MicroRNA-small molecule association identification: from experimental results to computational models

小分子 小RNA 计算生物学 功能(生物学) 鉴定(生物学) 生物 生物信息学 基因 遗传学 植物
作者
Xing Chen,Na-Na Guan,Yazhou Sun,Jianqiang Li,Jia Qu
出处
期刊:Briefings in Bioinformatics [Oxford University Press]
被引量:85
标识
DOI:10.1093/bib/bby098
摘要

Small molecule is a kind of low molecular weight organic compound with variety of biological functions. Studies have indicated that small molecules can inhibit a specific function of a multifunctional protein or disrupt protein-protein interactions and may have beneficial or detrimental effect against diseases. MicroRNAs (miRNAs) play crucial roles in cellular biology, which makes it possible to develop miRNA as diagnostics and therapeutic targets. Several drug-like compound libraries were screened successfully against different miRNAs in cellular assays further demonstrating the possibility of targeting miRNAs with small molecules. In this review, we summarized the concept and functions of small molecule and miRNAs. Especially, five aspects of miRNA functions were exhibited in detail with individual examples. In addition, four disease states that have been linked to miRNA alterations were summed up. Then, small molecules related to four important miRNAs miR-21, 122, 4644 and 27 were selected for introduction. Some important publicly accessible databases and web servers of the experimentally validated or potential small molecule-miRNA associations were discussed. Identifying small molecule targeting miRNAs has become an important goal of biomedical research. Thus, several experimental and computational models have been developed and implemented to identify novel small molecule-miRNA associations. Here, we reviewed four experimental techniques used in the past few years to search for small-molecule inhibitors of miRNAs, as well as three types of models of predicting small molecule-miRNA associations from different perspectives. Finally, we summarized the limitations of existing methods and discussed the future directions for further development of computational models.
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