Predicting kissing interactions in microRNA-target complex and assessment of microRNA activity.
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 83/105. We found that although the binding affinity alone predicts the miRNA repression efficiency with a high success rate of 73/105, 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 miRNA/mRNA complexes.
Department of Physics, University of Missouri, Columbia, MO 65211, USA.
Song
S
Cao
Shi-Jie
SJ
Chen
D000818:Animals; D001483:Base Sequence; D019295:Computational Biology; D004331:Drosophila melanogaster / Q000235:genetics; D015497:HIV-1 / Q000235:genetics; D035683:MicroRNAs / Q000737:chemistry* / Q000378:metabolism; D008969:Molecular Sequence Data; D009690:Nucleic Acid Conformation; D012333:RNA, Messenger / Q000737:chemistry* / Q000378:metabolism; D012367:RNA, Viral / Q000737:chemistry
D035683:MicroRNAs; D012333:RNA, Messenger; D012367:RNA, Viral