Bilateral NSF/BIO-BBSRC: Bayesian Quantitative Proteomics

双边 NSF/BIO-BBSRC：贝叶斯定量蛋白质组学

• 批准号: BB/M024954/1
• 负责人: Andrew Dowsey
• 金额: $ 39.67万
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FM024954%2F1
• 关键词: Bilateral; NSF; BIO; BBSRC; Bayesian

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.

生命科学的研究是通过研究在细胞中作用的分子的尖端技术来驱动的。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。作为一个例子，我们通常有兴趣研究患病细胞中哪些蛋白质以及与正常细胞相比的数量，因为蛋白质的身份可能有助于我们了解疾病过程，并寻找新药物靶标。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。然后鉴定出一组肽并经常在不同条件下（例如疾病与健康细胞）进行量化。我们经常知道肽是从特定的母蛋白中得出的，因此我们可以使用该肽的身份和定量作为对我们感兴趣的样本中蛋白质行为的替代度量，因此这些工作流称为“自下而上”。消化蛋白质的一个问题是，某些蛋白质比其他蛋白更快地分解 - 对于某些蛋白质/肽消化不完整，产生不可靠的量化数据，目前尚未通过分析软件完全理解或补偿，而自下而上的研究主导了该领域，他们目前有几个重要的弊端。蛋白质是通过在细胞中以多种不同的相关形式存在的分子，这些分子被称为蛋白质成型 - 通过编码以不同方式处理的蛋白质的基因（替代剪接），或通过添加功能上重要的化学基团（称为后翻译后修饰（PTMS））。由于不同的蛋白基型之间只有一个或几个肽是不同的，因此它们更具挑战性（或目前的技术不可能）来准确量化。蛋白质组学的当前实践通常忽略了这个问题 - 失去了有关系统中分子的真实性质的大量数据。有一些用于研究完整蛋白质及其蛋白质成型的MS技术（称为自上而下的方法），但目前这些技术在高通量模式下不起作用，因此通常用于针对少量蛋白质的有针对性研究。在少量的蛋白质上，以对蛋白质的量化和发现进行跨性别型的量化，我们将开发出一种集成的统一技术，以实现蛋白质的量化，这是一个有效的统计技术。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性，考虑到MS数据集中证据的微妙性质至关重要。从本质上讲，我们的计算技术将提供有关单个蛋白质成型（包括新发现的PTM）作为自上而下的技术的相同质量的数据，但基于自下而上的（肽的）工作流程（首次启用高度精确的蛋白质成型型发现和高发型模式下的定量）。为了确保我们的新方法的快速和广泛的吸收，我们将将我们的进步集成到我们正在开发的ProteOsuite的免费软件套件中。

项目成果

Mast cell infiltration of the choroid and protease release are early events in age-related macular degeneration associated with genetic risk at both chromosomes 1q32 and 10q26.

• DOI: 10.1073/pnas.2118510119
• 发表时间: 2022-05-17
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1

The need for statistical contributions to bioinformatics at scale, with illustration to mass spectrometry

需要对大规模生物信息学做出统计贡献，并以质谱法为例

• DOI: 10.1177/1471082x17708519
• 发表时间: 2017
• 期刊: Statistical Modelling
• 影响因子: 1
• 作者: Dowsey A

Cognitive dysfunction in diabetic rats is prevented by pyridoxamine treatment. A multidisciplinary investigation

• DOI: 10.1016/j.molmet.2019.08.003
• 发表时间: 2019-10-01
• 期刊: MOLECULAR METABOLISM
• 影响因子: 8.1
• 作者: Kassab, Sarah;Begley, Paul;Gardiner, Natalie J.
• 通讯作者: Gardiner, Natalie J.

mzMLb: a future-proof raw mass spectrometry data format based on standards-compliant mzML and optimized for speed and storage requirements

mzMLb：一种面向未来的原始质谱数据格式，基于符合标准的 mzML，并针对速度和存储要求进行了优化

• DOI: 10.1101/2020.02.13.947218
• 发表时间: 2020
• 作者: Bhamber R

Expanding the Use of Spectral Libraries in Proteomics.

• DOI: 10.1021/acs.jproteome.8b00485
• 发表时间: 2018-12-07
• 期刊: Journal of proteome research
• 影响因子: 4.4
• 作者: Deutsch EW;Perez-Riverol Y;Chalkley RJ;Wilhelm M;Tate S;Sachsenberg T;Walzer M;Käll L;Delanghe B;Böcker S;Schymanski EL;Wilmes P;Dorfer V;Kuster B;Volders PJ;Jehmlich N;Vissers JPC;Wolan DW;Wang AY;Mendoza L;Shofstahl J;Dowsey AW;Griss J;Salek RM;Neumann S;Binz PA;Lam H;Vizcaíno JA;Bandeira N;Röst H
• 通讯作者: Röst H