An Unbiased Adaptive Sampling Algorithm for the Exploration of RNA Mutational Landscapes under Evolutionary Pressure

The analysis of the impact of mutations on folding properties of RNAs is essential to decipher principles driving molecular evolution and to design new molecules. We recently introduced an algorithm called RNAmutants which samples RNA sequence-structure maps in polynomial time and space. However, since the mutation probabilities depend of the free energy of the structures, RNAmutants is bias toward G+C-rich regions of the mutational landscape. In this paper we introduce an unbiased adaptive sampling algorithm that enables RNAmutants to sample regions of the mutational landscape poorly covered by previous techniques. We applied the method to sample mutations in complete RNA sequence-structures maps of sizes up to 40 nucleotides. Our results indicate that the G+C-contents has a strong influence on the evolutionary accessible structural ensembles. In particular, we show that low G+C-contents favor the apparition of internal loops, while high G+C-contents reduce the size of the evolutionary accessible mutational landscapes.