Final Activity Report Summary - CM-RNA (Comparative Modeling of RNA structures at atomic resolution) The view of ribonucleic acid (RNA) as a simple information transfer molecule has been continuously challenged since the discovery of the ribozymes, a class of RNA molecules with enzyme-like functions. Moreover, the recent discovery of tiny RNA molecules such as microRNAs and small interfering RNAs is transforming our thinking about how gene expression is regulated. Thus, RNA molecules are now known to carry a large repertory of biological functions within cells, such as transfer of information, enzymatic catalysis and regulation of cellular processes. Similar to proteins, functional RNA molecules fold into specific three-dimensional conformation, which is essential for performing their biological activity. However, our knowledge on the atomic mechanism by which RNA molecules adopt their biological active structures is limited. The ultimate application of a better characterisation of RNA folding is the prediction of their structure. This proposal aimed to develop an RNA structure prediction module within the Modeller program. The application of the method would greatly advance our fundamental knowledge of the relationship between sequence, structure and function of RNA molecules, which was likely to impact various fields of biomedicine, including, but not limited to, the design of new drugs to interact with RNA molecules, the understanding of existing drugs that might be interacting with RNA molecules and the study of how RNA molecules regulated processes at the cellular level. During the first two years of the proposed research, we were able to address three of the four aims of the proposal, which were essential steps for the development of a comparative RNA structure prediction method. We described the RNA structural space by applying a newly developed RNA pair-wise structure alignment method called SARA, which could be found in http://sgu.bionfo.cipf.es/services/SARA/. The alignments generated with the SARA program allowed for the derivation of a set of structural propensities of RNA molecules, which were essential for the recent implementation of an RNA structure prediction method within the Modeller program.