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RESEARCH HIGHLIGHTS

STRUCTURAL RNA structure from sequence

An approach taken from poor sampling, can be higher than signal structure prediction makes it possible to from true contacts, Marks adds. computationally identify secondary and To make headway into RNA structure UGUUUGCAGCAAGAC tertiary contacts in RNA. ACUCUGCAGCAGGAU prediction, the team adapted a global evo- UGGAUUGAGCAUGCU Discoveries of new RNA species con- lutionary couplings model that the Marks UAUAUCUGAGAUACU tinue apace, presenting the noncoding lab had pioneered to predict UGUGUGCGACACUCC UUUCGCUAGCAUACU RNA field with a growing question: what contacts in . “It was a surprise even UGUUUCUUGCAUGCU do these molecules do? The functions for proteins, so we thought why not try it,”

and molecular interactions of RNA often Marks recalls. The global model considers University Harvard C. Weinreb, depend on its three-dimensional struc- all possible pairwise contacts in an RNA Bases at RNA structural contact points covary ture. Researchers have long tried to divine molecule together, allowing it to deconvolve across evolution. structure directly from sequence, but transitivity. RNA is notoriously flexible, requiring that The researchers used it to generate a wide countless possible spatial configurations be range of contact predictions, which they conserved among the vertebrates, for exam- sifted through to find the right structure. fed in some cases into modeling software ple, with too few sequences currently avail- One strategy uses evolutionary pat- to create all-atom models. They achieved able for robust prediction. But as there are terns to pinpoint residue combinations good precision for 22 RNA families, includ- only four nucleotides for RNA, compared to that have undergone selection for their ing the long 40S ribosomal RNA, that had 20 amino acids for proteins, RNA prediction roles in folding or function. This sequence been worked out with painstaking crystal- often requires less than an order of magni- covariation approach asks whether pairs of lography and nuclear magnetic resonance tude fewer sequences for prediction. Marks nucleotides change in tandem at specific spectroscopy. They also predicted contacts notes that the level of divergence between positions of aligned RNA from different in riboswitches, tRNA, RNase P and 160 sequences can also be as important as the

Nature America, Inc. All rights reserved. America, Inc. © 201 6 Nature , and it has been useful for find- RNAs of unknown structure, among them number of sequences: “It depends on how ing secondary structure—the presence of a long noncoding RNA. In a striking case, much evolution has sampled and how much Watson–Crick base pairs. But scientists they found that only one of two published evolution we’ve sampled,” she says. have had little faith that it could be used to HIV Rev response element (RRE) structures High-throughput experimental meth- npg find tertiary contacts such as long-distance had strong support. In addition to finding ods for probing RNA contacts are improv- couplings and alternative base interactions. long-range couplings, the was in ing, but they indirectly detect base-pairing “The dogma was that RNA can only make fact better at predicting secondary structure and not tertiary contacts, their resolution is one friend because it either base pairs or it than local sequence covariation methods typically limited to 5–10 nucleotides, and doesn’t do anything,” says Deborah Marks, such as mutual information. contacts are not necessarily enriched for a computational at Harvard Couplings between RNA-binding pro- functional significance. With evolution- University. teins and RNA can also be used in dock- ary couplings, “you get information about A key problem is that traditional ing to give structures of the complex and which pairs were important in evolution approaches test for the correlation of each detailed sites of RNA–protein interaction. for folding or constraints,” says Marks. The residue pair independently, making them The most highly co-evolving contacts researchers are working on unpacking the vulnerable to confounding by transitiv- between Rev protein and RRE were predict- parameters that affect coupling strength in ity. Caleb Weinreb, a graduate student in ed to be precisely where Rev binding initi- order to derive quantitative information the Marks lab, gives a minimal example of ates an oligomerization event essential for about the potential impact of mutations. transitivity. “You have three bases, A, B and nuclear export and viral function—a dem- By making their efficient prediction tool C, and A and B are genuinely co-evolved onstration that the approach finds function- freely available, Marks and Weinreb hope to because of biochemical contact, as are B ally relevant constraints. make RNA structure prediction a routine and C. But because they’re both co-evolving Weinreb sees a direction for improving step in exploring RNA function. in the same sequences simultaneously, you performance. “The biggest limiting factor Tal Nawy get a spurious correlation between A and for us was the bioinformatics challenge of RESEARCH PAPERS C,” he explains. Spurious signal, which can finding enough RNA and protein sequenc- Weinreb, C. et al. 3D RNA and functional interactions be due to shared evolutionary history or es,” he says. Many long noncoding RNAs are from evolutionary couplings. 165, 1–13 (2016).

NATURE METHODS | VOL.13 NO.6 | JUNE 2016 | 465