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• W1972508745 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 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." @default.
• W1972508745 created "2016-06-24" @default.
• W1972508745 creator A5022452561 @default.
• W1972508745 creator A5035021352 @default.
• W1972508745 date "2012-02-03" @default.
• W1972508745 modified "2023-09-26" @default.
• W1972508745 title "Predicting kissing interactions in microRNA–target complex and assessment of microRNA activity" @default.
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• W1972508745 doi "https://doi.org/10.1093/nar/gks052" @default.
• W1972508745 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3378890" @default.
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