In this study we propose a memory-efficient approach for local flooding of the lower portion of an RNA folding landscape
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
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.