Kevin M. Weeks

Architecture and secondary structure of an entire HIV-1 RNA genome.

Single-stranded RNA viruses encompass broad classes of infectious agents and cause the common cold, cancer, AIDS and other serious health threats. Viral replication is regulated at many levels, including the use of conserved genomic RNA structures. Most potential regulatory elements in viral RNA genomes are uncharacterized. Here we report the structure of an entire HIV-1 genome at single nucleotide resolution using SHAPE, a high-throughput RNA analysis technology. The genome encodes protein structure at two levels. In addition to the correspondence between RNA and protein primary sequences, a correlation exists between high levels of RNA structure and sequences that encode inter-domain loops in HIV proteins. This correlation suggests that RNA structure modulates ribosome elongation to promote native protein folding. Some simple genome elements previously shown to be important, including the ribosomal gag-pol frameshift stem-loop, are components of larger RNA motifs. We also identify organizational principles for unstructured RNA regions, including splice site acceptors and hypervariable regions. These results emphasize that the HIV-1 genome and, potentially, many coding RNAs are punctuated by previously unrecognized regulatory motifs and that extensive RNA structure constitutes an important component of the genetic code.

Selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE): quantitative RNA structure analysis at single nucleotide resolution

Selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE) interrogates local backbone flexibility in RNA at single-nucleotide resolution under diverse solution environments. Flexible RNA nucleotides preferentially sample local conformations that enhance the nucleophilic reactivity of 2′-hydroxyl groups toward electrophiles, such as N-methylisatoic anhydride (NMIA). Modified sites are detected as stops in an optimized primer extension reaction, followed by electrophoretic fragment separation. SHAPE chemistry scores local nucleotide flexibility at all four ribonucleotides in a single experiment and discriminates between base-paired versus unconstrained or flexible residues with a dynamic range of 20-fold or greater. Quantitative SHAPE reactivity information can be used to establish the secondary structure of an RNA, to improve the accuracy of structure prediction algorithms, to monitor structural differences between related RNAs or a single RNA in different states, and to detect ligand binding sites. SHAPE chemistry rarely needs significant optimization and requires two days to complete for an RNA of 100–200 nucleotides.Note: In the version of this article initially published online, the value for the pH range in the second paragraph on page 2 was incorrect; it should have read 7.5–8.2. In addition, two sentences were included that should have been removed from page 1. These errors have been corrected in all versions of the article.

Accurate SHAPE-directed RNA structure determination

Almost all RNAs can fold to form extensive base-paired secondary structures. Many of these structures then modulate numerous fundamental elements of gene expression. Deducing these structure–function relationships requires that it be possible to predict RNA secondary structures accurately. However, RNA secondary structure prediction for large RNAs, such that a single predicted structure for a single sequence reliably represents the correct structure, has remained an unsolved problem. Here, we demonstrate that quantitative, nucleotide-resolution information from a SHAPE experiment can be interpreted as a pseudo-free energy change term and used to determine RNA secondary structure with high accuracy. Free energy minimization, by using SHAPE pseudo-free energies, in conjunction with nearest neighbor parameters, predicts the secondary structure of deproteinized Escherichia coli 16S rRNA (>1,300 nt) and a set of smaller RNAs (75–155 nt) with accuracies of up to 96–100%, which are comparable to the best accuracies achievable by comparative sequence analysis.

High-throughput SHAPE analysis reveals structures in HIV-1 genomic RNA strongly conserved across distinct biological states

Replication and pathogenesis of the human immunodeficiency virus (HIV) is tightly linked to the structure of its RNA genome, but genome structure in infectious virions is poorly understood. We invent high-throughput SHAPE (selective 2′-hydroxyl acylation analyzed by primer extension) technology, which uses many of the same tools as DNA sequencing, to quantify RNA backbone flexibility at single-nucleotide resolution and from which robust structural information can be immediately derived. We analyze the structure of HIV-1 genomic RNA in four biologically instructive states, including the authentic viral genome inside native particles. Remarkably, given the large number of plausible local structures, the first 10% of the HIV-1 genome exists in a single, predominant conformation in all four states. We also discover that noncoding regions functioning in a regulatory role have significantly lower (p-value < 0.0001) SHAPE reactivities, and hence more structure, than do viral coding regions that function as the template for protein synthesis. By directly monitoring protein binding inside virions, we identify the RNA recognition motif for the viral nucleocapsid protein. Seven structurally homologous binding sites occur in a well-defined domain in the genome, consistent with a role in directing specific packaging of genomic RNA into nascent virions. In addition, we identify two distinct motifs that are targets for the duplex destabilizing activity of this same protein. The nucleocapsid protein destabilizes local HIV-1 RNA structure in ways likely to facilitate initial movement both of the retroviral reverse transcriptase from its tRNA primer and of the ribosome in coding regions. Each of the three nucleocapsid interaction motifs falls in a specific genome domain, indicating that local protein interactions can be organized by the long-range architecture of an RNA. High-throughput SHAPE reveals a comprehensive view of HIV-1 RNA genome structure, and further application of this technology will make possible newly informative analysis of any RNA in a cellular transcriptome.

Advances in RNA structure analysis by chemical probing

RNA is arguably the most versatile biological macromolecule because of its ability both to encode and to manipulate genetic information. The diverse roles of RNA depend on its ability to fold back on itself to form biologically functional structures that bind small molecule and large protein ligands, to change conformation, and to affect the cellular regulatory state. These features of RNA biology can be structurally interrogated using chemical mapping experiments. The usefulness and applications of RNA chemical probing technologies have expanded dramatically over the past five years because of several critical advances. These innovations include new sequence-independent RNA chemistries, algorithmic tools for high-throughput analysis of complex data sets composed of thousands of measurements, new approaches for interpreting chemical probing data for both secondary and tertiary structure prediction, facile methods for following time-dependent processes, and the willingness of individual research groups to tackle increasingly bold problems in RNA structural biology.

SHAPE-Directed RNA Secondary Structure Prediction

The diverse functional roles of RNA are determined by its underlying structure. Accurate and comprehensive knowledge of RNA structure would inform a broader understanding of RNA biology and facilitate exploiting RNA as a biotechnological tool and therapeutic target. Determining the pattern of base pairing, or secondary structure, of RNA is a first step in these endeavors. Advances in experimental, computational, and comparative analysis approaches for analyzing secondary structure have yielded accurate structures for many small RNAs, but only a few large (>500 nts) RNAs. In addition, most current methods for determining a secondary structure require considerable effort, analytical expertise, and technical ingenuity. In this review, we outline an efficient strategy for developing accurate secondary structure models for RNAs of arbitrary length. This approach melds structural information obtained using SHAPE chemistry with structure prediction using nearest-neighbor rules and the dynamic programming algorithm implemented in the RNAstructure program. Prediction accuracies reach >or=95% for RNAs on the kilobase scale. This approach facilitates both development of new models and refinement of existing RNA structure models, which we illustrate using the Gag-Pol frameshift element in an HIV-1 M-group genome. Most promisingly, integrated experimental and computational refinement brings closer the ultimate goal of efficiently and accurately establishing the secondary structure for any RNA sequence.

Accurate SHAPE-directed RNA secondary structure modeling, including pseudoknots

A pseudoknot forms in an RNA when nucleotides in a loop pair with a region outside the helices that close the loop. Pseudoknots occur relatively rarely in RNA but are highly overrepresented in functionally critical motifs in large catalytic RNAs, in riboswitches, and in regulatory elements of viruses. Pseudoknots are usually excluded from RNA structure prediction algorithms. When included, these pairings are difficult to model accurately, especially in large RNAs, because allowing this structure dramatically increases the number of possible incorrect folds and because it is difficult to search the fold space for an optimal structure. We have developed a concise secondary structure modeling approach that combines SHAPE (selective 2′-hydroxyl acylation analyzed by primer extension) experimental chemical probing information and a simple, but robust, energy model for the entropic cost of single pseudoknot formation. Structures are predicted with iterative refinement, using a dynamic programming algorithm. This melded experimental and thermodynamic energy function predicted the secondary structures and the pseudoknots for a set of 21 challenging RNAs of known structure ranging in size from 34 to 530 nt. On average, 93% of known base pairs were predicted, and all pseudoknots in well-folded RNAs were identified.

RNA motif discovery by SHAPE and mutational profiling (SHAPE-MaP)

Many biological processes are RNA-mediated, but higher-order structures for most RNAs are unknown, which makes it difficult to understand how RNA structure governs function. Here we describe selective 2′-hydroxyl acylation analyzed by primer extension and mutational profiling (SHAPE-MaP) that makes possible de novo and large-scale identification of RNA functional motifs. Sites of 2′-hydroxyl acylation by SHAPE are encoded as noncomplementary nucleotides during cDNA synthesis, as measured by massively parallel sequencing. SHAPE-MaP–guided modeling identified greater than 90% of accepted base pairs in complex RNAs of known structure, and we used it to define a new model for the HIV-1 RNA genome. The HIV-1 model contains all known structured motifs and previously unknown elements, including experimentally validated pseudoknots. SHAPE-MaP yields accurate and high-resolution secondary-structure models, enables analysis of low-abundance RNAs, disentangles sequence polymorphisms in single experiments and will ultimately democratize RNA-structure analysis.

RNA-Puzzles: A CASP-like evaluation of RNA three-dimensional structure prediction

We report the results of a first, collective, blind experiment in RNA three-dimensional (3D) structure prediction, encompassing three prediction puzzles. The goals are to assess the leading edge of RNA structure prediction techniques; compare existing methods and tools; and evaluate their relative strengths, weaknesses, and limitations in terms of sequence length and structural complexity. The results should give potential users insight into the suitability of available methods for different applications and facilitate efforts in the RNA structure prediction community in ongoing efforts to improve prediction tools. We also report the creation of an automated evaluation pipeline to facilitate the analysis of future RNA structure prediction exercises.

Protein-facilitated RNA folding

In the absence of protein collaborators, both simple and complex RNAs often misfold or are unfolded. Biologically important RNAs solve their folding problem, in part, using the assistance of chaperone and cofactor proteins. Recent work emphasizes several rules for RNA-protein complexes: formation involves induced fit; many large RNAs fold slowly; and ribonucleoprotein assembly requires multiple steps. Finally, protein binding can introduce thermodynamic side effects.