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膜蛋白数据库 3.0:包含 AlphaFold 模型的单次跨膜蛋白数据库。

Membranome 3.0: Database of single-pass membrane proteins with AlphaFold models.

机构信息

Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, Ann Arbor, Michigan, USA.

Department of Electrical Engineering and Computer Science, College of Engineering, University of Michigan, Ann Arbor, Michigan, USA.

出版信息

Protein Sci. 2022 May;31(5):e4318. doi: 10.1002/pro.4318.

DOI:10.1002/pro.4318
PMID:35481632
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9047035/
Abstract

The Membranome database provides comprehensive structural information on single-pass (i.e., bitopic) membrane proteins from six evolutionarily distant organisms, including protein-protein interactions, complexes, mutations, experimental structures, and models of transmembrane α-helical dimers. We present a new version of this database, Membranome 3.0, which was significantly updated by revising the set of 5,758 bitopic proteins and incorporating models generated by AlphaFold 2 in the database. The AlphaFold models were parsed into structural domains located at the different membrane sides, modified to exclude low-confidence unstructured terminal regions and signal sequences, validated through comparison with available experimental structures, and positioned with respect to membrane boundaries. Membranome 3.0 was re-developed to facilitate visualization and comparative analysis of multiple 3D structures of proteins that belong to a specified family, complex, biological pathway, or membrane type. New tools for advanced search and analysis of proteins, their interactions, complexes, and mutations were included. The database is freely accessible at https://membranome.org.

摘要

Membranome 数据库提供了来自六个不同进化起源的生物体中单通道(即双位)膜蛋白的全面结构信息,包括蛋白-蛋白相互作用、复合物、突变、实验结构和跨膜α-螺旋二聚体模型。我们展示了该数据库的新版本 Membranome 3.0,通过修订 5758 个双位蛋白集并在数据库中纳入 AlphaFold 2 生成的模型,对其进行了重大更新。AlphaFold 模型被解析为位于不同膜侧的结构域,经过修改以排除低置信度的无规终端区域和信号序列,通过与现有实验结构进行比较进行验证,并相对于膜边界进行定位。Membranome 3.0 被重新开发,以方便可视化和比较分析属于指定家族、复合物、生物途径或膜类型的多个蛋白质的 3D 结构。还包括用于蛋白质、它们的相互作用、复合物和突变的高级搜索和分析的新工具。该数据库可在 https://membranome.org 免费获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10b1/9047035/1dbfb1911dd3/PRO-31-e4318-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10b1/9047035/b8aea9312d3e/PRO-31-e4318-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10b1/9047035/1dbfb1911dd3/PRO-31-e4318-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10b1/9047035/b8aea9312d3e/PRO-31-e4318-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10b1/9047035/1dbfb1911dd3/PRO-31-e4318-g001.jpg

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2
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Front Pharmacol. 2024 Jan 24;14:1251061. doi: 10.3389/fphar.2023.1251061. eCollection 2023.
4
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