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，执行多个生物学任务。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导以及调节其他基因作为转录因子的产生。基因组测序的最新出现使我们将这些分子研究成“大数据”学科的能力与质谱（MS）和相关计算技术的进步结合在一起。 “'omics”的这个特殊分支称为蛋白质组学 - 在给定生物样品中可以检测到的所有蛋白质的高通量研究（识别和重要的定量）。例如，通过发现在不同的生命周期阶段（在发育期间或衰老期间）中发现更丰富的蛋白质，可以为我们提供有关哪种生物途径控制这些过程的线索。蛋白质组学仅在生物和生物医学研究中使用，用于分析系统，例如植物，模型生物，传染病/微生物，人类和动物的慢性病等。 当前，蛋白质组学中使用的主要技术是MS。给定MS运行中的每种测定（或扫描）（一个给定的实验）通过使用定义的酶（例如胰蛋白酶）研究从它们产生的肽中，为我们提供了有关样品中存在哪些蛋白质的信息。在质谱仪中，每个肽都会分解，并且仪器报告了所谓的质谱中不同片段的质量。如今，在最传统和最广泛使用的蛋白质组学方法中，称为“数据依赖性获取”（DDA）技术，仅通过仪器测量最丰富的肽，并且许多其余的肽均未检测到和/或测量。这留下了简单地遗漏了宝贵的生物学信息的可能性，这可以告知细胞中蛋白质的相对水平。最近，开始使用一组新型的蛋白质组学方法，可以克服DDA方法的某些局限性，即被称为数据独立获取（DIA）方法。令人兴奋的是，这些方法在该实验中捕获了蛋白质组几乎完整的数字记录，但是需要更复杂的软件工具来开采这些DIA映射。相对较少的小组是他们使用的专家，限制了社区分析日益增长的DIA数据集的潜力。此外，当前的软件工具还不够强大，也无法使用普通生物学家可以使用的用户友好的基于网络的平台。在这个项目中，我们将开发和构建开放软件，能够以鲁棒的方式分析使用这些新型DIA蛋白质组学方法生成的蛋白质组学数据集，因此将来可以在社区中的任何人使用它们。这将通过使该软件在欧洲生物信息学研究所的“云” IT基础架构上提供来实现。当项目完成后，生成的软件管道将准备好被部署在英国和国际上的其他类似基础架构中。我们还将通过使用公共领域中已经提供的蛋白质组学数据来改进和完善当前分析方法，并扩展现有的称为光谱库的质谱集合。这将支持用户群的（重新）分析方法的丰富组合，并具有“插件”组件，其中还包括支持检测所谓的翻译后修改（PTMS）的支持，众所周知，这是难以识别的。该项目的输出将极大地使对蛋白质组学技术感兴趣的生物学和生物医学研究人员进行质疑，即使他们无法访问质谱仪，也将有益于蛋白质组学技术。我们将通过提供研讨会，培训和在线帮助/教程来确保这将被传播。

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

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