Predicting kissing interactions in microRNA-target complex and assessment of microRNA activity

Song Cao, Shi Jie Chen

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

MicroRNAs (miRNAs) are a class of short RNA molecules that play an important role in post-transcriptional gene regulation. Computational prediction of the miRNA target sites in mRNA is crucial for understanding the mechanism of miRNA-mRNA interactions. We here develop a new computational model that allows us to treat a variety of miRNA-mRNA kissing interactions, which have been ignored in the currently existing miRNA target prediction algorithms. By including all the different inter-and intra-molecular base pairs, this new model can predict both the structural accessibility of the target sites and the binding affinity (free energy). Applications of the model to a test set of 105 miRNA-gene systems show a notably improved success rate of 83105. We found that although the binding affinity alone predicts the miRNA repression efficiency with a high success rate of 73105, the structure in the seed region can significantly influence the miRNA activity. The method also allows us to efficiently search for the potent miRNA from a pool of miRNA candidates for any given gene target. Furthermore, extension of the method may enable predictions of the three-dimensional (3D) structures of miRNAmRNA complexes.

Original languageEnglish
Pages (from-to)4681-4690
Number of pages10
JournalNucleic acids research
Volume40
Issue number10
DOIs
StatePublished - May 2012

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