Gene Ontology and KEGG Pathway Enrichment Analysis of a Drug Target-Based Classification System
Gene Ontology and KEGG Pathway Enrichment Analysis of a Drug Target-Based Classification System
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基于药物靶标的分类系统的基因本体和 KEGG 通路富集分析
DOI：
10.1371/journal.pone.0126492
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发表时间：
2015-05-07
期刊：
影响因子：
3.7
通讯作者：
Cai, Yu-Dong
中科院分区：
文献类型：
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作者：
Chen, Lei;Chu, Chen;Cai, Yu-Dong
药物-靶标相互作用（DTI）是药学研究的一个重要方面。随着新药物数据资源的不断增加，计算方法已成为预测新dti的强大且省力的工具。然而，到目前为止，这些预测大多是基于结构上的相似性，而不是生物学上的相关性。在本研究中，我们通过对DTI数据库的进一步分类和解释，首次提出了“GO and KEGG富集评分”方法来表示某一类药物分子。通过收集KEGG数据，构建了一个包含2015种药物的基准数据集，这些药物被划分为9类((1)G蛋白偶联受体、(2)细胞因子受体、(3)核受体、(4)离子通道、(5)转运体、(6)酶、(7)蛋白激酶、（8)细胞抗原和(9)病原体）。我们使用流行的特征选择“最小冗余最大相关性（mRMR）”方法分析了每个类别和每种药物在GO术语和KEGG途径中的贡献，并提取了关键的GO术语和KEGG途径。我们的分析揭示了每个药物类别中最富集的GO术语和KEGG通路，这些在文献和临床试验中都是高度富集的。我们的结果首次提供了药物、靶点和生物学功能之间的生物学相关性，为未来的DTI预测提供了新的基础。
Drug-target interaction (DTI) is a key aspect in pharmaceutical research. With the ever-increasing new drug data resources, computational approaches have emerged as powerful and labor-saving tools in predicting new DTIs. However, so far, most of these predictions have been based on structural similarities rather than biological relevance. In this study, we proposed for the first time a “GO and KEGG enrichment score” method to represent a certain category of drug molecules by further classification and interpretation of the DTI database. A benchmark dataset consisting of 2,015 drugs that are assigned to nine categories ((1) G protein-coupled receptors, (2) cytokine receptors, (3) nuclear receptors, (4) ion channels, (5) transporters, (6) enzymes, (7) protein kinases, (8) cellular antigens and (9) pathogens) was constructed by collecting data from KEGG. We analyzed each category and each drug for its contribution in GO terms and KEGG pathways using the popular feature selection “minimum redundancy maximum relevance (mRMR)” method, and key GO terms and KEGG pathways were extracted. Our analysis revealed the top enriched GO terms and KEGG pathways of each drug category, which were highly enriched in the literature and clinical trials. Our results provide for the first time the biological relevance among drugs, targets and biological functions, which serves as a new basis for future DTI predictions.