MicroRNA target prediction based upon metastable RNA conformations

MicroRNAs (miRNAs) play an important role in biomarker research. Identifying their targets and inferring their functions have been of a great importance to developing our understanding of many biological processes and fundamental novel anti-cancer and viral therapies. Since the discovery and validation of true miRNA-messengerRNA (mRNA) bindings is a laborious and expensive process, computational tools for the prediction of miRNA targets are essential in this research area. Advanced tools of miRNA target prediction incorporate knowledge about secondary structures of mRNA sequences, usually the 3’UTR into the evaluation and assessment of putative miRNAmRNA bindings. The default secondary RNA structure in most target prediction tools of this type is the minimum free energy conformation or a representative of the ensemble of all possible RNA structures. A key indicator of putative miRNA-mRNA bindings is the energy required to open base pairs that are present in the potential binding site within the conformation. However, mRNAs as well as miRNAs are present in a single cell in multiple copies, where the number of copies may range from several tens up to several hundreds of copies, each of them transcribed from DNA at different points of time and therefore, potentially, being present in different folding stages, most likely in metastable conformations. In this thesis we have addressed the problem of miRNA bindings to metastable RNA secondary structures in the context of Single Nucleotide Polymorphisms (SNPs). To this end, we first searched the recent literature for disease-related triples [mRNA/3’UTR; SNP; miRNA] that have been analysed by methods including PCR and/or luciferase reporter assays. We next compared results of two major computational approaches to miRNA target ranking prediction: conservation feature using TargetScan tool and target site accessibility feature using PITA and STarMir tools. We showed that site accessibility may be a better ranking criterion. We then studied the problem of miRNA bindings to metastable secondary structures in the context of SNPs and mRNA concentration levels i.e. whether features of miRNA bindings to metastable conformations could provide additional information supporting the differences in expression levels of the two sequences defined by a SNP. We showed that among the different parameters we introduced and analyzed, we found that three of them, related to the average depth and average opening energy of metastable conformations, may provide supporting information for a stronger separation between miRNA bindings to the two alleles defined by a given SNP. These findings were a trigger to devise a novel target prediction tool that incorporates metastable secondary structures with low energy levels into predictions. We present, RNAStrucTar, a miRNA target prediction tool that analyses putative mRNA binding sites within 3’UTR secondary structures representing metastable conformations. The first stage consists of generating conformations that can be classified as deep local minima. The second stage incorporates duplex structure prediction through sequence alignment and energy computation. Target site accessibility related to different sets of metastable conformations is also taken into account. An overall interaction score computed from multiple binding sites is returned. The approach is discussed in the context of SNPs where our manually curated [mRNA;SNP;miRNA] dataset is utilised. RNAStrucTar predictions are in favour of the allele with the stronger miRNA binding stated in the underlying literature in 22 instances, while the resulting scores are indifferent in ten cases. For the two other cases (HTR3E and FGF20), the score is in favour of the weaker allele. In this respect, RNAStrucTar results are better than PITA and STarMir, with a positive prediction for RAD51 and MSLN (STarMir favours the weaker allele).