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PlantMP:一个具有多重功能的植物蛋白数据库。

PlantMP: a database for moonlighting plant proteins.

机构信息

Department of Computer Science, College of Science and Technology, Wenzhou-Kean University, 88 Daxue Road, Ouhai, Wenzhou, Zhejiang Province, China.

Department of Biology, College of Science and Technology, Wenzhou-Kean University, 88 Daxue Road, Ouhai, Wenzhou, Zhejiang Province, China.

出版信息

Database (Oxford). 2019 Jan 1;2019. doi: 10.1093/database/baz050.

DOI:10.1093/database/baz050
PMID:31032837
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6482322/
Abstract

Moonlighting proteins are single polypeptide chains capable of executing two or more distinct biochemical and/or biological functions. Here, we describe the development of PlantMP, which is a manually curated online-based database of plant proteins that are known to moonlight'. The database contains searchable UniProt IDs and names, canonical and moonlighting functions, gene ontology numbers, plant species as well as links to the PubMed indexed articles. Proteins homologous to experimentally confirmed moonlighting proteins from the model plant Arabidopsis thaliana are provided as a separate list of likely moonlighters'. Additionally, we also provide a list of predicted Arabidopsis moonlighting proteins reported in the literature. Currently, PlantMP contains 110 plant moonlighting proteins, 10 likely moonlighters' and 27 predicted moonlighters'. Organizing plant moonlighting proteins in one platform enables researchers to conveniently harvest plant-specific raw and processed data such as the molecular functions, biological roles and structural features essential for hypothesis formulation in basic research and for biotechnological innovations.

摘要

月收入蛋白是能够执行两种或多种不同生化和/或生物学功能的单多肽链。在这里,我们描述了 PlantMP 的开发,这是一个在线的手动编辑的数据库,包含已知的具有“兼职”功能的植物蛋白。该数据库包含可搜索的 UniProt ID 和名称、规范和兼职功能、基因本体编号、植物物种以及指向 PubMed 索引文章的链接。还提供了与拟南芥模型植物中经过实验证实的兼职蛋白同源的蛋白作为“可能的兼职蛋白”的单独列表。此外,我们还提供了文献中报道的预测拟南芥兼职蛋白的列表。目前,PlantMP 包含 110 种植物兼职蛋白、10 种“可能的兼职蛋白”和 27 种“预测的兼职蛋白”。将植物兼职蛋白组织在一个平台上,使研究人员能够方便地获取植物特有的原始和处理数据,如分子功能、生物学作用和结构特征,这些数据对于基础研究中的假设形成和生物技术创新至关重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/761e/6482322/0a526548e996/baz050f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/761e/6482322/3778ceb2ed83/baz050f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/761e/6482322/2332dbf1b212/baz050f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/761e/6482322/0a526548e996/baz050f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/761e/6482322/3778ceb2ed83/baz050f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/761e/6482322/2332dbf1b212/baz050f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/761e/6482322/0a526548e996/baz050f3.jpg

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