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A deep mutational scanning platform to characterize the fitness landscape of anti-CRISPR proteins. 深度突变扫描平台,用于描述抗 CRISPR 蛋白的适应性景观。
IF 16.6 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-11-18 DOI: 10.1093/nar/gkae1052
Tobias Stadelmann, Daniel Heid, Michael Jendrusch, Jan Mathony, Sabine Aschenbrenner, Stéphane Rosset, Bruno E Correia, Dominik Niopek

Deep mutational scanning is a powerful method for exploring the mutational fitness landscape of proteins. Its adaptation to anti-CRISPR proteins, which are natural CRISPR-Cas inhibitors and key players in the co-evolution of microbes and phages, facilitates their characterization and optimization. Here, we developed a robust anti-CRISPR deep mutational scanning pipeline in Escherichia coli that combines synthetic gene circuits based on CRISPR interference with flow cytometry coupled sequencing and mathematical modeling. Using this pipeline, we characterized comprehensive single point mutation libraries for AcrIIA4 and AcrIIA5, two potent inhibitors of CRISPR-Cas9. The resulting mutational fitness landscapes revealed considerable mutational tolerance for both Acrs, suggesting an intrinsic redundancy with respect to Cas9 inhibitory features, and - for AcrIIA5 - indicated mutations that boost Cas9 inhibition. Subsequent in vitro characterization suggested that the observed differences in inhibitory potency between mutant inhibitors were mostly due to changes in binding affinity rather than protein expression levels. Finally, to demonstrate that our pipeline can inform Acrs-based genome editing applications, we employed a selected subset of mutant inhibitors to increase CRISPR-Cas9 target specificity by modulating Cas9 activity. Taken together, our work establishes deep mutational scanning as a powerful method for anti-CRISPR protein characterization and optimization.

深度突变扫描是一种探索蛋白质突变适应性景观的强大方法。抗CRISPR蛋白是天然的CRISPR-Cas抑制剂,也是微生物和噬菌体共同进化过程中的关键角色,它与抗CRISPR蛋白的适配促进了抗CRISPR蛋白的表征和优化。在这里,我们在大肠杆菌中开发了一种强大的抗 CRISPR 深度突变扫描管道,它将基于 CRISPR 干扰的合成基因电路与流式细胞仪耦合测序和数学建模相结合。利用这一方法,我们鉴定了 AcrIIA4 和 AcrIIA5 这两种 CRISPR-Cas9 强效抑制剂的综合单点突变库。由此产生的突变适应性图谱揭示了这两种 Acrs 相当大的突变耐受性,表明它们在 Cas9 抑制特征方面存在内在冗余,而 AcrIIA5 则表明突变能增强 Cas9 的抑制作用。随后的体外表征表明,在突变抑制剂之间观察到的抑制效力差异主要是由于结合亲和力的变化而不是蛋白质表达水平的变化。最后,为了证明我们的方法可以为基于 Acrs 的基因组编辑应用提供信息,我们使用了一组精选的突变抑制剂子集,通过调节 Cas9 的活性来提高 CRISPR-Cas9 的靶向特异性。总之,我们的工作确立了深度突变扫描作为抗CRISPR蛋白表征和优化的一种强大方法的地位。
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引用次数: 0
NASA open science data repository: open science for life in space. 美国国家航空航天局(NASA)开放科学数据储存库:太空生命开放科学。
IF 16.6 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-11-18 DOI: 10.1093/nar/gkae1116
Samrawit G Gebre, Ryan T Scott, Amanda M Saravia-Butler, Danielle K Lopez, Lauren M Sanders, Sylvain V Costes

Space biology and health data are critical for the success of deep space missions and sustainable human presence off-world. At the core of effectively managing biomedical risks is the commitment to open science principles, which ensure that data are findable, accessible, interoperable, reusable, reproducible and maximally open. The 2021 integration of the Ames Life Sciences Data Archive with GeneLab to establish the NASA Open Science Data Repository significantly enhanced access to a wide range of life sciences, biomedical-clinical and mission telemetry data alongside existing 'omics data from GeneLab. This paper describes the new database, its architecture and new data streams supporting diverse data types and enhancing data submission, retrieval and analysis. Features include the biological data management environment for improved data submission, a new user interface, controlled data access, an enhanced API and comprehensive public visualization tools for environmental telemetry, radiation dosimetry data and 'omics analyses. By fostering global collaboration through its analysis working groups and training programs, the open science data repository promotes widespread engagement in space biology, ensuring transparency and inclusivity in research. It supports the global scientific community in advancing our understanding of spaceflight's impact on biological systems, ensuring humans will thrive in future deep space missions.

空间生物学和健康数据对于深空任务的成功和人类在地球外的可持续存在至关重要。有效管理生物医学风险的核心是致力于开放科学原则,确保数据可查找、可访问、可互操作、可重复使用、可复制和最大限度地开放。2021 年,艾姆斯生命科学数据档案馆与 GeneLab 整合,建立了 NASA 开放科学数据存储库,大大提高了对来自 GeneLab 的现有 "omics "数据以及各种生命科学、生物医学-临床和任务遥测数据的访问。本文介绍了新的数据库、其架构和新的数据流,支持多种数据类型,并加强了数据提交、检索和分析。其特点包括用于改进数据提交的生物数据管理环境、新的用户界面、受控数据访问、增强型应用程序接口以及用于环境遥感测量、辐射剂量测定数据和'omics'分析的综合公共可视化工具。通过其分析工作组和培训计划促进全球合作,开放科学数据储存库推动了空间生物学的广泛参与,确保了研究的透明度和包容性。它支持全球科学界进一步了解太空飞行对生物系统的影响,确保人类在未来的深空任务中茁壮成长。
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引用次数: 0
The European Nucleotide Archive in 2024. 2024 年欧洲核苷酸档案。
IF 16.6 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-11-18 DOI: 10.1093/nar/gkae975
Colman O'Cathail, Alisha Ahamed, Josephine Burgin, Carla Cummins, Rajkumar Devaraj, Khadim Gueye, Dipayan Gupta, Vikas Gupta, Muhammad Haseeb, Maira Ihsan, Eugene Ivanov, Suran Jayathilaka, Vishnukumar Kadhirvelu, Manish Kumar, Ankur Lathi, Rasko Leinonen, Jasmine McKinnon, Lili Meszaros, Joana Pauperio, Stephane Pesant, Nadim Rahman, Gabriele Rinck, Sandeep Selvakumar, Swati Suman, Yanisa Sunthornyotin, Marianna Ventouratou, Zahra Waheed, Peter Woollard, David Yuan, Ahmad Zyoud, Tony Burdett, Guy Cochrane

The European Nucleotide Archive (ENA, https://www.ebi.ac.uk/ena), maintained at the European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) provides freely accessible services, both for deposition of, and access to, open nucleotide sequencing data. Open scientific data are of paramount importance to the scientific community and contribute daily to the acceleration of scientific advance. Outlined here are changes to and updates on the ENA service in 2024, aligning with the broad goals of enhancing interoperability, globalisation of the service and scaling the platform to meet current and future needs.

欧洲核苷酸档案(ENA,https://www.ebi.ac.uk/ena)由欧洲分子生物学实验室的欧洲生物信息学研究所(EMBL-EBI)负责维护,为开放核苷酸测序数据的存放和访问提供免费访问服务。开放科学数据对科学界至关重要,每天都在促进科学进步。本文概述了 2024 年 ENA 服务的变化和更新,这些变化和更新与增强互操作性、服务全球化以及扩展平台以满足当前和未来需求的广泛目标相一致。
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引用次数: 0
Intrinsically disordered RNA-binding motifs cooperate to catalyze RNA folding and drive phase separation. 本质上无序的 RNA 结合图案合作催化 RNA 折叠并驱动相分离。
IF 16.6 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-11-18 DOI: 10.1093/nar/gkae1107
Annika Niedner-Boblenz, Thomas Monecke, Janosch Hennig, Melina Klostermann, Mario Hofweber, Elena Davydova, André P Gerber, Irina Anosova, Wieland Mayer, Marisa Müller, Roland Gerhard Heym, Robert Janowski, Jean-Christophe Paillart, Dorothee Dormann, Kathi Zarnack, Michael Sattler, Dierk Niessing

RNA-binding proteins are essential for gene regulation and the spatial organization of cells. Here, we report that the yeast ribosome biogenesis factor Loc1p is an intrinsically disordered RNA-binding protein with eight repeating positively charged, unstructured nucleic acid binding (PUN) motifs. While a single of these previously undefined motifs stabilizes folded RNAs, multiple copies strongly cooperate to catalyze RNA folding. In the presence of RNA, these multivalent PUN motifs drive phase separation. Proteome-wide searches in pro- and eukaryotes for proteins with similar arrays of PUN motifs reveal a strong enrichment in RNA-mediated processes and DNA remodeling. Thus, PUN motifs are potentially involved in a large variety of RNA- and DNA-related processes by concentrating them in membraneless organelles. The general function and wide distribution of PUN motifs across species suggest that in an ancient 'RNA world' PUN-like motifs may have supported the correct folding of early ribozymes.

RNA 结合蛋白对基因调控和细胞的空间组织至关重要。在这里,我们报告了酵母核糖体生物发生因子 Loc1p 是一种内在无序的 RNA 结合蛋白,具有八个重复的带正电的非结构化核酸结合(PUN)基序。虽然这些以前未定义的基团中的一个能稳定折叠的 RNA,但多个副本能强力合作催化 RNA 折叠。在 RNA 存在的情况下,这些多价 PUN 基团会推动相分离。在原核生物和真核生物的蛋白质组范围内搜索具有类似 PUN 基序阵列的蛋白质,发现它们在 RNA 介导的过程和 DNA 重塑中具有很强的富集性。因此,PUN基序可能通过将它们集中在无膜细胞器中,参与了大量与RNA和DNA相关的过程。PUN基序在不同物种中的普遍功能和广泛分布表明,在古老的 "RNA世界 "中,类似PUN基序的基序可能支持了早期核酶的正确折叠。
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引用次数: 0
Complex portal 2025: predicted human complexes and enhanced visualisation tools for the comparison of orthologous and paralogous complexes. 复合物门户网站 2025:预测的人类复合物以及用于比较同源和旁系复合物的增强型可视化工具。
IF 16.6 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-11-18 DOI: 10.1093/nar/gkae1085
Sucharitha Balu, Susie Huget, Juan Jose Medina Reyes, Eliot Ragueneau, Kalpana Panneerselvam, Samantha N Fischer, Erin R Claussen, Savvas Kourtis, Colin W Combe, Birgit H M Meldal, Livia Perfetto, Juri Rappsilber, Georg Kustatscher, Kevin Drew, Sandra Orchard, Henning Hermjakob

The Complex Portal (www.ebi.ac.uk/complexportal) is a manually curated reference database for molecular complexes. It is a unifying web resource linking aggregated data on composition, topology and the function of macromolecular complexes from 28 species. In addition to significantly extending the number of manually curated complexes, we have massively extended the coverage of the human complexome through the incorporation of high confidence assemblies predicted by machine-learning algorithms trained on large-scale experimental data. The current content of the portal comprising 2150 human complexes has been augmented by 14 964 machine-learning (ML) predicted complexes from hu.MAP3.0. We have refactored the website to enable easy search and filtering of these different classes of protein complexes and have implemented the Complex Navigator, a visualisation tool to facilitate comparison of related complexes in the context of orthology or paralogy. We have embedded the Rhea reaction visualisation tool into the website to enable users to view the catalytic activity of enzyme complexes.

复合体门户网站(www.ebi.ac.uk/complexportal)是一个人工编辑的分子复合体参考数据库。它是一个统一的网络资源,链接了 28 个物种的大分子复合物的组成、拓扑结构和功能的汇总数据。除了大幅增加人工整理的复合物数量外,我们还通过纳入根据大规模实验数据训练的机器学习算法预测的高置信度组合,大规模扩展了人类复合物组的覆盖范围。hu.MAP3.0 中的 14 964 个机器学习(ML)预测的复合体增加了门户网站目前包含的 2150 个人类复合体的内容。我们对网站进行了重构,以方便搜索和过滤这些不同类别的蛋白质复合物,并实施了 "复合物导航器"(Complex Navigator),这是一种可视化工具,便于在选系或旁系的背景下比较相关复合物。我们将雷亚反应可视化工具嵌入网站,使用户能够查看酶复合物的催化活性。
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引用次数: 0
Correction to 'Regulation of the androgen receptor by SET9-mediated methylation'. SET9 介导的甲基化对雄激素受体的调控 "的更正。
IF 16.6 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-11-18 DOI: 10.1093/nar/gkae1166
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引用次数: 0
PubChem 2025 update. PubChem 2025 更新。
IF 16.6 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-11-18 DOI: 10.1093/nar/gkae1059
Sunghwan Kim, Jie Chen, Tiejun Cheng, Asta Gindulyte, Jia He, Siqian He, Qingliang Li, Benjamin A Shoemaker, Paul A Thiessen, Bo Yu, Leonid Zaslavsky, Jian Zhang, Evan E Bolton

PubChem (https://pubchem.ncbi.nlm.nih.gov) is a large and highly-integrated public chemical database resource at NIH. In the past two years, significant updates were made to PubChem. With additions from over 130 new sources, PubChem contains >1000 data sources, 119 million compounds, 322 million substances and 295 million bioactivities. New interfaces, such as the consolidated literature panel and the patent knowledge panel, were developed. The consolidated literature panel combines all references about a compound into a single list, allowing users to easily find, sort, and export all relevant articles for a chemical in one place. The patent knowledge panels for a given query chemical or gene display chemicals, genes, and diseases co-mentioned with the query in patent documents, helping users to explore relationships between co-occurring entities within patent documents. PubChemRDF was expanded to include the co-occurrence data underlying the literature knowledge panel, enabling users to exploit semantic web technologies to explore entity relationships based on the co-occurrences in the scientific literature. The usability and accessibility of information on chemicals with non-discrete structures (e.g. biologics, minerals, polymers, UVCBs and glycans) were greatly improved with dedicated web pages that provide a comprehensive view of all available information in PubChem for these chemicals.

PubChem (https://pubchem.ncbi.nlm.nih.gov) 是美国国立卫生研究院的一个大型、高度集成的公共化学数据库资源。在过去两年中,PubChem 进行了重大更新。由于新增了 130 多个数据源,PubChem 包含超过 1000 个数据源、1.19 亿种化合物、3.22 亿种物质和 2.95 亿种生物活性物质。开发了新的界面,如综合文献面板和专利知识面板。合并文献面板将有关化合物的所有参考文献合并到一个列表中,使用户可以在一个地方轻松查找、排序和导出有关化学品的所有相关文章。针对给定查询化学品或基因的专利知识面板显示了专利文献中与该查询共同提及的化学品、基因和疾病,帮助用户探索专利文献中共同出现的实体之间的关系。PubChemRDF 已扩展到包括文献知识面板基础的共现数据,使用户能够利用语义网技术,根据科学文献中的共现情况探索实体关系。有关非离散结构化学品(如生物制品、矿物、聚合物、UVCB 和聚糖)信息的可用性和可访问性得到了极大改善,专门的网页提供了 PubChem 中有关这些化学品的所有可用信息的综合视图。
{"title":"PubChem 2025 update.","authors":"Sunghwan Kim, Jie Chen, Tiejun Cheng, Asta Gindulyte, Jia He, Siqian He, Qingliang Li, Benjamin A Shoemaker, Paul A Thiessen, Bo Yu, Leonid Zaslavsky, Jian Zhang, Evan E Bolton","doi":"10.1093/nar/gkae1059","DOIUrl":"10.1093/nar/gkae1059","url":null,"abstract":"<p><p>PubChem (https://pubchem.ncbi.nlm.nih.gov) is a large and highly-integrated public chemical database resource at NIH. In the past two years, significant updates were made to PubChem. With additions from over 130 new sources, PubChem contains >1000 data sources, 119 million compounds, 322 million substances and 295 million bioactivities. New interfaces, such as the consolidated literature panel and the patent knowledge panel, were developed. The consolidated literature panel combines all references about a compound into a single list, allowing users to easily find, sort, and export all relevant articles for a chemical in one place. The patent knowledge panels for a given query chemical or gene display chemicals, genes, and diseases co-mentioned with the query in patent documents, helping users to explore relationships between co-occurring entities within patent documents. PubChemRDF was expanded to include the co-occurrence data underlying the literature knowledge panel, enabling users to exploit semantic web technologies to explore entity relationships based on the co-occurrences in the scientific literature. The usability and accessibility of information on chemicals with non-discrete structures (e.g. biologics, minerals, polymers, UVCBs and glycans) were greatly improved with dedicated web pages that provide a comprehensive view of all available information in PubChem for these chemicals.</p>","PeriodicalId":19471,"journal":{"name":"Nucleic Acids Research","volume":" ","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142668525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
The Immune Epitope Database (IEDB): 2024 update. 免疫表位数据库(IEDB):2024 年更新。
IF 16.6 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-11-18 DOI: 10.1093/nar/gkae1092
Randi Vita, Nina Blazeska, Daniel Marrama, Sebastian Duesing, Jason Bennett, Jason Greenbaum, Marcus De Almeida Mendes, Jarjapu Mahita, Daniel K Wheeler, Jason R Cantrell, James A Overton, Darren A Natale, Alessandro Sette, Bjoern Peters

Over the past 20 years, the Immune Epitope Database (IEDB, iedb.org) has established itself as the foremost resource for immune epitope data. The IEDB catalogs published epitopes and their contextual experimental data in a freely searchable public resource. The IEDB team manually curates data from the literature into a structured format and spans infectious, allergic, autoimmune, and transplant diseases. Here, we describe the enhancements made since our 2018 paper, capturing user-directed updates to the search interface, advanced data exports, increases in data quality, and improved interoperability across related resources. As we look forward to the next 20 years, we are confident in our ability to meet the needs of our users and to contribute to the broader field of data standardization.

在过去的 20 年中,免疫表位数据库(IEDB,iedb.org)已成为最重要的免疫表位数据资源。IEDB 将已发表的表位及其相关实验数据编入一个可免费检索的公共资源目录。IEDB 团队以人工方式将文献中的数据整理成结构化格式,涵盖传染病、过敏性疾病、自身免疫性疾病和移植疾病。在此,我们将介绍自2018年发表论文以来所做的改进,包括用户导向的搜索界面更新、高级数据导出、数据质量的提高以及相关资源间互操作性的改善。展望未来 20 年,我们有信心有能力满足用户需求,并为更广泛的数据标准化领域做出贡献。
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引用次数: 0
GPCRdb in 2025: adding odorant receptors, data mapper, structure similarity search and models of physiological ligand complexes. 2025 年的 GPCRdb:增加气味受体、数据映射器、结构相似性搜索和生理配体复合物模型。
IF 16.6 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-11-18 DOI: 10.1093/nar/gkae1065
Luis P Taracena Herrera, Søren N Andreassen, Jimmy Caroli, Ismael Rodríguez-Espigares, Ali A Kermani, György M Keserű, Albert J Kooistra, Gáspár Pándy-Szekeres, David E Gloriam

G protein-coupled receptors (GPCRs) are membrane-spanning transducers mediating the actions of numerous physiological ligands and drugs. The GPCR database GPCRdb supports a large global research community with reference data, analysis, visualization, experiment design and dissemination. Here, we describe our sixth major GPCRdb release starting with an overview of all resources for receptors and ligands. As a major addition, all ∼400 human odorant receptors and their orthologs in major model organisms can now be studied across the various data and tool resources. For the first time, a Data mapper page enables users to map their own data onto receptors visualized as a GPCRome wheel, tree, clusters, list or heatmap. The structure model data have been expanded with models of physiological ligand complexes and updated with new state-specific structure models of all human GPCRs (built using AlphaFold, RoseTTAFold and AlphaFold-Multistate). Furthermore, a structure or model (pdb file) can now be queried against GPCRdb's entire structure/model collection through a Structuresimilarity search page implementing FoldSeek. Finally, for ligands, new search tools can query names, database identifiers, similarities or substructures against integrated entries from the ChEMBL, Guide to Pharmacology, PDSP Ki, PubChem, DrugCentral and DrugBank databases. GPCRdb is available at https://gpcrdb.org.

G 蛋白偶联受体(GPCR)是跨膜的传导体,可介导多种生理配体和药物的作用。GPCR 数据库 GPCRdb 在参考数据、分析、可视化、实验设计和传播方面为庞大的全球研究团体提供支持。在此,我们将从受体和配体的所有资源概述开始,介绍我们发布的第六个主要 GPCRdb 版本。作为一项重要补充,现在可以通过各种数据和工具资源对所有 400 多种人类气味受体及其在主要模式生物中的同源物进行研究。数据映射器页面首次允许用户将自己的数据映射到可视化为 GPCRome 轮、树、簇、列表或热图的受体上。结构模型数据增加了生理配体复合物模型,并更新了所有人类 GPCR 的新的特定状态结构模型(使用 AlphaFold、RoseTTAFold 和 AlphaFold-Multistate 构建)。此外,现在还可以通过实施 FoldSeek 的结构相似性(Structuresimilarity)搜索页面,对照 GPCRdb 的整个结构/模型集查询结构或模型(pdb 文件)。最后,对于配体,新的搜索工具可以根据 ChEMBL、Guide to Pharmacology、PDSP Ki、PubChem、DrugCentral 和 DrugBank 数据库中的整合条目查询名称、数据库标识符、相似性或子结构图。GPCRdb 可在 https://gpcrdb.org 上查阅。
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引用次数: 0
UniProt: the Universal Protein Knowledgebase in 2025. UniProt:2025 年的通用蛋白质知识库。
IF 16.6 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-11-18 DOI: 10.1093/nar/gkae1010

The aim of the UniProt Knowledgebase (UniProtKB; https://www.uniprot.org/) is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this publication, we describe ongoing changes to our production pipeline to limit the sequences available in UniProtKB to high-quality, non-redundant reference proteomes. We continue to manually curate the scientific literature to add the latest functional data and use machine learning techniques. We also encourage community curation to ensure key publications are not missed. We provide an update on the automatic annotation methods used by UniProtKB to predict information for unreviewed entries describing unstudied proteins. Finally, updates to the UniProt website are described, including a new tab linking protein to genomic information. In recognition of its value to the scientific community, the UniProt database has been awarded Global Core Biodata Resource status.

UniProt Knowledgebase (UniProtKB; https://www.uniprot.org/) 的目的是为用户提供一套全面、高质量和可免费访问的注有功能信息的蛋白质序列。在本出版物中,我们介绍了我们正在对生产流水线进行的改革,以便将 UniProtKB 中的序列限制在高质量、非冗余的参考蛋白质组中。我们将继续手工整理科学文献,添加最新的功能数据,并使用机器学习技术。我们还鼓励社区进行整理,以确保不会遗漏关键出版物。我们更新了 UniProtKB 使用的自动注释方法,以预测描述未研究蛋白质的未审查条目的信息。最后,我们还介绍了 UniProt 网站的更新,包括一个链接蛋白质和基因组信息的新标签。鉴于其对科学界的价值,UniProt 数据库已被授予全球核心生物数据资源地位。
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引用次数: 0
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Nucleic Acids Research
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