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      Automated 3D structure composition for large RNAs

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          Abstract

          Understanding the numerous functions that RNAs play in living cells depends critically on knowledge of their three-dimensional structure. Due to the difficulties in experimentally assessing structures of large RNAs, there is currently great demand for new high-resolution structure prediction methods. We present the novel method for the fully automated prediction of RNA 3D structures from a user-defined secondary structure. The concept is founded on the machine translation system. The translation engine operates on the RNA FRABASE database tailored to the dictionary relating the RNA secondary structure and tertiary structure elements. The translation algorithm is very fast. Initial 3D structure is composed in a range of seconds on a single processor. The method assures the prediction of large RNA 3D structures of high quality. Our approach needs neither structural templates nor RNA sequence alignment, required for comparative methods. This enables the building of unresolved yet native and artificial RNA structures. The method is implemented in a publicly available, user-friendly server RNAComposer. It works in an interactive mode and a batch mode. The batch mode is designed for large-scale modelling and accepts atomic distance restraints. Presently, the server is set to build RNA structures of up to 500 residues.

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          Comparison of the predicted and observed secondary structure of T4 phage lysozyme.

          B W Matthews (1975)
          Predictions of the secondary structure of T4 phage lysozyme, made by a number of investigators on the basis of the amino acid sequence, are compared with the structure of the protein determined experimentally by X-ray crystallography. Within the amino terminal half of the molecule the locations of helices predicted by a number of methods agree moderately well with the observed structure, however within the carboxyl half of the molecule the overall agreement is poor. For eleven different helix predictions, the coefficients giving the correlation between prediction and observation range from 0.14 to 0.42. The accuracy of the predictions for both beta-sheet regions and for turns are generally lower than for the helices, and in a number of instances the agreement between prediction and observation is no better than would be expected for a random selection of residues. The structural predictions for T4 phage lysozyme are much less successful than was the case for adenylate kinase (Schulz et al. (1974) Nature 250, 140-142). No one method of prediction is clearly superior to all others, and although empirical predictions based on larger numbers of known protein structure tend to be more accurate than those based on a limited sample, the improvement in accuracy is not dramatic, suggesting that the accuracy of current empirical predictive methods will not be substantially increased simply by the inclusion of more data from additional protein structure determinations.
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            The MC-Fold and MC-Sym pipeline infers RNA structure from sequence data.

            Marc Parisien, François Major (2008)
            The classical RNA secondary structure model considers A.U and G.C Watson-Crick as well as G.U wobble base pairs. Here we substitute it for a new one, in which sets of nucleotide cyclic motifs define RNA structures. This model allows us to unify all base pairing energetic contributions in an effective scoring function to tackle the problem of RNA folding. We show how pipelining two computer algorithms based on nucleotide cyclic motifs, MC-Fold and MC-Sym, reproduces a series of experimentally determined RNA three-dimensional structures from the sequence. This demonstrates how crucial the consideration of all base-pairing interactions is in filling the gap between sequence and structure. We use the pipeline to define rules of precursor microRNA folding in double helices, despite the presence of a number of presumed mismatches and bulges, and to propose a new model of the human immunodeficiency virus-1 -1 frame-shifting element.
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              Selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE): quantitative RNA structure analysis at single nucleotide resolution.

              Kevin Wilkinson, Edward Merino, Kevin Weeks (2006)
              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.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Nucleic Acids Res
                Journal ID (iso-abbrev): Nucleic Acids Res
                Journal ID (publisher-id): nar
                Journal ID (hwp): nar
                Title: Nucleic Acids Research
                Publisher: Oxford University Press
                ISSN (Print): 0305-1048
                ISSN (Electronic): 1362-4962
                Publication date Collection: August 2012
                Publication date (Print): August 2012
                Publication date (Electronic): 26 April 2012
                Publication date PMC-release: 26 April 2012
                Volume: 40
                Issue: 14
                Page: e112
                Affiliations
                1Laboratory of Structural Chemistry of Nucleic Acids, 2Laboratory of Bioinformatics, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan 61-704 and 3Institute of Computing Science, Poznan University of Technology, Poznan 60-965, Poland
                Author notes
                *To whom correspondence should be addressed. Tel: +48 61 8528503; Fax: +48 61 8520532; Email: adamiakr@ 123456ibch.poznan.pl
                Article
                Publisher ID: gks339
                DOI: 10.1093/nar/gks339
                PMC ID: 3413140
                PubMed ID: 22539264
                SO-VID: 5946e38f-cb7d-48a4-afc7-9aa8cfbcf3a2
                Copyright © © The Author(s) 2012. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 14 January 2012
                Date revision received : 5 April 2012
                Date accepted : 5 April 2012
                Page count
                Pages: 12
                Categories
                Subject: Methods Online

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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