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.

蛋白质是存在于所有生物体中的分子，它们提供结构并执行细胞中的大多数重要功能，包括催化(引起或加速)不同细胞之间的化学反应和信号传递。蛋白质组学是对给定生物样本(如细胞或细菌、植物或人类等有机体)中的一整套蛋白质的研究。由于蛋白质对如此多的关键功能至关重要，蛋白质组学可以告诉我们许多关于生物体如何工作的信息，也可以告诉我们疾病中发生了什么，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被称为质谱学(MS)的技术，其工作原理是将蛋白质分解成小片段，对它们进行分类，然后报告它们的质量。然后，可以使用不同的软件工具来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的组织方式将有助于它完成其工作。这种结构还决定了它如何能够与其他蛋白质相互作用，例如，一种在细胞周围运输另一种蛋白质的蛋白质需要有一个特定结合它的部分。蛋白质结构可以用像X射线结晶学这样的技术来研究，这种技术利用了不同结构对X射线的衍射(弯曲)方式。最近的一项开发称为交叉连接MS(CL-MS)，它是一个强大的工具，用于可视化蛋白质如何折叠和连接在一起，它的工作原理是对通过称为交联剂的特殊化学试剂连接的蛋白质运行MS。遗憾的是，CL-MS还没有协调成熟的开放标准，现有的数据集也没有很好地与其他关于蛋白质结构的信息联系起来。这意味着很难在研究小组之间比较和整合研究结果，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易于查找(可找到)、免费和开源(可访问)、易于共享和处理(可互操作)和可重复使用的公平原则。我们的研究小组管理着两个世界领先的数据库：蛋白质组学识别数据库(PROID)和蛋白质数据库(PDB)，前者是使用MS生成的蛋白质组学数据的储存库，后者是包括蛋白质在内的大分子的3D结构数据的所在地。该项目将把这些工具与我们在CL-MS方面的专业知识结合起来，以开发公平的数据标准和软件，以便使用CL-MS生成的蛋白质组学数据具有通用的格式和处理流程，并提供一套软件工具，以便自由和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，并将提供用于数据存储和可视化的关键软件工具。作为重点，我们将在PARE和PDB之间建立联系，以便能够对结构数据进行联合审查，包括在PDB和PARE提交系统之间进行整合。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与其他项目之间的联系，即使他们自己无法使用CL-MS设备。该项目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，从而加强和联系研究成果。该软件将确保重要和新颖的结构蛋白质组数据可供访问和查找，这些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，并将举办讲习班，培训和协助研究人员充分利用这些宝贵资源。

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

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.

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.