Mining significant substructure pairs for interpreting polypharmacology in drug-target network.
Mining significant substructure pairs for interpreting polypharmacology in drug-target network.
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DOI：
10.1371/journal.pone.0016999
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发表时间：
2011-02-23
期刊：
影响因子：
3.7
通讯作者：
Mamitsuka H
中科院分区：
文献类型：
--
作者：
Takigawa I;Tsuda K;Mamitsuka H
目前药物-靶点网络的一个重要特征是药物往往与多个靶点结合，称为多药理学或药物混杂。最近的文献表明，药物和靶标中相对较小的片段在形成多药理学中至关重要。我们假设多药理学背后的原理嵌入在药物与靶点相互作用的分子图和氨基酸序列中的配对片段中。我们开发了一种快速，可扩展的算法，用于从药物-靶标相互作用中挖掘显着共现的子图-子序列对。我们的方法的一个值得注意的特点是捕捉显着的子图子序列的配对模式，而无论是药物或目标的模式，在文献中只考虑到目前为止。重要的子结构对允许将药物-靶标相互作用分组为簇，覆盖约75%的含有批准药物的相互作用。这些聚类彼此高度排他性，具有统计学显著性，并且在逻辑上意味着每个聚类对应于一种独特的多种药理学类型。这些排他的集群不能很容易地通过使用药物或目标信息，但自然发现突出药物-目标相互作用中的显着的子结构对。这些结果证实了我们的方法解释药物靶点网络中的多药理学的有效性。
A current key feature in drug-target network is that drugs often bind to multiple targets, known as polypharmacology or drug promiscuity. Recent literature has indicated that relatively small fragments in both drugs and targets are crucial in forming polypharmacology. We hypothesize that principles behind polypharmacology are embedded in paired fragments in molecular graphs and amino acid sequences of drug-target interactions. We developed a fast, scalable algorithm for mining significantly co-occurring subgraph-subsequence pairs from drug-target interactions. A noteworthy feature of our approach is to capture significant paired patterns of subgraph-subsequence, while patterns of either drugs or targets only have been considered in the literature so far. Significant substructure pairs allow the grouping of drug-target interactions into clusters, covering approximately 75% of interactions containing approved drugs. These clusters were highly exclusive to each other, being statistically significant and logically implying that each cluster corresponds to a distinguished type of polypharmacology. These exclusive clusters cannot be easily obtained by using either drug or target information only but are naturally found by highlighting significant substructure pairs in drug-target interactions. These results confirm the effectiveness of our method for interpreting polypharmacology in drug-target network.
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