Integrating Chemical Footprinting Data into RNA Secondary Structure Prediction

摘要: Chemical and enzymatic footprinting experiments, such as shape (selective 2'-hydroxyl acylation analyzed by primer extension), yield important information about RNA secondary structure. Indeed, since the [Formula: see text]-hydroxyl is reactive at flexible (loop) regions, but unreactive base-paired yields quantitative data which nucleotides are base-paired. Recently, low error rates in structure prediction have been reported for three RNAs of moderate size, including base stacking pseudo-energy terms derived from into computation minimum free energy Here, we describe a novel method, RNAsc (RNA soft constraints), includes each nucleotide position, rather than only positions. We prove that self-consistent, sense nucleotide-specific probabilities being unpaired Boltzmann ensemble always become more closely correlated with input after application RNAsc. From this mathematical perspective, predicted should be 'correct', much 'correct'. benchmark against previously mentioned method eight RNAs, both native structures known, to find same accuracy 7 out 8 cases, an improvement 25% one case. Furthermore, present what appears first direct comparison in-line probing data, comparing yeast asp-tRNA literature experiments recently performed. With respect several criteria, appear robust least case asp-tRNA.