3D-Proteomics: FAIRification of proteomics data for comprehensive integration with structural biology information

3D-蛋白质组学：蛋白质组学数据的公平化，以与结构生物学信息全面整合

• 批准号: BB/V018779/1
• 负责人: Juan Antonio Vizcaino
• 金额: $ 89.39万
• 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: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FV018779%2F1
• 关键词: 3D; Proteomics; FAIRification; proteomics; data

Proteins are molecules found in all living organisms that provide structure and carry out most of the important functions in a cell, including catalysing (causing or speeding up) chemical reactions and signalling between different cells. Proteomics is the study of the entire set of proteins in a given biological sample such as a cell or an organism like a bacteria, plant or human. Since proteins are essential for so many crucial functions, proteomics can tell us a lot about how organisms work and also about what happens in illnesses, as well as helping to identify potential treatments. This means that proteomics is used across many areas of beneficial biological and biomedical research.Currently the primary technology used in proteomics is a technique called mass spectrometry (MS), which works by breaking up a protein into small fragments, sorting them and then reporting their mass. The quantity and identity of the protein can then be determined using different software tools. The structure of a protein is also very important, as the way that a protein is organised via folding will help it to carry out its job. The structure also determines how it is able to interact with other proteins, for example a protein that transports another protein around a cell needs to have a part that binds to it specifically. Protein structure can be studied using techniques like x-ray crystallography, which makes use of the way that different structures diffract (bend) x-rays. A more recent development called cross-linking MS (CL-MS) is a powerful tool for visualising how proteins fold and join together, and it works by running MS on proteins that are linked by specialised chemical reagents called cross-linkers. Unfortunately, CL-MS does not yet have coordinated mature open standards and existing datasets are not well linked to other information about protein structure. This means that it is difficult to compare and integrate findings between research groups and that important knowledge may be missed.It is important that proteomics databases follow the FAIR principles of being easy to find (Findable), free and open source (Accessible), easily shared and processed (Interoperable) and Reusable. Our research groups manage two world-leading databases: the PRoteomics IDEntifications database (PRIDE), which is a repository for proteomics data generated using MS, and the Protein Data Bank (PDB), which is home to 3D structural data for large molecules including proteins. This project will combine these tools with our expertise in CL-MS in order to develop FAIR data standards and software so that proteomics data generated using CL-MS has a common format and processing pipeline, and so that a suite of software tools is made available in order to process and analyse the data freely and easily. PRIDE will be extended to include these standardised CL-MS data formats, and key software tools for data deposition and visualisation will be made available. As a key point, we will create links between PRIDE and PDB in order to allow for joined-up examination of structural data, including integration between the PDB and PRIDE submission systems. This will mean that researchers will be able to more easily analyse proteins and identify links between their research and other projects, even if they don't have access to CL-MS equipment themselves.The tools and standards that will be generated by this project will benefit researchers across a wide range of biological and biomedical fields, and will provide an interface between proteomics and structural biology information that will enhance and connect research findings. The software will ensure that important and novel structural proteomics data are made accessible and findable, and the standards will maintain its interoperability and reusability. We will make sure that our work is disseminated widely and we will deliver workshops to train and assist researchers in making full use of these valuable resources.

蛋白质是在所有生物体中发现的分子，提供结构并在细胞中执行大多数重要功能，包括催化（引起或加速）不同细胞之间的化学反应和信号传导。蛋白质组学是研究给定生物样品中的整套蛋白质，例如细胞或生物体，如细菌，植物或人类。由于蛋白质对许多关键功能至关重要，蛋白质组学可以告诉我们很多关于生物体如何工作以及疾病发生的情况，以及帮助确定潜在的治疗方法。这意味着蛋白质组学被用于许多有益的生物和生物医学研究领域。目前蛋白质组学中使用的主要技术是一种称为质谱法（MS）的技术，其工作原理是将蛋白质分解成小片段，对其进行分类，然后报告其质量。然后可以使用不同的软件工具确定蛋白质的数量和身份。蛋白质的结构也非常重要，因为蛋白质通过折叠组织的方式将有助于它执行其工作。这种结构还决定了它如何与其他蛋白质相互作用，例如，在细胞周围运输另一种蛋白质的蛋白质需要有一个与之特异性结合的部分。蛋白质的结构可以用X射线晶体学这样的技术来研究，这种技术利用了不同的结构对X射线的反射（弯曲）。最近的发展称为交联MS（CL-MS）是一种强大的工具，用于可视化蛋白质如何折叠和连接在一起，它通过在由称为交联剂的专门化学试剂连接的蛋白质上运行MS来工作。不幸的是，CL-MS还没有协调成熟的开放标准，现有的数据集与蛋白质结构的其他信息没有很好的联系。这意味着很难比较和整合研究小组之间的发现，重要的知识可能会被遗漏。重要的是，蛋白质组学数据库遵循FAIR原则，即易于查找（Findable），免费和开源（可扩展），易于共享和处理（互操作）和可重用。我们的研究小组管理着两个世界领先的数据库：PRoteomics IDEntifications数据库（PRIDE），这是使用MS生成的蛋白质组学数据的存储库，以及蛋白质数据库（PDB），这是包括蛋白质在内的大分子的3D结构数据的所在地。该项目将联合收割机与我们在CL-MS方面的专业知识相结合，以开发FAIR数据标准和软件，使使用CL-MS生成的蛋白质组学数据具有通用格式和处理管道，并提供一套软件工具，以自由轻松地处理和分析数据。PRIDE将扩展到包括这些标准化的CL-MS数据格式，并将提供用于数据沉积和可视化的关键软件工具。作为一个关键点，我们将在PRIDE和PDB之间创建链接，以便对结构数据进行联合检查，包括PDB和PRIDE提交系统之间的集成。这将意味着研究人员将能够更容易地分析蛋白质，并确定他们的研究和其他项目之间的联系，即使他们自己没有使用CL-MS设备。该项目将产生的工具和标准将使广泛的生物和生物医学领域的研究人员受益，并将提供蛋白质组学和结构生物学信息之间的接口，以增强和联系研究结果。该软件将确保重要和新颖的结构蛋白质组学数据可以访问和查找，标准将保持其互操作性和可重用性。我们将确保我们的工作得到广泛传播，我们将举办讲习班，培训和协助研究人员充分利用这些宝贵的资源。

项目成果

PDBe and PDBe-KB: Providing high-quality, up-to-date and integrated resources of macromolecular structures to support basic and applied research and education.

• DOI: 10.1002/pro.4439
• 发表时间: 2022-10
• 期刊: PROTEIN SCIENCE
• 影响因子: 8
• 作者: Varadi, Mihaly;Anyango, Stephen;Appasamy, Sri Devan;Armstrong, David;Bage, Marcus;Berrisford, John;Choudhary, Preeti;Bertoni, Damian;Deshpande, Mandar;Leines, Grisell Diaz;Ellaway, Joseph;Evans, Genevieve;Gaborova, Romana;Gupta, Deepti;Gutmanas, Aleksandras;Harrus, Deborah;Kleywegt, Gerard J.;Bueno, Weslley Morellato;Nadzirin, Nurul;Nair, Sreenath;Pravda, Lukas;Afonso, Marcelo Querino Lima;Sehnal, David;Tanweer, Ahsan;Tolchard, James;Abrams, Charlotte;Dunlop, Roisin;Velankar, Sameer
• 通讯作者: Velankar, Sameer

Proteomics Standards Initiative at Twenty Years: Current Activities and Future Work.

二十年来的蛋白质组学标准倡议：当前的活动和未来工作。

• DOI: 10.1021/acs.jproteome.2c00637
• 发表时间: 2023-02-03
• 期刊: JOURNAL OF PROTEOME RESEARCH
• 影响因子: 4.4
• 作者: Deutsch, Eric W.;Vizcaino, Juan Antonio;Jones, Andrew R.;Binz, Pierre-Alain;Lam, Henry;Klein, Joshua;Bittremieux, Wout;Perez-Riverol, Yasset;Tabb, David L.;Walzer, Mathias;Ricard-Blum, Sylvie;Hermjakob, Henning;Neumann, Steffen;Mak, Tytus D.;Kawano, Shin;Mendoza, Luis;Van Den Bossche, Tim;Gabriels, Ralf;Bandeira, Nuno;Carver, Jeremy;Pullman, Benjamin;Sun, Zhi;Hoffmann, Nils;Shofstahl, Jim;Zhu, Yunping;Licata, Luana;Quaglia, Federica;Tosatto, Silvio C. E.;Orchard, Sandra E.
• 通讯作者: Orchard, Sandra E.

ProteomicsML: An Online Platform for Community-Curated Data sets and Tutorials for Machine Learning in Proteomics.

• DOI: 10.1021/acs.jproteome.2c00629
• 发表时间: 2023-02-03
• 期刊: JOURNAL OF PROTEOME RESEARCH
• 影响因子: 4.4
• 作者: Rehfeldt, Tobias G.;Gabriels, Ralf;Bouwmeester, Robbin;Gessulat, Siegfried;Neely, Benjamin A.;Palmblad, Magnus;Perez-Riverol, Yasset;Schmidt, Tobias;Vizcaino, Juan Antonio;Deutsch, Eric W.
• 通讯作者: Deutsch, Eric W.