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データ融合によるタンパク質切断解析および疾患との関連性発見

通过数据融合进行蛋白质裂解分析并发现与疾病的关系

基本信息

批准号：
15F15788
负责人：
阿久津達也
金额：
$ 0.45万
依托单位：
Kyoto University
依托单位国家：
日本
项目类别：
Grant-in-Aid for JSPS Fellows
财政年份：
2015
资助国家：
日本
起止时间：
2015-11-09 至 2017-03-31
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-15F15788/
关键词：
caspase protease data fution bioinformatics Protein cleavage Data fusion Machine learning,

项目摘要

The problem of protease-protein target prediction has been extensively studied in Bioinformatics. However, existing algorithms are either very specific (i.e. they work only with specific proteins or protein families, such as Caspases) or solely based on the primary structure, therefore very prone to provide false positives (i.e. non-cleaving pairs wrongly labeled as cleaving). Our work consisted in the design of a protease-protein target algorithm, wrapping up the general protein cleavage machinery, through the application of data fusion.We extracted up to 9000 pairs of cleaving, wet lab tested protease-protein target pairs from the MEROPS database. Beside the use of protein similarity (BLAST), the model was designed by fuse relevant, but directly related cleavage information. By harvesting publicly available data bases such as KEGG, BioGRID, STRING, Domine and Interpro, we included domain, pathway, gene and protein knowledge to our model. By assessing our model on test data, not involved in the training and tuning phase, we showed how it outperforms state-of-the-art software for the protease cleavage target prediction. Unlike state-of-the-art approaches, this algorithm is general and not dedicated to specific proteases, therefore it can be used to explore poorly-studied proteases, where for example secondary and tertiary structure are completely unknown.

蛋白酶-蛋白质靶标预测问题在生物信息学中得到了广泛的研究。然而，现有的算法要么非常具体(即它们只适用于特定的蛋白质或蛋白质家族，如Caspase)，要么仅基于一级结构，因此非常容易提供假阳性(即错误地标记为切割的非切割对)。通过应用数据融合技术，我们设计了一个蛋白酶-蛋白质靶标算法，通过数据融合，我们从MEROPS数据库中提取了9000对经过湿法实验室测试的蛋白酶-蛋白质靶标对。除了使用蛋白质相似度(BLAST)外，该模型还融合了相关但直接相关的切割信息。通过收集KEGG、BioGRID、STRING、Domine和InterPro等公开可用的数据库，我们将结构域、途径、基因和蛋白质知识纳入到我们的模型中。通过在测试数据上评估我们的模型，而不涉及训练和调整阶段，我们展示了它如何在预测蛋白酶切割目标方面优于最先进的软件。与最先进的方法不同，该算法是通用的，并不专门用于特定的蛋白酶，因此它可以用于探索研究较少的蛋白酶，例如，二级和三级结构完全未知的蛋白酶。