title: Exact calculation of loop formation probability identifies folding motifs in RNA secondary structures.
abstract: RNA secondary structure prediction is widely used to analyze RNA sequences. In an RNA partition function calculation, free energy nearest neighbor parameters are used in a dynamic programming algorithm to estimate statistical properties of the secondary structure ensemble. Previously, partition functions have largely been used to estimate the probability that a given pair of nucleotides form a base pair, the conditional stacking probability, the accessibility to binding of a continuous stretch of nucleotides, or a representative sample of RNA structures. Here it is demonstrated that an RNA partition function can also be used to calculate the exact probability of formation of hairpin loops, internal loops, bulge loops, or multibranch loops at a given position. This calculation can also be used to estimate the probability of formation of specific helices. Benchmarking on a set of RNA sequences with known secondary structures indicated that loops that were calculated to be more probable were more likely to be present in the known structure than less probable loops. Furthermore, highly probable loops are more likely to be in the known structure than the set of loops predicted in the lowest free energy structures.
authors.affiliation: Department of Biochemistry and Biophysics and Center for RNA Biology, University of Rochester Medical Center, Rochester, New York 14642, USA.
authors.forename: Michael F
authors.initials: MF
authors.lastname: Sloma
authors.affiliation: Department of Biochemistry and Biophysics and Center for RNA Biology, University of Rochester Medical Center, Rochester, New York 14642, USA.
authors.forename: David H
authors.initials: DH
authors.lastname: Mathews
mesh_terms: D009690:Nucleic Acid Conformation; D011336:Probability; D012313:RNA / Q000737:chemistry*; D013269:Stochastic Processes
chemical_list: D012313:RNA
keywords: RNA folding thermodynamics; RNA secondary structure; coaxial stacking; partition function; stochastic sampling