While RNA folding was originally seen as a simple problem to solve, it has been shown that the promiscuous interactions of the nucleobases result in structural polymorphism, with several competing structures generally observed for non-coding RNA. This inherent complexity limits our understanding of these molecules from experiments alone, and computational methods are commonly used to study RNA. Here, we discuss three advanced sampling schemes, namely Hamiltonian-replica exchange molecular dynamics (MD), ratchet-and-pawl MD and discrete path sampling, as well as the HiRE-RNA coarse-graining scheme, and highlight how these approaches are complementary with reference to recent case studies. While all computational methods have their shortcomings, the plurality of simulation methods leads to a better understanding of experimental findings and can inform and guide experimental work on RNA polymorphism.

RNA molecules play many functional and regulatory roles in cells, and hence, have gained considerable traction in recent times as therapeutic interventions. Within drug discovery, structure-based approaches have successfully identified potent and selective small-molecule modulators of pharmaceutically relevant protein targets. Here, we embrace the perspective of computational chemists who use these traditional approaches, and we discuss the challenges of extending these methods to target RNA molecules. In particular, we focus on recognition between RNA and small-molecule binders, on selectivity, and on the expected properties of RNA ligands.

Selective DNA binding by transcription factors (TFs) is crucial for the correct regulation of DNA transcription. In healthy cells, promoters of active genes are hypomethylated. A single CpG methylation within a TF response element (RE) may change the binding preferences of the protein, thus causing the dysregulation of transcription programs. Here, we investigate a molecular mechanism driving the downregulation of the NDUFA13 gene, due to hypermethylation, which is associated with multiple cancers. Using bioinformatic analyses of breast cancer cell line MCF7, we identify a hypermethylated region containing the binding sites of two TFs dimers, CEBPB and E2F1-DP1, located 130 b.p. from the gene transcription start site. All-atom extended MD simulations of wild type and methylated DNA alone and in complex with either one or both TFs dimers provide mechanistic insights into the cooperative asymmetric binding order of the two dimers; the CEBPB binding should occur first to facilitate the E2F1-DP1–DNA association. The CpG methylation within the E2F1-DP1 RE and the linker decrease the cooperativity effects and renders the E2F1-DP1 binding site less recognizable by the TF dimer. Taken together, the identified CpG methylation site may contribute to the downregulation of the NDUFA13 gene.