Genome-scale characterization of RNA tertiary structures and their functional impact by RNA solvent accessibility prediction.
As most RNA structures are elusive to structure determination, obtaining solvent accessible surface areas (ASAs) of nucleotides in an RNA structure is an important first step to characterize potential functional sites and core structural regions. Here, we developed RNAsnap, the first machine-learning method trained on protein-bound RNA structures for solvent accessibility prediction. Built on sequence profiles from multiple sequence alignment (RNAsnap-prof), the method provided robust prediction in fivefold cross-validation and an independent test (Pearson correlation coefficients, r, between predicted and actual ASA values are 0.66 and 0.63, respectively). Application of the method to 6178 mRNAs revealed its positive correlation to mRNA accessibility by dimethyl sulphate (DMS) experimentally measured in vivo (r = 0.37) but not in vitro (r = 0.07), despite the lack of training on mRNAs and the fact that DMS accessibility is only an approximation to solvent accessibility. We further found strong association across coding and noncoding regions between predicted solvent accessibility of the mutation site of a single nucleotide variant (SNV) and the frequency of that variant in the population for 2.2 million SNVs obtained in the 1000 Genomes Project. Moreover, mapping solvent accessibility of RNAs to the human genome indicated that introns, 5' cap of 5' and 3' cap of 3' untranslated regions, are more solvent accessible, consistent with their respective functional roles. These results support conformational selections as the mechanism for the formation of RNA-protein complexes and highlight the utility of genome-scale characterization of RNA tertiary structures by RNAsnap. The server and its stand-alone downloadable version are available at http://sparks-lab.org.
Institute for Glycomics and School of Information and Communication Technology, Griffith University, Gold Coast, QLD 4222, Australia.
Yuedong
Y
Yang
Institute for Glycomics and School of Information and Communication Technology, Griffith University, Gold Coast, QLD 4222, Australia.|School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China.
Xiaomei
X
Li
Institute of Health and Biomedical Innovation, Queensland University of Technology, Queensland 4222, Australia.
Huiying
H
Zhao
Institute for Glycomics and School of Information and Communication Technology, Griffith University, Gold Coast, QLD 4222, Australia.
Jian
J
Zhan
Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou 253023, China.
Jihua
J
Wang
Institute for Glycomics and School of Information and Communication Technology, Griffith University, Gold Coast, QLD 4222, Australia.|Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou 253023, China.
Yaoqi
Y
Zhou
D019295:Computational Biology / Q000379:methods; D015894:Genome, Human; D006801:Humans; D000069550:Machine Learning; D008958:Models, Molecular; D008968:Molecular Conformation; D012313:RNA / Q000737:chemistry* / Q000235:genetics*; D012997:Solvents / Q000737:chemistry*
D012997:Solvents; D012313:RNA
1000 Genomes Project; RNA structure prediction; conformational selections; genetic variants; in vivo RNA structure; induced fit; single nucleotide variants; solvent accessibility