In silico mass spectrometry for biologists: Tools and resources for next-generation proteomics

蛋白质是细胞中的关键功能分子，执行多种生物学任务。这包括催化反应，为细胞成分提供结构，不同细胞之间的信号传递，以及作为转录因子调节其他基因的产生。最近基因组测序的出现，将我们研究这些分子的能力转变为一门“大数据”学科，再加上质谱学(MS)和相关计算技术的进步。“组学”的这一特定分支被称为蛋白质组学--对给定生物样本中可以检测到的所有蛋白质进行高通量研究(鉴定和重要的是量化)。例如，通过发现在不同生命周期阶段(发育或衰老期间)更丰富的蛋白质，可能给我们提供关于哪些生物途径控制这些过程的线索。蛋白质组学被直接应用于生物和生物医学研究，用于描述各种系统，如植物、模式生物、传染病/微生物、人类和动物的慢性疾病等。目前，蛋白质组学中使用的主要技术是MS，在给定的MS运行(一个给定的实验)中的每一次化验(或扫描)都通过使用特定的酶(例如胰酶)研究从样品中产生的多肽来为我们提供有关样品中存在哪些蛋白质的信息。在质谱仪中，每个肽都被分解，仪器在所谓的质谱图中报告不同碎片的质量。在当今最传统和最广泛使用的蛋白质组学方法中，被称为数据依赖获取(DDA)技术的仪器只测量最丰富的多肽，而许多剩余的多肽根本没有被检测和/或测量。这就留下了一种可能性，即宝贵的生物信息被简单地遗漏了，而这些信息是关于细胞中蛋白质的相对水平的。最近，一组新的蛋白质组学方法开始被使用，它们可以克服DDA方法的一些局限性，被称为数据独立获取(DIA)方法。令人兴奋的是，这些方法在实验中捕捉到了接近完整的蛋白质组数字记录，但需要更复杂的软件工具来挖掘这些DIA地图。相对较少的群体是其使用方面的专家，限制了社区分析日益增长的DIA数据集的潜力。此外，目前的软件工具还不够强大，也不能在普通生物学家可以使用的用户友好的基于网络的平台上使用。在这个项目中，我们将开发和构建能够以稳健的方式分析使用这些新的DIA蛋白质组学方法生成的蛋白质组学数据集的开放软件，以便它们可以在未来被社区中的任何人使用。这将通过在欧洲生物信息学研究所的“云”IT基础设施上提供该软件来实现。当项目完成时，生成的软件管道将准备好部署在英国和国际上的其他类似基础设施中。我们还将利用已在公共领域获得的蛋白质组学数据，通过扩展现有的称为谱库的质谱库集合，来改进和完善当前的分析方法。这将支持针对用户基础的丰富的(重新)分析方法组合，具有即插即用组件，其中还包括对所谓的翻译后修改(PTM)的检测，以其他方式难以识别而出了名的。该项目的成果将极大地惠及对蛋白质组技术询问样本感兴趣的广泛的生物和生物医学研究人员-即使他们无法获得质谱仪。我们将确保通过举办讲习班、培训和在线帮助/教程来传播这一点。

Proteins are the key functional molecules in cells, performing multiple biological tasks. This includes catalysing reactions, providing structure to cellular components, signalling between different cells and regulating the production of other genes as transcription factors. The recent advent of genome sequencing has transformed our ability to study these molecules into a "Big Data" discipline, coupled to advances in mass spectrometry (MS) and allied computing techniques. This particular branch of the "'omics" is referred to as proteomics - the high-throughput study (identification and importantly, quantification) of all the proteins that can be detected in a given biological sample. For example, by discovery of the proteins that are more abundant in different life cycle stages (during development or during ageing) ,may give us clues as to which biological pathways control these processes. Proteomics is used right across biological and biomedical research for profiling systems as varied as plants, model organisms, infectious diseases/microbes, chronic disease of humans and animals, among many others. Currently, the primary technology used in proteomics is MS. Each assay (or scan) in a given MS run (one given experiment) provides us information about which proteins are present in our samples, by studying the peptides generated from them using a defined enzyme (e.g. trypsin). In the mass spectrometer, each peptide is broken up, and the instrument reports the masses of the different fragments in so called mass spectra. In the most traditional and most widely-used proteomics approaches nowadays, called 'data dependent acquisition' (DDA) techniques, only the most abundant peptides are measured by the instrument, and a lot of the remaining peptides are simply not detected and/or measured. This leaves the possibility that invaluable biological information is simply missed, which informs on the relative level of proteins in the cell. Recently, a novel group of proteomic approaches are starting to be used which can overcome some of the limitations of DDA approaches, known as Data Independent Acquisition (DIA) methods. Excitingly, these methods capture a near-complete digital record of the proteome in that experiment, but require more sophisticated software tools to mine these DIA maps. Relatively few groups are expert in their use, limiting the potential of the community to analyse the growing numbers of DIA data sets. Additionally, the current software tools are not yet robust enough, nor available on user-friendly web-based platforms that the average biologist can use. In this project, we will develop and build open software able to analyse proteomics datasets generated using these novel DIA proteomics approaches in a robust manner, so they can be used in the future by anyone in the community. This will be achieved by making the software available on the European Bioinformatics Institute's "cloud" IT infrastructure. When the project finishes, the generated software pipelines will be ready to be deployed in other similar infrastructures in the UK and internationally. We will also improve and refine current analysis methods by using proteomics data already made available in the public domain, by extending existing collections of mass spectra called spectral libraries. This will support a rich portfolio of (re)analysis methods for the user base, with 'plug and play' components, that also includes support for detection of so called post-translational modifications (PTMs), which are notoriously difficult to identify otherwise. The project outputs will greatly benefit a wide-range of biological and biomedical researchers interested in proteomic techniques for interrogation of samples - even if they don't have access to mass spectrometers. We will ensure this is disseminated via delivering workshops, training and online help/tutorials.