3D RNA from evolutionary couplings

Abstract

Non-protein-coding RNAs are ubiquitous in cell physiology, with a diverse repertoire of known functions. In fact, the majority of the eukaryotic genome does not code for proteins, and thousands of conserved long non-protein-coding RNAs of currently unkown function have been identified. When available, knowledge of their 3D structure is very helpful in elucidating the function of these RNAs. However, despite some outstanding structure elucidation of RNAs using X-ray crystallography, NMR and cryoEM, learning RNA 3D structures remains lowthroughput. RNA structure prediction in silico is a promising alternative approach and works well for double-helical stems, but full 3D structure determination requires tertiary contacts outside of secondary structures that are difficult to infer from sequence information. Here, based only on information from RNA multiple sequence alignments, we use a global statistical sequence probability model of co-variation in a pairs of nucleotide positions to detect 3D contacts, in analogy to recently developed breakthrough methods for computational protein folding. In blinded tests on 22 known RNA structures ranging in size from 65 to 1800 nucleotides, the predicted contacts matched physical nucleotide interactions with 65-95% true positive prediction accuracy. Importantly, we infer many long-range tertiary contacts, including non-Watson-Crick interactions, where secondary structure elements assemble in 3D. When used as restraints in molecular dynamics simulations, the inferred contacts improve RNA 3D structure prediction to a coordinate error as low as 6 -10 Å rmsd deviation in atom positions, with potential for further refinement by molecular dynamics. These contacts include functionally important interactions, such as those that distinguish the active and inactive conformations of four riboswitches. In blind prediction mode, we present evolutionary couplings suitable for folding simulations for 180 RNAs of unknown structure, available at https://marks.hms.harvard.edu/ev_rna/. We anticipate that this approach can help shed light on the structure and function of non-protein-coding RNAs as well as 3D-structured mRNAs.

RNA 3D structure prediction using multiple sequence alignment information

“…The approach described here could be combined with direct-coupling analysis, proposed for example by [24] and [12]. In this approach, a DCA analysis should be performed for an alignment to generate restraints for several homologous sequences.…”

“…To evaluate the performance of the presented methodology, we compiled a dataset of 8 RNA sequences: five RNA sequences from [24]: Adenine riboswitch (Ade, PDB ID: 1Y26, RFAM ID: RF00167) [26], Thiamine pyrophosphate-sensing riboswitch (TPP, PDB ID: 2GDI, RFAM ID: RF00059) [27] Multiple sequence alignment generation and selection of homologs Each query sequence was taken from the corresponding PDB file. The MSA was obtained from the Rfam database [32] and in case of the Pistol ribozyme, the MSA was published as the supplementary data provided by [19].…”