High Resolution Structure of the Large Ribosomal Subunit from a Mesophilic Eubacterium

Harms, Joerg; Schluenzen, Frank; Zarivach, Raz; Bashan, Anat; Gat, S.; Agmon, Ilana; Bartels, Heike; Franceschi, François; Yonath, Ada

doi:10.1016/s0092-8674(01)00546-3

Cited by 856 publications

(955 citation statements)

References 59 publications

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“…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. We then use the subset screening criterion; we reject those candidates for which: (17) Here S * is the corresponding sum for (p 1 ,…, p k ).…”

Section: Building Lists Of Partial Candidates and The Screening Algormentioning

confidence: 99%

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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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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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“…Studies into the composition and structure of prokaryotic ribosomes have been long-time undertakings and have culminated recently with high-resolution structures for bacterial (Schluenzen et al, 2000;Wimberly et al, 2000;Harms et al, 2001) and archeal (Ban et al, 2000) ribosomal subunits.…”

Section: Introductionmentioning

confidence: 99%

High heterogeneity within the ribosomal proteins of the Arabidopsis thaliana 80S ribosome

et al. 2005

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Proteomic studies have addressed the composition of plant chloroplast ribosomes and 70S ribosomes from the unicellular organism Chlamydomonas reinhardtii, but comprehensive characterization of cytoplasmic 80S ribosomes from higher plants has been lacking. We have used two-dimensional gel electrophoresis (2-DE) and mass spectrometry (MS) to analyse the cytoplasmic 80S ribosomes from the model flowering plant Arabidopsis thaliana. Of the 80 ribosomal protein families predicted to comprise the cytoplasmic 80S ribosome, we have confirmed the presence of 61; specifically, 27 (84%) of the small 40S subunit and 34 (71%) of the large 60S subunit. Nearly half (45%) of the ribosomal proteins identified are represented by two or more distinct spots in the 2-DE gel indicating that these proteins are either post-translationally modified or present as different isoforms. Consistently, MS-based protein identification revealed that at least one-third (34%) of the identified ribosomal protein families showed expression of two or more family members. In addition, we have identified a number of non-ribosomal proteins that co-migrate with the plant 80S ribosomes during gradient centrifugation suggesting their possible association with the 80S ribosomes. Among them, RACK1 has recently been proposed to be a ribosome-associated protein that promotes efficient translation in yeast. The study, thus provides the basis for further investigation into the function of the other identified non-ribosomal proteins as well as the biological meaning of the various ribosomal protein isoforms.

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“…Studies of RNA structure have revealed very few long-distance base-pair interactions beyond 800 nt. For example, in the 16S and 23S rRNA structures solved by X-ray crystallography, 99.9% of the pairings are of bases within 600 nt of each other in the primary sequence (Harms, Schluenzen et al 2001;Schuwirth, Borovinskaya et al 2005). Thus, the results from analysis of the accessibility of the Bcl2 coding region should reflect its accessibility in the full length Bcl2 mRNA given that the same structure is predicted of the coding region whether alone or in context of the 5' and 3' UTRs.…”

Section: Discussionmentioning

confidence: 99%

Plasminogen activator inhibitor 1: Mechanisms of its synergistic regulation by growth factors

Song

2010

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High Resolution Structure of the Large Ribosomal Subunit from a Mesophilic Eubacterium

Cited by 856 publications

References 59 publications

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

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

High heterogeneity within the ribosomal proteins of the Arabidopsis thaliana 80S ribosome

Plasminogen activator inhibitor 1: Mechanisms of its synergistic regulation by growth factors

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