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

蛋白酶-蛋白靶点预测问题在生物信息学领域得到了广泛研究。然而，现有的算法要么是非常特定的（即它们只适用于特定的蛋白质或蛋白质家族，如Caspases），要么仅仅基于一级结构，因此非常容易提供假阳性（即非切割对被错误地标记为切割）。我们的工作是通过数据融合的应用，设计一个蛋白酶-蛋白靶算法，包裹了一般的蛋白质裂解机制。我们从MEROPS数据库中提取了多达9000对切割，湿实验室测试的蛋白酶-蛋白靶对。除了利用蛋白相似度（BLAST）外，还利用融合相关但直接相关的卵裂信息来设计模型。通过收集公开可用的数据库，如KEGG， BioGRID， STRING， Domine和Interpro，我们将结构域，途径，基因和蛋白质知识纳入我们的模型。通过在测试数据上评估我们的模型，而不是在训练和调整阶段，我们展示了它如何优于最先进的蛋白酶切割目标预测软件。与最先进的方法不同，该算法是通用的，而不是专用于特定的蛋白酶，因此它可以用于探索研究不足的蛋白酶，例如二级和三级结构完全未知。

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.