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

生物学家的计算机质谱分析：下一代蛋白质组学的工具和资源

• 批准号: BB/P024599/1
• 负责人: Juan Antonio Vizcaino
• 金额: $ 56.67万
• 依托单位: European Bioinformatics Institute
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FP024599%2F1
• 关键词: silico; mass; spectrometry; biologists; Tools

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.

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，在不同细胞之间发出信号，以及调节作为转录因子的其他基因的产生。最近基因组测序的出现已经将我们研究这些分子的能力转变为“大数据”学科，再加上质谱（MS）和相关计算技术的进步。“组学”的这一特殊分支被称为蛋白质组学--对给定生物样品中可检测到的所有蛋白质进行高通量研究（鉴定，重要的是定量）。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，可以为我们提供关于哪些生物途径控制这些过程的线索。蛋白质组学用于生物和生物医学研究，用于分析植物，模式生物，传染病/微生物，人类和动物的慢性疾病等各种系统。 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）中的每个测定（或扫描）通过使用定义的酶（例如胰蛋白酶）研究由它们产生的肽，为我们提供了关于样品中存在哪些蛋白质的信息。在质谱仪中，每个肽被分解，仪器在所谓的质谱中报告不同片段的质量。在当今最传统和最广泛使用的蛋白质组学方法中，称为“数据依赖采集”（DDA）技术，只有最丰富的肽被仪器测量，并且许多剩余的肽根本没有被检测和/或测量。这就有可能错过宝贵的生物信息，而这些信息可以告诉细胞中蛋白质的相对水平。最近，一组新的蛋白质组学方法开始被使用，它可以克服DDA方法的一些限制，称为数据独立采集（DIA）方法。令人兴奋的是，这些方法捕获了实验中蛋白质组的近乎完整的数字记录，但需要更复杂的软件工具来挖掘这些DIA图谱。相对而言，只有少数团体是使用这些数据集的专家，这限制了社区分析日益增多的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，我们将开发和构建开放的软件，能够以强大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，因此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基础设施上提供软件来实现。项目完成后，生成的软件管道将准备部署在英国和国际上的其他类似基础设施中。我们还将通过使用已经在公共领域提供的蛋白质组学数据，通过扩展称为光谱库的现有质谱集合来改进和完善当前的分析方法。这将为用户群提供丰富的（再）分析方法组合，具有“即插即用”组件，还包括支持检测所谓的翻译后修饰（PTM），这是众所周知的难以识别。该项目的成果将极大地有利于对蛋白质组学技术感兴趣的生物和生物医学研究人员进行样品询问-即使他们无法使用质谱仪。我们将确保通过提供研讨会、培训和在线帮助/教程传播这一信息。

项目成果

Data Management of Sensitive Human Proteomics Data: Current Practices, Recommendations, and Perspectives for the Future.

• DOI: 10.1016/j.mcpro.2021.100071
• 发表时间: 2021
• 期刊: Molecular & cellular proteomics : MCP
• 作者: Bandeira N;Deutsch EW;Kohlbacher O;Martens L;Vizcaíno JA
• 通讯作者: Vizcaíno JA

Is DIA proteomics data FAIR? Current data sharing practices, available bioinformatics infrastructure and recommendations for the future.

• DOI: 10.1002/pmic.202200014
• 发表时间: 2023-04
• 期刊: Proteomics
• 影响因子: 3.4

Expression Atlas update: gene and protein expression in multiple species.

• DOI: 10.1093/nar/gkab1030
• 发表时间: 2022-01-07
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Moreno P;Fexova S;George N;Manning JR;Miao Z;Mohammed S;Muñoz-Pomer A;Fullgrabe A;Bi Y;Bush N;Iqbal H;Kumbham U;Solovyev A;Zhao L;Prakash A;García-Seisdedos D;Kundu DJ;Wang S;Walzer M;Clarke L;Osumi-Sutherland D;Tello-Ruiz MK;Kumari S;Ware D;Eliasova J;Arends MJ;Nawijn MC;Meyer K;Burdett T;Marioni J;Teichmann S;Vizcaíno JA;Brazma A;Papatheodorou I
• 通讯作者: Papatheodorou I

The PRIDE database and related tools and resources in 2019: improving support for quantification data.

• DOI: 10.1093/nar/gky1106
• 发表时间: 2019-01-08
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Perez-Riverol Y;Csordas A;Bai J;Bernal-Llinares M;Hewapathirana S;Kundu DJ;Inuganti A;Griss J;Mayer G;Eisenacher M;Pérez E;Uszkoreit J;Pfeuffer J;Sachsenberg T;Yilmaz S;Tiwary S;Cox J;Audain E;Walzer M;Jarnuczak AF;Ternent T;Brazma A;Vizcaíno JA
• 通讯作者: Vizcaíno JA

Future Prospects of Spectral Clustering Approaches in Proteomics.

• DOI: 10.1002/pmic.201700454
• 发表时间: 2018-07
• 期刊: Proteomics
• 影响因子: 3.4
• 作者: Perez-Riverol Y;Vizcaíno JA;Griss J
• 通讯作者: Griss J