![]() ![]() However, this step either leaves the necessity to first calculate a huge set of suboptimal solutions or reduces the output by too small an amount, with the additional danger of missing the native structure. structures without lone base pairs) ( 7, 8) or saturated structures ( 9, 10). The problem has been tackled either by filtering the output to reduce the number of similar structures ( 6) or by directly reducing the number of solutions with the restriction to canonical structures (i.e. However, the number of suboptimal solutions grows exponentially with the size of the energy range, for moderately long sequences reaching several hundred thousand within an energy range of only a few kcal/mol. This is addressed by a number of programs that output suboptimal solutions ( 4, 5). ![]() However, it seems reasonable that the energy of the native structure should not be too far away from the predicted minimum free energy, and thus the native structure should be present among suboptimal solutions in a small energy range above the minimum free energy. Similar problems are inaccuracies of the energy model, different chemical conditions in living cells and the fact that RNA molecules interact with other molecules that can alter their conformations. This can be explained by the existence of modified bases in native tRNAs, which leads to the formation of a structure that is not the optimal under the energy model used. Ironically, for one of the most-studied classes of RNA, the transfer RNA (tRNA), predicted minimum free energy structures are frequently much different from the native cloverleaf structure, forming an elongated hairpin. Efficient solutions based on thermodynamic parameters have been known since ( 1), with improvements in the energy models ( 2) and extensions to related questions such as base pair probabilities ( 3). Since the experimental determination of RNA structure is time-consuming and expensive, its computational prediction is of great interest. The function of a non-protein-coding RNA is often determined by its structure. RNAshapes is available for download and as an online version on the Bielefeld Bioinformatics Server. We complement this study with a large-scale analysis of the growth behaviour of structure and shape spaces. This demonstrates that the researcher can quickly focus on the structures of interest, without processing up to thousands of near-optimal solutions. For a given energy range, the number of shapes is considerably smaller than the number of structures, and in all cases, the native structures were among the top shape representatives. We applied RNAshapes to the prediction of optimal and suboptimal abstract shapes of several RNAs. Shape analysis is implemented in the program RNAshapes. Each shape of an RNA molecule comprises a class of similar structures and has a representative structure of minimal free energy within the class. Here, we formalize the concept of abstract shapes and introduce their efficient computation. While this can be accomplished by a number of programs, the user is often confronted with large outputs of similar structures, although he or she is interested in structures with more fundamental differences, or, in other words, with different abstract shapes. Frequently, however, the predicted minimum free energy structures are not the native ones, leading to the necessity of generating suboptimal solutions. Since experimental determination of RNA structure is time-consuming and expensive, its computational prediction is of great interest, and efficient solutions based on thermodynamic parameters are known. ![]() ![]()
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