 Dear students, in this module, I am going to review the RNA structure prediction strategies that we studied in the chapter. First, we looked at the roles of the RNA molecules and their various types. We came to know that RNAs, they play a very important role in a lot of biological processes. They code for proteins, they regulate gene expression and so on. Then we looked at the methodology, the experimental methodology to measure the RNA structures. Atomic force microscopy was studied, but the problem was that since the process is difficult and cumbersome, we are going to have an algorithmic strategy to predict the RNA structures. Therefore, the need for RNA structure prediction led us to study the various structures that were present in the RNA molecules. We came to know that there were four general types of secondary structures that are there in an RNA molecule. These included the hairpin loops, bulges, intersections and the helices. So these secondary structures, they come together to create tertiary structures and then the tertiary structured RNAs, they go on to perform a variety of functions within the biological system. So the conceptual basis for structure prediction by looking at these four different secondary structures was that once an RNA molecule it folds, then it releases some energy. So once it releases some energy, the overall energy of the molecule goes down. So once the overall energy goes down, the molecule becomes more stable. So therefore, the foundation for structure prediction was laid on this very important statute that the more energy is released from a molecule, the more stable the structure will be. So therefore, we said folded RNA molecules are more stable as compared to unfolded ones. Then we also looked at some algorithms to perform this energy optimization as a result of base pairing in the RNA strand. So of course, the complementary nucleotides they couple by hydrogen bonding and released energy. The first thing that we looked at was the dart plot. So the dart plot simply counted the complementary nucleotides and then we created the secondary structures. Next we moved on to Zooker's algorithm. In Zooker's algorithm, we studied how specific quantities of energies released from the bonding between different nucleotides can be used towards computing the overall energy of the structure. Next we looked at the Martinez algorithm. In the Martinez algorithm, we weighted the energy released from each possible combination of the nucleotide coupling and therefore, we came towards a stochastic model for predicting the overall energy of the resultant molecule. And lastly, we looked at the Nusinov Jacob algorithm. So in this, we used a dynamic programming approach towards predicting the structure of the RNA molecule. Lastly, we looked at the RNA databases such as RNA bricks, etc., and how we can use these databases towards predicting better structures. Of course, there are several online tools which you can use to do all of this and one of them was the Virginia Tech server for Chauvasma for the Nusinov Jacobson algorithm and therefore, this was the overall strategy and the material for RNA structure prediction.