Construction of phosphorylation interaction networks by text mining of full-length articles using the eFIP system
Construction of phosphorylation interaction networks by text mining of full-length articles using the eFIP system
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DOI：
10.1093/database/bav020
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发表时间：
2015-03-31
影响因子：
5.8
通讯作者：
Arighi, Cecilia N.
中科院分区：
文献类型：
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作者：
Tudor, Catalina O.;Ross, Karen E.;Arighi, Cecilia N.
蛋白质磷酸化是一种可逆的翻译后修饰，其中蛋白激酶向蛋白质添加磷酸基团，从而可能调节其功能、定位和/或活性。磷酸化可以影响蛋白质-蛋白质相互作用（PPI），消除与先前结合伴侣的相互作用或实现新的相互作用。从科学文献中提取与PPI信息相结合的磷酸化信息将有助于创建激酶，底物和相互作用伙伴的磷酸化相互作用网络，从而发现蛋白质磷酸化的功能结果。越来越多的PPI数据库对捕获相互作用伙伴的磷酸化状态感兴趣。我们之前开发了eFIP（提取磷酸化的功能影响）文本挖掘系统，该系统可以识别磷酸化蛋白质和磷酸化依赖的PPI。在这项工作中，我们提出了几个增强eFIP系统：（i）全文文章的文本挖掘从PubMed中心开放获取集合;（ii）的RLIMS-P 2.0系统的集成提取磷酸化事件与激酶，底物和网站信息;（iii）扩展PPI模块，增加描述互动的新触发词/短语，以及（iv）增加了iSimp工具，用于简化句子，以帮助匹配句法模式。我们增强了网站功能：（i）支持基于蛋白质角色（激酶，底物，相互作用伙伴）或使用关键字的搜索;（ii）将蛋白质实体链接到其相应的UniProt标识符（如果映射）和（iii）支持使用Cytoscape可视化探索磷酸化相互作用网络。eFIP在100篇文章中的准确率为92.4%，召回率为76.5%，F-measure为83.7%。为了证明eFIP用于知识提取和发现，我们构建了磷酸化依赖的相互作用网络，涉及从癌症相关与糖尿病相关文章中鉴定的14-3-3蛋白。从eFIP搜索中获得的激酶、磷蛋白和相互作用物的磷酸化相互作用网络的比较，沿着蛋白质集的富集分析，揭示了几个共享的相互作用，突出了在这两种疾病的背景下讨论的共同途径。
Protein phosphorylation is a reversible post-translational modification where a protein kinase adds a phosphate group to a protein, potentially regulating its function, localization and/or activity. Phosphorylation can affect protein-protein interactions (PPIs), abolishing interaction with previous binding partners or enabling new interactions. Extracting phosphorylation information coupled with PPI information from the scientific literature will facilitate the creation of phosphorylation interaction networks of kinases, substrates and interacting partners, toward knowledge discovery of functional outcomes of protein phosphorylation. Increasingly, PPI databases are interested in capturing the phosphorylation state of interacting partners. We have previously developed the eFIP (Extracting Functional Impact of Phosphorylation) text mining system, which identifies phosphorylated proteins and phosphorylation-dependent PPIs. In this work, we present several enhancements for the eFIP system: (i) text mining for full-length articles from the PubMed Central open-access collection; (ii) the integration of the RLIMS-P 2.0 system for the extraction of phosphorylation events with kinase, substrate and site information; (iii) the extension of the PPI module with new trigger words/phrases describing interactions and (iv) the addition of the iSimp tool for sentence simplification to aid in the matching of syntactic patterns. We enhance the website functionality to: (i) support searches based on protein roles (kinases, substrates, interacting partners) or using keywords; (ii) link protein entities to their corresponding UniProt identifiers if mapped and (iii) support visual exploration of phosphorylation interaction networks using Cytoscape. The evaluation of eFIP on full-length articles achieved 92.4% precision, 76.5% recall and 83.7% F-measure on 100 article sections. To demonstrate eFIP for knowledge extraction and discovery, we constructed phosphorylation-dependent interaction networks involving 14-3-3 proteins identified from cancer-related versus diabetes-related articles. Comparison of the phosphorylation interaction network of kinases, phosphoproteins and interactants obtained from eFIP searches, along with enrichment analysis of the protein set, revealed several shared interactions, highlighting common pathways discussed in the context of both diseases.