Basin Hopping Graph: a computational framework to characterize RNA folding landscapes

Kucharík, Marcel; Hofacker, Ivo L.; Stadler, Peter F.; Qin, Jing

doi:10.1093/bioinformatics/btu156

Cited by 38 publications

(55 citation statements)

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“…Understanding the kinetics, such as the rate and pathways for the conformational changes, is critical for deciphering the mechanism of RNA function (9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19). Extensive experimental and theoretical studies on RNA folding kinetics have provided significant insights into the kinetic mechanism of RNA functions (19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36). However, due to the complexity of the RNA folding energy landscape (37)(38)(39)(40)(41)(42)(43)(44)(45)(46) and the limitations of experimental tools (47)(48)(49)(50)(51)(52)(53)(54)(55), many fundamental problems, ...…”

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confidence: 99%

Understanding the kinetic mechanism of RNA single base pair formation

Chen

2015

Proc. Natl. Acad. Sci. U.S.A.

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RNA functions are intrinsically tied to folding kinetics. The most elementary step in RNA folding is the closing and opening of a base pair. Understanding this elementary rate process is the basis for RNA folding kinetics studies. Previous studies mostly focused on the unfolding of base pairs. Here, based on a hybrid approach, we investigate the folding process at level of single base pairing/stacking. The study, which integrates molecular dynamics simulation, kinetic Monte Carlo simulation, and master equation methods, uncovers two alternative dominant pathways: Starting from the unfolded state, the nucleotide backbone first folds to the native conformation, followed by subsequent adjustment of the base conformation. During the base conformational rearrangement, the backbone either retains the native conformation or switches to nonnative conformations in order to lower the kinetic barrier for base rearrangement. The method enables quantification of kinetic partitioning among the different pathways. Moreover, the simulation reveals several intriguing ion binding/ dissociation signatures for the conformational changes. Our approach may be useful for developing a base pair opening/closing rate model. RNAs perform critical cellular functions at the level of gene expression and regulation (1-4). RNA functions are determined not only by RNA structure or structure motifs [e.g., tetraloop hairpins (5, 6)] but also by conformational distributions and dynamics and kinetics of conformational changes. For example, riboswitches can adopt different conformations in response to specific conditions of the cellular environment (7,8). Understanding the kinetics, such as the rate and pathways for the conformational changes, is critical for deciphering the mechanism of RNA function (9-19). Extensive experimental and theoretical studies on RNA folding kinetics have provided significant insights into the kinetic mechanism of RNA functions (19-36). However, due to the complexity of the RNA folding energy landscape (37-46) and the limitations of experimental tools (47-55), many fundamental problems, including single base flipping and base pair formation and fraying, remain unresolved. These unsolved fundamental problems have hampered our ability to resolve other important issues, such as RNA hairpin and larger structure folding kinetics. Several key questions remain unanswered, such as whether the hairpin folding is rate-limited by the conformational search of the native base pairs, whose formation leads to fast downhill folding of the whole structure, or by the breaking of misfolded base pairs before refolding to the native structure (18,19,(54)(55)(56)(57)(58)(59)(60)(61)(62)(63)(64)(65)(66)(67)(68)(69)(70)(71)(72)(73).Motivated by the need to understand the basic steps of nucleic acids folding, Hagan et al. (74) performed forty-three 200-ps unfolding trajectories at 400 K and identified both on-and offpathway intermediates and two dominant unfolding pathways for a terminal C-G base pair in a DNA duplex. In one of the pathways, bas...

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confidence: 99%

Understanding the kinetic mechanism of RNA single base pair formation

Chen

2015

Proc. Natl. Acad. Sci. U.S.A.

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“…available within the global flooding barriers pipeline, is to ignore RNA structures above a global upper absolute energy bound (maxE), also discussed e.g. in (Hofacker et al, 2010;Kucharík et al, 2014). Due to the bellshaped density-of-states distribution of RNAs (see e.g.…”

Section: Maxe -Global Absolute Energy Boundmentioning

confidence: 99%

“…Entzian & Raden local minima and subsequently estimates the minimal energy barrier between them via direct folding pathway computations (Kucharík et al, 2014). While this enables kinetics predictions for longer RNAs, the procedure might miss transition states (Kucharík et al, 2014) and the dynamics can be distorted due to poor transition rate estimates based on saddle heights only (see supplementary material F).…”

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confidence: 99%

pourRNA—a time- and memory-efficient approach for the guided exploration of RNA energy landscapes

Entzian

Raden

2019

Bioinformatics

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Motivation The folding dynamics of ribonucleic acids (RNAs) are typically studied via coarse-grained models of the underlying energy landscape to face the exponential growths of the RNA secondary structure space. Still, studies of exact folding kinetics based on gradient basin abstractions are currently limited to short sequence lengths due to vast memory requirements. In order to compute exact transition rates between gradient basins, state-of-the-art approaches apply global flooding schemes that require to memorize the whole structure space at once. pourRNA tackles this problem via local flooding techniques where memorization is limited to the structure ensembles of individual gradient basins. Results Compared to the only available tool for exact gradient basin-based macro-state transition rates (namely barriers), pourRNA computes the same exact transition rates up to 10 times faster and requires two orders of magnitude less memory for sequences that are still computationally accessible for exhaustive enumeration. Parallelized computation as well as additional heuristics further speed up computations while still producing high-quality transition model approximations. The introduced heuristics enable a guided trade-off between model quality and required computational resources. We introduce and evaluate a macroscopic direct path heuristics to efficiently compute refolding energy barrier estimations for the co-transcriptionally trapped RNA sv11 of length 115 nt. Finally, we also show how pourRNA can be used to identify folding funnels and their respective energetically lowest minima. Availability and implementation pourRNA is freely available at https://github.com/ViennaRNA/pourRNA. Supplementary information Supplementary data are available at Bioinformatics online.

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“…Since barriers relies on an exhaustive enumeration of low energy structures, the computational effort grows exponentially with sequence size, which limits the length of RNAs that can be studied to a little over 100 nt. Recently, a number of heuristic approaches have been reported that attempt to raise this limit based on flooding techniques [34,24] or sampling of local minima [27,28,20,21].…”

Section: The Barriers Approachmentioning

confidence: 99%

Efficient computation of co-transcriptional RNA-ligand interaction dynamics

Wolfinger

Hofacker

2018

Preprint

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Riboswitches form an abundant class of cis-regulatory RNA elements that mediate gene expression by binding a small metabolite. For synthetic biology applications, they are becoming cheap and accessible systems for selectively triggering transcription or translation of downstream genes. Many riboswitches are kinetically controlled, hence knowledge of their co-transcriptional mechanisms is essential. We present here an efficient implementation for analyzing co-transcriptional RNA-ligand interaction dynamics. This approach allows for the first time to model concentration-dependent metabolite binding/unbinding kinetics. We exemplify this novel approach by means of the recently studied I-A 2'-deoxyguanosine (2'dG)-sensing riboswitch from Mesoplasma florum.Keywords: RNA-ligand interaction, RNA dynamics, co-transcriptional folding, energy landscape, riboswitch BackgroundRiboswitches are cis-acting regulatory RNAs that undergo an allosteric conformational switch upon binding of a cognate metabolite. They have originally been characterized in bacteria, where they are typically located in 5' untranslated regions (5'-UTR) of mRNAs. The regulatory repertoire of procaryotic riboswitches comprises modulation of the expression of adjacent genes by means of transcription termination or translation initiation. Riboswitches typically consist of two domains, an evolutionary conserved aptamer domain that specifically senses a metabolite and a variable expression platform that can form specific RNA structural elements required for modulation of gene expression [21,8]. The binary characteristics of a transcriptional ribsowitch, being either premature transcription termination (OFF-switch) or continuation (ON-switch) is triggered by metabolite binding. In this line, aptamer formation, ligand binding and subsequent formation of RNA structures in the expression platform (terminator / anti-terminator) are kinetically controlled co-transcriptional events [25].

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Basin Hopping Graph: a computational framework to characterize RNA folding landscapes

Abstract: The algorithms described here are implemented in C++ as standalone programs. Its source code and supplemental material can be freely downloaded from http://www.tbi.univie.ac.at/bhg.html.

Cited by 38 publications

References 49 publications

Understanding the kinetic mechanism of RNA single base pair formation

Understanding the kinetic mechanism of RNA single base pair formation

pourRNA—a time- and memory-efficient approach for the guided exploration of RNA energy landscapes

Efficient computation of co-transcriptional RNA-ligand interaction dynamics

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