Discrete energy landscapes provide a valuable means for analyzing non-equilibrium properties of biopolymers. RNA folding dynamics, for example, can be described by a continuous-time Markov process at the level of local minima, their corresponding basins of attraction and saddle points connecting them.

A connected set of structures, often denoted state space is required for energy landscape construction. While complete suboptimal folding of RNA is practically impossible for chain lengths above 100nt, alternative strategies to enumerate the lower part of the energy landscape emerged over the last years.

We have recently extended previous work on global flooding by a local flooding approach that minimizes memory consumption and published the method in Bioinformatics.

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


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 available at http://www.bioinf.uni-freiburg.de/Software/.