Crystal structure of a phage Twort group I ribozyme–product complex

Golden, Barbara L.; Kim, Hajeong; Chase, Elaine

doi:10.1038/nsmb868

Cited by 180 publications

(230 citation statements)

References 49 publications

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“…It was first identified by phylogeny in group I and II introns by Michel, Westhof, and colleagues and observed for the first time in the seminal crystal structure of the P4-P6 domain of the Tetrahymena group I intron, in which this motif reinforces the side-by-side packing architecture of RNA helices [84][85][86]. This motif and others are ubiquitous in the ribosome and other structured RNAs [87][88][89][90][91]. We wish to emphasize that information on motif partners and secondary structuredetermined from phylogeny -provides a powerful tool in modeling RNA structure.…”

Section: Simple Forces Underlying the Complex Behavior Of Rnamentioning

confidence: 81%

Unwinding RNA's secrets: advances in the biology, physics, and modeling of complex RNAs

Chu

Herschlag

2008

Current Opinion in Structural Biology

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SummaryThe rapid development of our understanding of the diverse biological roles fulfilled by non-coding RNA has motivated interest in the basic macromolecular behavior, structure, and function of RNA. We focus on two areas in the behavior of complex RNAs. First, we present advances in the understanding of how RNA folding is accomplished in vivo by presenting a mechanism for the action of DEAD-box proteins. Members of this family are intimately associated with almost all cellular processes involving RNA, mediating RNA structural rearrangements and chaperoning their folding. Next, we focus on advances in understanding and characterizing the basic biophysical forces that govern the folding of complex RNAs. Ultimately we expect that a confluence and synergy between these approaches will lead to profound understanding of RNA and its biology.The explosion in our knowledge about noncoding RNA (ncRNA) -RNA that is not translated into protein -has expanded interest in RNA beyond the traditional RNA community. Diverse ncRNAs include the recently-discovered riboswitches, whose novel gene-regulation function is induced by the binding of small metabolites [1]; most of the so-called "Human Accelerated Regions" (HAR) of the human genome, whose recent evolution may be linked to the emergence of the human brain [2]; and many ncRNAs of unknown function [3]. These discoveries underscore the need to understand the fundamental macromolecular behavior of RNA, its structure and dynamics, and how these RNAs are handled and exploited in biology.Study of catalytic RNAs, which allow detection of correct folding via activity measurements, has established basic thermodynamic and kinetic properties of RNA folding. Large catalytic RNAs such as the group I intron from Tetrahymena thermophila and the RNase P RNA from Bacillus subtilis fold at vastly different rates under different conditions upon addition of Mg 2+ and have been shown to fold via multiple parallel pathways, in which different molecules follow different routes with different rates and intermediates, to a common final folded structure [4,5]. These observations support the common view that RNAs traverse rugged energy landscapes as they fold [6,7]. However, little is known about the true shape of these

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Section: Simple Forces Underlying the Complex Behavior Of Rnamentioning

confidence: 81%

Unwinding RNA's secrets: advances in the biology, physics, and modeling of complex RNAs

Chu

Herschlag

2008

Current Opinion in Structural Biology

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“…From S (12) we obtain a list of (p, q) pairs for which S (12) (p, q) > 0, and so (p, q) satisfy the pairwise constraint for (1, 2). If m > 2, for each (p, q) pair, we find all possible third nucleotides r for which two additional constraints are met: (14) This results in a list of (p, q, r) triples from the RNA 3D structure file which satisfy all pairwise constraints for query motif nucleotides (1, 2, 3). Now we reject some of these partial candidates by applying the subset screening criterion (10) with I = {1, 2, 3}; we reject (p, q, r) triples for which (15) because we can be certain that any m-nucleotide candidate for which (p, q, r) correspond to ( (p 1 ,…, p k , q) for (1, 2,…, k, k + 1) provided that (16) so that all pairwise constraints between the k existing nucleotides and the one new nucleotide are met.…”

Section: Building Lists Of Partial Candidates and The Screening Algormentioning

confidence: 99%

“…The database of atomic-resolution RNA 3D structures is growing rapidly [6,7,11,21] and now includes ribozymes [1,14,25], ribosomal subunits [3,16,44] and intact 70S ribosomes [42]. The number, size, and complexity of these structures make manual analyses to find and classify recurrent RNA 3D motifs difficult and time-consuming.…”

Section: Introductionmentioning

confidence: 99%

FR3D: finding local and composite recurrent structural motifs in RNA 3D structures

et al. 2007

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New methods are described for finding recurrent three-dimensional (3D) motifs in RNA atomicresolution structures. Recurrent RNA 3D motifs are sets of RNA nucleotides with similar spatial arrangements. They can be local or composite. Local motifs comprise nucleotides that occur in the same hairpin or internal loop. Composite motifs comprise nucleotides belonging to three or more different RNA strand segments or molecules. We use a base-centered approach to construct efficient, yet exhaustive search procedures using geometric, symbolic, or mixed representations of RNA structure that we implement in a suite of MATLAB programs, "Find RNA 3D" (FR3D). The first modules of FR3D preprocess structure files to classify base-pair and -stacking interactions. Each base is represented geometrically by the position of its glycosidic nitrogen in 3D space and by the rotation matrix that describes its orientation with respect to a common frame. Base-pairing and basestacking interactions are calculated from the base geometries and are represented symbolically according to the Leontis/Westhof basepairing classification, extended to include base-stacking. These data are stored and used to organize motif searches. For geometric searches, the user supplies the 3D structure of a query motif which FR3D uses to find and score geometrically similar candidate motifs, without regard to the sequential position of their nucleotides in the RNA chain or the identity of their bases. To score and rank candidate motifs, FR3D calculates a geometric discrepancy by rigidly rotating candidates to align optimally with the query motif and then comparing the relative orientations of the corresponding bases in the query and candidate motifs. Given the growing size of the RNA structure database, it is impossible to explicitly compute the discrepancy for all conceivable candidate motifs, even for motifs with less than ten nucleotides. The screening algorithm that we describe finds all candidate motifs whose geometric discrepancy with respect to the query motif falls below a user-specified cutoff discrepancy. This technique can be applied to NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript RMSD searches. Candidate motifs identified geometrically may be further screened symbolically to identify those that contain particular basepair types or base-stacking arrangements or that conform to sequence continuity or nucleotide identity constraints. Purely symbolic searches for motifs containing user-defined sequence, continuity and interaction constraints have also been implemented. We demonstrate that FR3D finds all occurrences, both local and composite and with nucleotide substitutions, of sarcin/ricin and kink-turn motifs in the 23S and 5S ribosomal RNA 3D structures of the H. marismortui 50S ribosomal subunit and assigns the lowest discrepancy scores to bona fide examples of these motifs. The search algorithms have been optimized for speed to allow users to search the non-redundant RNA 3D structure database on a personal compute...

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“…32 Deletion or alteration of this functional group results in a complete loss of peptidyl transferase activity (>10 6 fold loss of activity). Without this hydroxyl group, the rate of spontaneous ester hydrolysis is faster than the rate of peptide bond formation.…”

Section: Ribosomal Peptide Bond Formation By Substrate Assistancementioning

confidence: 99%

RNA catalysis: ribozymes, ribosomes, and riboswitches

Strobel

Cochrane

2007

Current Opinion in Chemical Biology

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The catalytic mechanisms employed by RNA are chemically more diverse than initially suspected. Divalent metal ions, nucleobases, ribosyl hydroxyl groups, and even functional groups on metabolic cofactors all contribute in the various strategies employed by RNA enzymes. This catalytic breadth raises intriguing evolutionary questions about how RNA lost its biological role in some cases, but not in others, and what catalytic roles RNA might still be playing in biology.RNA is well-suited for its role as a purveyor of genetic information. The consistent hydrogen bonding potential of each nucleobase is critical for fidelity during replication, transcription and translation. It is easy to imagine evolutionary pressures that would select for residues with such properties. This is achieved in part because none of the nucleobases have ionizable groups near neutrality Although optimized for reliable base pairing, RNA is also able to catalyze essential biochemical reactions, including RNA processing and protein synthesis. It does this despite significant biophysical handicaps. RNA has a small repertoire of functional groups and these are embedded in a poorly-constrained ribosyl-phosphate backbone heavy with negative charge. The pK a s of the bases would appear to be either too low (3.5 for A, 4.2 for C) or too high (9.8 for G, 10.5 for U) for use in efficient general acid or base catalysis. As a group, RNA enzymes, or ribozymes, are less efficient catalysts relative to chemically supercharged proteins, yet in many cases ribozymes provide enough rate enhancement to have escaped replacement by protein alternatives in the evolution from an RNA World to the protein dominated world of modern biochemistry.In this review we will examine four examples of RNA catalysis, each of which provides a different chemical strategy used to promote a ribozyme reaction. This includes: the group I class of self-splicing introns which employs two divalent metal ions to promote RNA cleavage and ligation reactions; the Hepatitis Delta Virus ribozyme which catalyzes autolytic RNA cleavage using an essential, pK a perturbed cytidine; the glmS ribozyme which is an autolytic riboswitch that uses the small molecule metabolite glucosamine-6-phosphate (GlcN6P) as a catalytic cofactor; and the ribosomal peptidyl transferase center which doesn't appear to use any of these strategies, but instead forms peptide bonds with the assistance of a functional group on the tRNA substrate. The diversity of solutions employed by RNA to achieve reaction catalysis is unexpected; raising intriguing questions about the evolution and devolution of RNA catalysis. What led RNA to loose its catalytic role in some biological cases but not others, and what other catalytic functions RNA might still be playing in biology?

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Crystal structure of a phage Twort group I ribozyme–product complex

Cited by 180 publications

References 49 publications

Unwinding RNA's secrets: advances in the biology, physics, and modeling of complex RNAs

Unwinding RNA's secrets: advances in the biology, physics, and modeling of complex RNAs

FR3D: finding local and composite recurrent structural motifs in RNA 3D structures

RNA catalysis: ribozymes, ribosomes, and riboswitches

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