Hierarchical assembly of single-stranded RNA

Abstract

Single-stranded RNA (ssRNA) plays a major role in the flow of genetic information- most notably in the form of messenger RNA (mRNA)-and in the regulation of biological processes. The highly dynamic nature of chains of unpaired nucleobases challenges structural characterizations of ssRNA by experiments or molecular dynamics (MD) simulations alike. Here we use hierarchical chain growth (HCG) to construct ensembles of ssRNA chains. HCG assembles the structures of protein and nucleic acid chains from fragment libraries created by MD simulations. Applied to homo- and heteropolymeric ssRNAs of different lengths, we find that HCG produces structural ensembles that overall are in good agreement with diverse experiments including nuclear magnetic resonance (NMR), small-angle X-ray scattering (SAXS), and single-molecule Forster resonance energy transfer (FRET). The agreement can be further improved by ensemble refinement using Bayesian inference of ensembles (BioEn). HCG can also be used to assemble RNA structures that combine base-paired and unpaired regions, as illustrated for the 51 untranslated region (UTR) of SARS-CoV-2 mRNA.