The Detection of Architectural Modules in RNA sequences and the RNA-Puzzles Modeling Contest

This keynote lecture was given during the ECCB'14 conference, Wednesday, September 10, 2014, Palais de la Musique et des Congrès, Strasbourg, France. Professor Eric Westhof was introduced by Dr. Olivier Poch, CNRS Research Director, Strasbourg, France.

RNA molecules are characterized by the formation of hydrogen-bonded pairs between the bases along the polymer. All base-base interactions present in nucleic acids, with at least two ‚Äústandard‚ÄĚ H-bonds, can be classified in twelve families where each family is a 4x4 matrix of the usual bases. The common Watson-Crick pairs belong to one of these families and the other eleven families gather the non-Watson-Crick pairs. The Watson-Crick pairs form the secondary structure and all the other families are critical for the tertiary structure. RNA architecture is thus viewed as the hierarchical assembly of preformed double-stranded helices defined by Watson-Crick base pairs and RNA modules maintained by non-Watson-Crick base pairs. RNA modules are recurrent ensemble of ordered non-Watson-Crick base pairs. Such RNA modules constitute a signal for detecting structured non-coding RNAs with specific biological functions. It is, therefore, important to be able to recognize such elements within genomes. Through systematic comparisons between homologous sequences and x-ray structures, followed by automatic clustering, the whole range of sequence diversity in recurrent RNA modules has been characterized. These data permitted the construction of a computational pipeline for identifying known 3D structural modules in single and multiple RNA sequences in the absence of any other information. Any module can in principle be searched, but four can be searched automatically: the G-bulged loop, the Kink-turn, the C-loop and the tandem GA loop. The present pipeline can be used for RNA 2D structure refinement, 3D model assembly, and for searching and annotating structured RNAs. RNA-Puzzles are collective and blind experiments in RNA three-dimensional structure prediction. 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 their efforts to improve their tools. Generally, the less well predicted models always had worse non-Watson-Crick scores, demonstrating the importance of identifying non-Watson-Crick pairs and RNA modules.

 


Eric Westhof

University of Strasbourg, Strasbourg, France 
Professor, Director of the Institute of Molecular and Cellular Biology, CNRS

Bio: Eric Westhof received his Ph.D. in Biophysics in 1974 (Liège University, Belgium) after graduate work at Regensburg University, Germany. In 1977, as a FULBRIGHT-HAYS Research Fellow, he joined the Department of Biochemistry, University of Wisconsin (Madison, USA) to work with M. Sundaralingam in crystallography of nucleic acids. In 1981, with a EMBO post-doctoral fellowship, he moved to Strasbourg to work with Dino Moras on transfer RNA crystals. In 1988, he became Professor of Structural Biochemistry at the University of Strasbourg. Since 2006, he is Director of the Institut de Biologie Moléculaire et Cellulaire and head of the unit Architecture et Réactivité de l’ARN of the CNRS. He is an executive editor of RNA Journal and Nucleic Acids Research and a member of EMBO, Deutsche Akademie der Naturforscher LEOPOLDINA, Academia Europaea, the Académie des Sciences. 
His research activities are centered on the relationships between sequences, architectures, evolution and functions of RNA molecules, especially those with catalytic activity. With his collaborators, he develops and studies RNA sequence alignments in the light of RNA structures and architectures in order to identify RNA modules and to develop rules for predicting RNA folds and functions. The rules are transformed into algorithms for manipulating and assembling RNA architectures ab initio or in density maps. The tools used are X-ray crystallography, bioinformatics, sequence comparisons, three-dimensional modeling and molecular dynamics simulations.

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