I am afraid there isn't any bioinformatic tool to find miRNA-mRNA binding, but you can choose some database (such as miRTarBase or starBase) to extract some high quality interactions. Then, using luciferase reporter assay to validate these targets is necessary.  

Target prediction tools are flaky at best. The intersection between any two prediction methods, or a prediction method and validated targets, is minimal. We really don't know yet the relative value of thermodynamic parameters, evolutionary conservation ets. Moreover, the algorithms always assume a linear unfolded free mRNA and an unlimited supply of the miRNA. In reality, mRNA is found in an mRNP, i.e. folded and coated by RNA binding proteins. miRNAs are found in limiting quantities, so their activity on you gene of interest depends on the abundance of other competing transcripts. And this is just the tip of the iceberg in terms of complications.

I would say the first key question is to check which miRNAs are actually expressed in your system. You don't want to investigate a miRNA that CAN regulate your GOI. You want to study the one that DOES regulate it. Any publicly available datasets where you can look for anti-correlations?

Good luck!

I think Anna made the best "starting point comment: I assume you work on a certain type of tissue/cell type, your gene of interest (GOI) is expressed in this cell type. If you want to know which microRNAs may control your GOI, it may be of great advantage to know which microRNAs are the main players in your cells. If you can find out, check the top 10 or 20 of them and play it conservatively: which of them are predicted to target your GOI based on Targetscan. This should dramatically narrow down your cohort of candidates. Then you could "dump" microRNA inhibitors on your cells and see which inhibitor can actually increase the endogenous protein (or mRNA) expression or which one can actually increase the expression of a reporter gene linked to the 3'UTR of your GOI. Does that seem like a plan?

Data on microRNA expression are abundant, on their exact level (top 20) is a little bit harder to determine. But you may already have such data already at hand or can find it in databases or the literature. GOOD LUCK!

Thank you all for your comments. I used miRwalk to find out the miRNAs that target my GOI. I think this prediction tools do consider the parameters which Anna mentioned. As Thomas suggested, I am looking at those miRNAs which are present in my cell under study. With the help of literature survey, I am also looking at their expression levels in the disease under consideration as well as the other genes and signalling pathways they regulate.

Thanks

Wish you all a happy research :)

Puwen Tan is there any reference for above mentioned tools.