Center for non-coding RNA in Technology and Health


Automated RNA 3D module extraction and modeling with discriminative power.


Recent progress in predicting RNA structure is taking a route towards not only explicit prediction of RNA 3D structure, but also filling the 'gap' in 2D RNA structure prediction where, for example, predicted internal loops often can take a structure based on non-canonical base pairs. This is increasingly recognized with the steady increase of known RNA 3D modules. There is a general interest in matching modules from one molecule to other molecules for which the 3D structure is not known. However, a major challenge is to determine whether the module is trustworthy in the first place. Another challenge is that module recognition and modeling require time consuming manual interference. We have created a pipeline, metaRNAmodules, which completely automates extracting putative FR3D modules and mapping of such modules to Rfam alignments to obtain comparative evidence. In a subsequent step a module represented as a two-dimensional graph is fed into the RMDetect program to test the discriminative power on real and randomized Rfam alignments. An initial extraction of 22495 3D modules in all PDB files results in 977 internal loop and 17 hairpin loop modules with clear discriminative power. Many of these modules describe only minor variants of each other. Indeed, mapping of the modules onto Rfam families results in 35 unique locations in 11 different families.


The standalone version of the metaRNAmodules pipeline is available for download here.