Random versus Deterministic Descent in RNA Energy Landscape Analysis.

Kathleen Steinhöfel,Luke Day,Andreas A Albrecht,Ouala Abdelhadi Ep Souki

doi:10.1155/2016/9654921

Kathleen Steinhöfel, Luke Day + Show 2 more

Open Access

https://doi.org/10.1155/2016/9654921

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Abstract

Identifying sets of metastable conformations is a major research topic in RNA energy landscape analysis, and recently several methods have been proposed for finding local minima in landscapes spawned by RNA secondary structures. An important and time-critical component of such methods is steepest, or gradient, descent in attraction basins of local minima. We analyse the speed-up achievable by randomised descent in attraction basins in the context of large sample sets where the size has an order of magnitude in the region of ~106. While the gain for each individual sample might be marginal, the overall run-time improvement can be significant. Moreover, for the two nongradient methods we analysed for partial energy landscapes induced by ten different RNA sequences, we obtained that the number of observed local minima is on average larger by 7.3% and 3.5%, respectively. The run-time improvement is approximately 16.6% and 6.8% on average over the ten partial energy landscapes. For the large sample size we selected for descent procedures, the coverage of local minima is very high up to energy values of the region where the samples were randomly selected from the partial energy landscapes; that is, the difference to the total set of local minima is mainly due to the upper area of the energy landscapes.

Highlights

There is a great diversity in recent research on RNA secondary structure predictions, including refinements of wellestablished methods such as Mfold [1] and RNAfold [2], kinetic folding simulations, modelling of cotranscriptional folding, and sampling techniques focussing on approximations of the partition function over all secondary structures or for metastable conformations
Since helices formed by the incomplete chain may be too stable to refold later on, cotranscriptional folding may drive the folding process to a well-defined folded state that is different from a minimum free energy conformation
We compare the performance of the three descent algorithms in terms of run-time performance and number of observed local minima

Summary

Introduction

There is a great diversity in recent research on RNA secondary structure predictions, including refinements of wellestablished methods such as Mfold [1] and RNAfold [2], kinetic folding simulations, modelling of cotranscriptional folding, and sampling techniques focussing on approximations of the partition function over all secondary structures or for metastable conformations. Since helices formed by the incomplete chain may be too stable to refold later on, cotranscriptional folding may drive the folding process to a well-defined folded state that is different from a minimum free energy conformation. In a recent experimental study, Solomatin et al [9] argue in favour of multiple RNA folding pathways to different biologically active conformations (where the authors include the wider perspective of protein folding)

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