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UniProt:2023 年的通用蛋白质知识库。

UniProt: the Universal Protein Knowledgebase in 2023.

出版信息

Nucleic Acids Res. 2023 Jan 6;51(D1):D523-D531. doi: 10.1093/nar/gkac1052.


DOI:10.1093/nar/gkac1052
PMID:36408920
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9825514/
Abstract

The aim of the UniProt Knowledgebase 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 enhancements made to our data processing pipeline and to our website to adapt to an ever-increasing information content. The number of sequences in UniProtKB has risen to over 227 million and we are working towards including a reference proteome for each taxonomic group. We continue to extract detailed annotations from the literature to update or create reviewed entries, while unreviewed entries are supplemented with annotations provided by automated systems using a variety of machine-learning techniques. In addition, the scientific community continues their contributions of publications and annotations to UniProt entries of their interest. Finally, we describe our new website (https://www.uniprot.org/), designed to enhance our users' experience and make our data easily accessible to the research community. This interface includes access to AlphaFold structures for more than 85% of all entries as well as improved visualisations for subcellular localisation of proteins.

摘要

UniProt 知识库的目标是为用户提供一套全面、高质量且免费获取的蛋白质序列,这些序列都附有功能信息注释。在本出版物中,我们描述了对我们的数据处理管道和网站所做的增强,以适应不断增长的信息量。UniProtKB 中的序列数量已超过 2.27 亿,我们正在努力为每个分类群包含一个参考蛋白质组。我们继续从文献中提取详细注释来更新或创建经过评审的条目,同时使用各种机器学习技术的自动系统提供的注释来补充未经评审的条目。此外,科学界继续向他们感兴趣的 UniProt 条目的出版物和注释做出贡献。最后,我们描述了我们的新网站(https://www.uniprot.org/),旨在增强我们用户的体验,并使我们的数据易于研究社区访问。该界面包括对超过 85%的所有条目进行 AlphaFold 结构的访问,以及对蛋白质亚细胞定位的改进可视化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2b9/9825514/f9ac32fcee00/gkac1052fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2b9/9825514/e5bfd5ada95d/gkac1052fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2b9/9825514/1b3132739308/gkac1052fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2b9/9825514/75b12f023d77/gkac1052fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2b9/9825514/f9ac32fcee00/gkac1052fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2b9/9825514/e5bfd5ada95d/gkac1052fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2b9/9825514/1b3132739308/gkac1052fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2b9/9825514/75b12f023d77/gkac1052fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2b9/9825514/f9ac32fcee00/gkac1052fig4.jpg

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本文引用的文献

[1]
InterPro in 2022.

Nucleic Acids Res. 2023-1-6

[2]
SwissBioPics-an interactive library of cell images for the visualization of subcellular location data.

Database (Oxford). 2022-4-12

[3]
A joint NCBI and EMBL-EBI transcript set for clinical genomics and research.

Nature. 2022-4

[4]
Protein embeddings and deep learning predict binding residues for various ligand classes.

Sci Rep. 2021-12-13

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A crowdsourcing open platform for literature curation in UniProt.

PLoS Biol. 2021-12

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Database resources of the national center for biotechnology information.

Nucleic Acids Res. 2022-1-7

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The European Nucleotide Archive in 2021.

Nucleic Acids Res. 2022-1-7

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The European Bioinformatics Institute (EMBL-EBI) in 2021.

Nucleic Acids Res. 2022-1-7

[9]
Identification of Iron-Sulfur (Fe-S) Cluster and Zinc (Zn) Binding Sites Within Proteomes Predicted by DeepMind's AlphaFold2 Program Dramatically Expands the Metalloproteome.

J Mol Biol. 2022-1-30

[10]
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Nucleic Acids Res. 2022-1-7

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