RNA folding with hard and soft constraints

Abstract

BackgroundA large class of RNA secondary structure prediction programs uses an elaborate energy model grounded in extensive thermodynamic measurements and exact dynamic programming algorithms. External experimental evidence can be in principle be incorporated by means of hard constraints that restrict the search space or by means of soft constraints that distort the energy model. In particular recent advances in coupling chemical and enzymatic probing with sequencing techniques but also comparative approaches provide an increasing amount of experimental data to be combined with secondary structure prediction.ResultsResponding to the increasing needs for a versatile and user-friendly inclusion of external evidence into diverse flavors of RNA secondary structure prediction tools we implemented a generic layer of constraint handling into the ViennaRNA Package. It makes explicit use of the conceptual separation of the “folding grammar” defining the search space and the actual energy evaluation, which allows constraints to be interleaved in a natural way between recursion steps and evaluation of the standard energy function.ConclusionsThe extension of the ViennaRNA Package provides a generic way to include diverse types of constraints into RNA folding algorithms. The computational overhead incurred is negligible in practice. A wide variety of application scenarios can be accommodated by the new framework, including the incorporation of structure probing data, non-standard base pairs and chemical modifications, as well as structure-dependent ligand binding.Electronic supplementary materialThe online version of this article (doi:10.1186/s13015-016-0070-z) contains supplementary material, which is available to authorized users.

Computationally Reconstructing Cotranscriptional RNA Folding Pathways from Experimental Data Reveals Rearrangement of Non-Native Folding Intermediates

“…The existing RNA secondary structure prediction algorithms that can use experimental probing data as input can be placed into two groups that either: (1) directly modify the free energy model used in the folding calculation (Deigan et al, 2009;Lorenz et al, 2016a;Lorenz et al, 2016b;Lorenz et al, 2016c;Washietl et al, 2012;Wu et al, 2015) or (2) select a structure from a set of possible structures generated by unmodified folding calculations (Ding et al, 2004;Ouyang et al, 2013;Tan et al, 2017). These methods have shown great improvements in structure prediction, with prediction accuracies increasing by two-fold in some cases (Deigan et al, 2009).…”

Beyond Plug and Pray: Context Sensitivity andin silicoDesign of Artificial Neomycin Riboswitches

“…In thermodynamic equilibrium, the probability to observe a particular structure x depends on its Gibbs free energy ∆G(x), which can be computed using the nearest-neighbor energy model [18], which specifies sequence-dependent stabilizing contributions for base pair stacking and destabilizing loop terms. [19,20] because it provides a versatile way to handle constraints [21] and thus to compute partition functions for a set Y of structures with desired structural features, e. g., a terminator hairpin or a binding pocket for a certain ligand.…”

Protein yield is tunable by synonymous codon changes of translation initiation sites

“…Minimum free energy, opening energy and avoidance were calculated using RNAfold, RNAplfold and RNAup from ViennaRNA package (version 2.4.11), respectively [50][51][52][55][56][57][58] .…”