Tomasz Puton

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.

ModeRNA: a tool for comparative modeling of RNA 3D structure

RNA is a large group of functionally important biomacromolecules. In striking analogy to proteins, the function of RNA depends on its structure and dynamics, which in turn is encoded in the linear sequence. However, while there are numerous methods for computational prediction of protein three-dimensional (3D) structure from sequence, with comparative modeling being the most reliable approach, there are very few such methods for RNA. Here, we present ModeRNA, a software tool for comparative modeling of RNA 3D structures. As an input, ModeRNA requires a 3D structure of a template RNA molecule, and a sequence alignment between the target to be modeled and the template. It must be emphasized that a good alignment is required for successful modeling, and for large and complex RNA molecules the development of a good alignment usually requires manual adjustments of the input data based on previous expertise of the respective RNA family. ModeRNA can model post-transcriptional modifications, a functionally important feature analogous to post-translational modifications in proteins. ModeRNA can also model DNA structures or use them as templates. It is equipped with many functions for merging fragments of different nucleic acid structures into a single model and analyzing their geometry. Windows and UNIX implementations of ModeRNA with comprehensive documentation and a tutorial are freely available.

Computational methods for prediction of protein–RNA interactions

Understanding the molecular mechanism of protein-RNA recognition and complex formation is a major challenge in structural biology. Unfortunately, the experimental determination of protein-RNA complexes by X-ray crystallography and nuclear magnetic resonance spectroscopy (NMR) is tedious and difficult. Alternatively, protein-RNA interactions can be predicted by computational methods. Although less accurate than experimental observations, computational predictions can be sufficiently accurate to prompt functional hypotheses and guide experiments, e.g. to identify individual amino acid or nucleotide residues. In this article we review 10 methods for predicting protein-RNA interactions, seven of which predict RNA-binding sites from protein sequences and three from structures. We also developed a meta-predictor that uses the output of top three sequence-based primary predictors to calculate a consensus prediction, which outperforms all the primary predictors. In order to fully cover the software for predicting protein-RNA interactions, we also describe five methods for protein-RNA docking. The article highlights the strengths and shortcomings of existing methods for the prediction of protein-RNA interactions and provides suggestions for their further development.

RNA and protein 3D structure modeling: similarities and differences.

AbstractIn analogy to proteins, the function of RNA depends on its structure and dynamics, which are encoded in the linear sequence. While there are numerous methods for computational prediction of protein 3D structure from sequence, there have been very few such methods for RNA. This review discusses template-based and template-free approaches for macromolecular structure prediction, with special emphasis on comparison between the already tried-and-tested methods for protein structure modeling and the very recently developed “protein-like” modeling methods for RNA. We highlight analogies between many successful methods for modeling of these two types of biological macromolecules and argue that RNA 3D structure can be modeled using “protein-like” methodology. We also highlight the areas where the differences between RNA and proteins require the development of RNA-specific solutions. FigureApproaches for predicting RNA structure. Top: Template-free modeling. Bottom: Template-based modeling

CompaRNA: a server for continuous benchmarking of automated methods for RNA secondary structure prediction

We present a continuous benchmarking approach for the assessment of RNA secondary structure prediction methods implemented in the CompaRNA web server. As of 3 October 2012, the performance of 28 single-sequence and 13 comparative methods has been evaluated on RNA sequences/structures released weekly by the Protein Data Bank. We also provide a static benchmark generated on RNA 2D structures derived from the RNAstrand database. Benchmarks on both data sets offer insight into the relative performance of RNA secondary structure prediction methods on RNAs of different size and with respect to different types of structure. According to our tests, on the average, the most accurate predictions obtained by a comparative approach are generated by CentroidAlifold, MXScarna, RNAalifold and TurboFold. On the average, the most accurate predictions obtained by single-sequence analyses are generated by CentroidFold, ContextFold and IPknot. The best comparative methods typically outperform the best single-sequence methods if an alignment of homologous RNA sequences is available. This article presents the results of our benchmarks as of 3 October 2012, whereas the rankings presented online are continuously updated. We will gladly include new prediction methods and new measures of accuracy in the new editions of CompaRNA benchmarks.

ModeRNA server

SUMMARY The diverse functional roles of non-coding RNA molecules are determined by their underlying structure. ModeRNA server is an online tool for RNA 3D structure modeling by the comparative approach, based on a template RNA structure and a user-defined target-template sequence alignment. It offers an option to search for potential templates, given the target sequence. The server also provides tools for analyzing, editing and formatting of RNA structure files. It facilitates the use of the ModeRNA software and offers new options in comparison to the standalone program. AVAILABILITY AND IMPLEMENTATION ModeRNA server was implemented using the Python language and the Django web framework. It is freely available at http://iimcb.genesilico.pl/modernaserver. CONTACT iamb@genesilico.pl.

A toolbox for developing bioinformatics software

Creating useful software is a major activity of many scientists, including bioinformaticians. Nevertheless, software development in an academic setting is often unsystematic, which can lead to problems associated with maintenance and long-term availibility. Unfortunately, well-documented software development methodology is difficult to adopt, and technical measures that directly improve bioinformatic programming have not been described comprehensively. We have examined 22 software projects and have identified a set of practices for software development in an academic environment. We found them useful to plan a project, support the involvement of experts (e.g. experimentalists), and to promote higher quality and maintainability of the resulting programs. This article describes 12 techniques that facilitate a quick start into software engineering. We describe 3 of the 22 projects in detail and give many examples to illustrate the usage of particular techniques. We expect this toolbox to be useful for many bioinformatics programming projects and to the training of scientific programmers.

Template-Based and Template-Free Modeling of RNA 3D Structure: Inspirations from Protein Structure Modeling

In analogy to proteins, the function of RNA depends on its structure and dynamics, which are encoded in the linear sequence. While there are numerous methods for computational prediction of protein 3D structure from sequence, there have been however very few such methods for RNA. This chapter discusses template-based and template-free approaches for macromolecular structure prediction, with special emphasis on comparison between the already tried-and-tested methods for protein structure modeling and the very recently developed “protein-like” modeling methods for RNA. As examples, we briefly review our recently developed tools for RNA 3D structure prediction, including ModeRNA (template-based or comparative/homology modeling) and SimRNA (template-free or de novo modeling). ModeRNA requires, as an input, atomic 3D coordinates of a template RNA molecule and a user-specified sequence alignment between the target to be modeled and the template. It can model posttranscriptional modifications, a functionally important feature analogous to posttranslational modifications in proteins. It can model the structures of RNAs of essentially any length, provided that a starting template is known. SimRNA can fold RNA 3D structure starting from sequence alone. It is based on a coarse-grained representation of the polynucleotide chains (only three atoms per nucleotide) and uses a Monte Carlo sampling scheme to generate moves in the 3D space, with a statistical potential to estimate the free energy. The current implementation based on simulated annealing is able to find native-like conformations for RNAs <100 nt in length, with multiple runs required to fold long sequences.

RNA tertiary structure prediction with ModeRNA

Noncoding RNAs perform important roles in the cell. As their function is tightly connected with structure, and as experimental methods are time-consuming and expensive, the field of RNA structure prediction is developing rapidly. Here, we present a detailed study on using the ModeRNA software. The tool uses the comparative modeling approach and can be applied when a structural template is available and an alignment of reasonable quality can be performed. We guide the reader through the entire process of modeling Escherichia coli tRNA(Thr) in a conformation corresponding to the complex with an aminoacyl-tRNA synthetase (aaRS). We describe the choice of a template structure, preparation of input files, and explore three possible modeling strategies. In the end, we evaluate the resulting models using six alternative benchmarks. The ModeRNA software can be freely downloaded from http://iimcb.genesilico.pl/moderna/ under the conditions of the General Public License. It runs under LINUX, Windows and Mac OS. It is also available as a server at http://iimcb.genesilico.pl/modernaserver/. The models and the script to reproduce the study from this article are available at http://www.genesilico.pl/moderna/examples/.