Integrating protein-protein interactions and text mining for protein function prediction.
Integrating protein-protein interactions and text mining for protein function prediction.
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DOI：
10.1186/1471-2105-9-s8-s2
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发表时间：
2008-07-22
影响因子：
3
通讯作者：
Rebholz-Schuhmann D
中科院分区：
文献类型：
--
作者：
Jaeger S;Gaudan S;Leser U;Rebholz-Schuhmann D
蛋白质的功能注释仍然是一项具有挑战性的任务。目前，科学文献是尚未整理的功能注释的主要来源，但整理工作缓慢且昂贵。支持这项工作的自动技术仍然缺乏可靠性。我们开发了一种方法来识别保守的蛋白质相互作用图并根据这些图中的直向同源物预测缺失的蛋白质功能。为了提高结果的准确性，我们还实施了一个程序，根据文献报告的结果验证所有预测。使用此程序，可以自动验证 UniProtKb/Swiss-Prot 中提供的具有高度保守的直向同源蛋白的 GO 注释的 80% 以上。对于蛋白质的子集，我们预测了 UniProtKb/Swiss-Prot 中不可用的新 GO 注释。根据训练有素的策展人的验证，所有预测都是正确的（100% 精确度）。因此，我们将 CCS 和文献挖掘相结合的方法是一种高度可靠的方法，可以预测具有直向同源物的弱特征蛋白质的 GO 注释。
Functional annotation of proteins remains a challenging task. Currently the scientific literature serves as the main source for yet uncurated functional annotations, but curation work is slow and expensive. Automatic techniques that support this work are still lacking reliability. We developed a method to identify conserved protein interaction graphs and to predict missing protein functions from orthologs in these graphs. To enhance the precision of the results, we furthermore implemented a procedure that validates all predictions based on findings reported in the literature. Using this procedure, more than 80% of the GO annotations for proteins with highly conserved orthologs that are available in UniProtKb/Swiss-Prot could be verified automatically. For a subset of proteins we predicted new GO annotations that were not available in UniProtKb/Swiss-Prot. All predictions were correct (100% precision) according to the verifications from a trained curator. Our method of integrating CCSs and literature mining is thus a highly reliable approach to predict GO annotations for weakly characterized proteins with orthologs.