下工作，因此通常用于对少数蛋白质的靶向研究。为了在蛋白质形式的量化和发现方面取得阶段性变化，我们将利用一种名为贝叶斯建模的强大统计技术开发一套集成的分析技术。使用贝叶斯方法，手头的问题被概率地模拟了数千次。通过解释得出的不同结论的范围，我们可以了解我们对结果的确定程度，这是至关重要的，因为MS数据集中证据的微妙性质。本质上，我们的计算技术将提供与自上而下技术相同的关于单个蛋白质形式的数据质量(包括PTM的新发现)，但基于自下而上(以肽为中心)的工作流程-因此，第一次能够以高通量模式实现高精度的蛋白质形式水平的发现和量化。为了确保快速和广泛地采用我们的新方法，我们将把我们的进步整合到我们正在开发的免费软件套件ProteoSuite中。

Research in the life sciences is being driven forward by cutting-edge techniques for studying the molecules acting in cells. 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. As one example, we are often interested in studying what proteins are present in diseased cells and in what quantities, compared with normal cells, since the identity of the proteins may help us understand the disease process, and the search for new drug targets. 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. The set of peptides is then identified and often quantified across different conditions (e.g. disease versus healthy cells). We often know that a peptide was derived from a specific parent protein, and so we can use the identity and quantification of that peptide as a proxy measure for the behaviour of the protein across our samples of interest, and as such these workflows are called "bottom-up". One issue with the digestion of proteins is that some proteins break down quicker than others - for some proteins/peptides digestion is incomplete, producing unreliable quantification data, which at present is not fully understood or compensated for by the analysis software.While bottom-up studies dominate the field, they currently have several significant drawbacks. Proteins are molecules that tend to exist in multiple different, related forms in the cells, which have been called proteoforms - through the gene encoding the protein being processed in different ways (alternatively splicing), or through the addition of functionally important chemical groups, called post-translational modifications (PTMs). Since only one or a few peptides are different between different proteoforms, they are far more challenging (or impossible with current techniques) to quantify accurately. Current practice in proteomics generally ignores this problem - losing vast amounts of data about the true nature of the molecules in the system. There are MS techniques for studying intact proteins and their proteoforms (called top-down methods), but at present these do not function in high-throughput mode, and thus are typically used for targeted studies on a small number of proteins.In order to make a step change in the quantification and discovery of proteoforms, we will develop an integrated suite of analysis techniques using a powerful statistical technique called Bayesian modelling. With Bayesian approaches, the problem at hand is simulated many thousands of times probabilistically. By interpreting the range of different conclusions reached, we can get an idea of how certain we are about the results, which is crucial given the subtle nature of the evidence within the MS datasets. In essence, our computational techniques will deliver the same quality of data about individual proteoforms (including novel discovery of PTMs) as top-down techniques, but based off bottom-up (peptide-focussed) workflows - thus, for the first time, enabling highly accurate proteoform-level discovery and quantification in high-throughput mode. To ensure rapid and wide uptake of our new methods, we will integrate our advancements into a freely available software suite we are developing, ProteoSuite.