Jun 12 2014 How to efficiently explore the lower portion of RNA energy landscapes

Discrete energy landscapes provide a valuable means for analyzing non-equilibrium properties of biopolymers. In this study we propose a memory-efficient approach for local flooding of the lower portion of an RNA folding landscape

The dynamic process of RNA folding can be modeled as a continuous-time Markov process, focusing on local minima, their associated basins of attraction, and the saddle points that connect them.

To construct an energy landscape, a connected set of RNA structures, commonly referred to as the state space, is required, along with a neighborhood relation between these states and an assigned energy or fitness value. Although obtaining the complete suboptimal folding for RNA sequences longer than 100 nucleotides is computationally impractical, various strategies have been developed to explore the lower-energy portion of the landscape.

In this work, we introduce a local variant of our previous global flooding approach to energy landscapes. This localized flooding technique significantly reduces memory usage, allowing for the analysis of energy landscapes for longer RNA sequences.

Abstract

Motivation: Energy landscapes provide a valuable means for studying the folding dynamics of short RNA molecules in detail by modeling all possible structures and their transitions. Higher abstraction levels based on a macro-state decomposition of the landscape enable the study of larger systems; however, they are still restricted by huge memory requirements of exact approaches.

Results: We present a highly parallelizable local enumeration scheme that enables the computation of exact macro-state transition models with highly reduced memory requirements. The approach is evaluated on RNA secondary structure landscapes using a gradient basin definition for macro-states. Furthermore, we demonstrate the need for exact transition models by comparing two barrier-based approaches, and perform a detailed investigation of gradient basins in RNA energy landscapes.

Availability and implementation: Source code is part of the C++ Energy Landscape Library.

Citation

Memory-efficient RNA energy landscape exploration
Martin Mann, Marcel Kucharík, Christoph Flamm, Michael T. Wolfinger
Bioinformatics 30(18):2584-2591 (2014) | doi: 10.1093/bioinformatics/btu337 | PDF

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