Structural Constraints Identified with Covariation Analysis in Ribosomal RNA

Abstract

Covariation analysis is used to identify those positions with similar patterns of sequence variation in an alignment of RNA sequences. These constraints on the evolution of two positions are usually associated with a base pair in a helix. While mutual information (MI) has been used to accurately predict an RNA secondary structure and a few of its tertiary interactions, early studies revealed that phylogenetic event counting methods are more sensitive and provide extra confidence in the prediction of base pairs. We developed a novel and powerful phylogenetic events counting method (PEC) for quantifying positional covariation with the Gutell lab’s new R NA C omparative A nalysis D atabase (rCAD). The PEC and MI-based methods each identify unique base pairs, and jointly identify many other base pairs. In total, both methods in combination with an N-best and helix-extension strategy identify the maximal number of base pairs. While covariation methods have effectively and accurately predicted RNAs secondary structure, only a few tertiary structure base pairs have been identified. Analysis presented herein and at the Gutell lab’s Comparative RNA Web (CRW) Site reveal that the majority of these latter base pairs do not covary with one another. However, covariation analysis does reveal a weaker although significant covariation between sets of nucleotides that are in proximity in the three-dimensional RNA structure. This reveals that covariation analysis identifies other types of structural constraints beyond the two nucleotides that form a base pair.

Universal and domain-specific sequences in 23S–28S ribosomal RNA identified by computational phylogenetics

“…Conformational changes in the ribosome during translation reflect changes in tertiary interactions, whereas secondary structure interactions remain relatively stable (Schmeing and Ramakrishnan 2009). Secondary structure interactions are maintained by covariation where compensatory base changes retain the helical structure; in contrast, the majority of nucleotides involved in tertiary interactions do not covary with one another (Shang et al 2012). The approach we describe here has the power to identify conserved sequences in rRNA that can be of functional importance, including in those conformers of the ribosome not yet visualized by X-ray crystallography.…”

Ten lessons with Carl Woese about RNA and comparative analysis

“…25 This phylogenetic covariation events program utilizes rCAD, RNA Comparative Analysis Database, a novel database system developed in collaboration with Microsoft Research (see Lesson Ten). 26 As expected, this method has the greatest sensitivity since it identifies and scores Carl's evolutionary event counts (see Fig.…”

“…And usually within these networks, the positions that form traditional base pairs have the highest covariation score with one another. 24,25,34 Two examples of networks of covariations have been found in tRNA. The first contains nucleotides that are in proximity in the three-dimensional structure.…”

“…Other examples of these types of clusters have been observed in the group I introns and the rRNAs. 24,25,34 Several of these, including this example in the D stem of tRNA, have several base triples in the same helix. Other networks occur in hairpin loops such as the tetraloop.…”

“…We have aligned sequences with template-based methods with the highest accuracy, 58,59 developed a true phylogenetic event counting method to identify covariation in RNA alignments, 25 developed structural statistics from our mapping of RNA structural elements onto sequence alignments, and utilized rCAD to create structural potentials in place of experimentally derived energy values that resulted in the prediction of some RNA secondary structures (from a single sequence) with higher accuracy. 60 At a microcosm level, this rCAD example does reveal that the enhanced data organization and cross-indexing information creates opportunities for new analysis and discovery.…”