Michal Boniecki

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.

RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures

This paper is a report of a second round of RNA-Puzzles, a collective and blind experiment in three-dimensional (3D) RNA structure prediction. Three puzzles, Puzzles 5, 6, and 10, represented sequences of three large RNA structures with limited or no homology with previously solved RNA molecules. A lariat-capping ribozyme, as well as riboswitches complexed to adenosylcobalamin and tRNA, were predicted by seven groups using RNAComposer, ModeRNA/SimRNA, Vfold, Rosetta, DMD, MC-Fold, 3dRNA, and AMBER refinement. Some groups derived models using data from state-of-the-art chemical-mapping methods (SHAPE, DMS, CMCT, and mutate-and-map). The comparisons between the predictions and the three subsequently released crystallographic structures, solved at diffraction resolutions of 2.5-3.2 Å, were carried out automatically using various sets of quality indicators. The comparisons clearly demonstrate the state of present-day de novo prediction abilities as well as the limitations of these state-of-the-art methods. All of the best prediction models have similar topologies to the native structures, which suggests that computational methods for RNA structure prediction can already provide useful structural information for biological problems. However, the prediction accuracy for non-Watson-Crick interactions, key to proper folding of RNAs, is low and some predicted models had high Clash Scores. These two difficulties point to some of the continuing bottlenecks in RNA structure prediction. All submitted models are available for download at http://ahsoka.u-strasbg.fr/rnapuzzles/.

FRankenstein becomes a cyborg: The automatic recombination and realignment of fold recognition models in CASP6

In the course of CASP6, we generated models for all targets using a new version of the “FRankensteins monster approach.” Previously (in CASP5) we were able to build many very accurate full‐atom models by selection and recombination of well‐folded fragments obtained from crude fold recognition (FR) results, followed by optimization of the sequence–structure fit and assessment of alternative alignments on the structural level. This procedure was however very arduous, as most of the steps required extensive visual and manual input from the human modeler. Now, we have automated the most tedious steps, such as superposition of alternative models, extraction of best‐scoring fragments, and construction of a hybrid “monster” structure, as well as generation of alternative alignments in the regions that remain poorly scored in the refined hybrid model. We have also included the ROSETTA method to construct those parts of the target for which no reasonable structures were generated by FR methods (such as long insertions and terminal extensions). The analysis of successes and failures of the current version of the FRankenstein approach in modeling of CASP6 targets reveals that the considerably streamlined and automated method performs almost as well as the initial, mostly manual version, which suggests that it may be a useful tool for accurate protein structure prediction even in the hands of nonexperts. Proteins 2005;Suppl 7:106–113.

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

SimRNA: a coarse-grained method for RNA folding simulations and 3D structure prediction

RNA molecules play fundamental roles in cellular processes. Their function and interactions with other biomolecules are dependent on the ability to form complex three-dimensional (3D) structures. However, experimental determination of RNA 3D structures is laborious and challenging, and therefore, the majority of known RNAs remain structurally uncharacterized. Here, we present SimRNA: a new method for computational RNA 3D structure prediction, which uses a coarse-grained representation, relies on the Monte Carlo method for sampling the conformational space, and employs a statistical potential to approximate the energy and identify conformations that correspond to biologically relevant structures. SimRNA can fold RNA molecules using only sequence information, and, on established test sequences, it recapitulates secondary structure with high accuracy, including correct prediction of pseudoknots. For modeling of complex 3D structures, it can use additional restraints, derived from experimental or computational analyses, including information about secondary structure and/or long-range contacts. SimRNA also can be used to analyze conformational landscapes and identify potential alternative structures.

Structural bioinformatics analysis of enzymes involved in the biosynthesis pathway of the hypermodified nucleoside ms2io6A37 in tRNA

tRNAs from all organisms contain posttranscriptionally modified nucleosides, which are derived from the four canonical nucleosides. In most tRNAs that read codons beginning with U, adenosine in the position 37 adjacent to the 3′ position of the anticodon is modified to N6‐(Δ2‐isopentenyl) adenosine (i6A). In many bacteria, such as Escherichia coli, this residue is typically hypermodified to N6‐isopentenyl‐2‐thiomethyladenosine (ms2i6A). In a few bacteria, such as Salmonella typhimurium, ms2i6A can be further hydroxylated to N6‐(cis‐4‐hydroxyisopentenyl)‐2‐thiomethyladenosine (ms2io6A). Although the enzymes that introduce the respective modifications (prenyltransferase MiaA, methylthiotransferase MiaB, and hydroxylase MiaE) have been identified, their structures remain unknown and sequence‐function relationships remain obscure. We carried out sequence analysis and structure prediction of MiaA, MiaB, and MiaE, using the protein fold‐recognition approach. Three‐dimensional models of all three proteins were then built using a new modeling protocol designed to overcome uncertainties in the alignments and divergence between the templates. For MiaA and MiaB, the catalytic core was built based on the templates from the P‐loop NTPase and Radical‐SAM superfamilies, respectively. For MiaB, we have also modeled the C‐terminal TRAM domain and the newly predicted N‐terminal flavodoxin‐fold domain. For MiaE, we confidently predict that it shares the three‐dimensional fold with the ferritin‐like four‐helix bundle proteins and that it has a similar active site and mechanism of action to diiron carboxylate enzymes, in particular, methane monooxygenase (E.C.1.14.13.25) that catalyses the biological hydroxylation of alkanes. Our models provide the first structural platform for enzymes involved in the biosynthesis of i6A, ms2i6A, and ms2io6A, explain the data available from the literature and will help to design further experiments and interpret their results. Proteins 2008.

Computational modeling of RNA 3D structures, with the aid of experimental restraints

In addition to mRNAs whose primary function is transmission of genetic information from DNA to proteins, numerous other classes of RNA molecules exist, which are involved in a variety of functions, such as catalyzing biochemical reactions or performing regulatory roles. In analogy to proteins, the function of RNAs depends on their structure and dynamics, which are largely determined by the ribonucleotide sequence. Experimental determination of high-resolution RNA structures is both laborious and difficult, and therefore, the majority of known RNAs remain structurally uncharacterized. To address this problem, computational structure prediction methods were developed that simulate either the physical process of RNA structure formation (“Greek science” approach) or utilize information derived from known structures of other RNA molecules (“Babylonian science” approach). All computational methods suffer from various limitations that make them generally unreliable for structure prediction of long RNA sequences. However, in many cases, the limitations of computational and experimental methods can be overcome by combining these two complementary approaches with each other. In this work, we review computational approaches for RNA structure prediction, with emphasis on implementations (particular programs) that can utilize restraints derived from experimental analyses. We also list experimental approaches, whose results can be relatively easily used by computational methods. Finally, we describe case studies where computational and experimental analyses were successfully combined to determine RNA structures that would remain out of reach for each of these approaches applied separately.

SimRNAweb: a web server for RNA 3D structure modeling with optional restraints

RNA function in many biological processes depends on the formation of three-dimensional (3D) structures. However, RNA structure is difficult to determine experimentally, which has prompted the development of predictive computational methods. Here, we introduce a user-friendly online interface for modeling RNA 3D structures using SimRNA, a method that uses a coarse-grained representation of RNA molecules, utilizes the Monte Carlo method to sample the conformational space, and relies on a statistical potential to describe the interactions in the folding process. SimRNAweb makes SimRNA accessible to users who do not normally use high performance computational facilities or are unfamiliar with using the command line tools. The simplest input consists of an RNA sequence to fold RNA de novo. Alternatively, a user can provide a 3D structure in the PDB format, for instance a preliminary model built with some other technique, to jump-start the modeling close to the expected final outcome. The user can optionally provide secondary structure and distance restraints, and can freeze a part of the starting 3D structure. SimRNAweb can be used to model single RNA sequences and RNA-RNA complexes (up to 52 chains). The webserver is available at http://genesilico.pl/SimRNAweb.

Solution conformation of adenovirus virus associated RNA-I and its interaction with PKR

Adenovirus virus-associated RNA (VAI) provides protection against the host antiviral response in part by inhibiting the interferon-induced double stranded RNA-activated protein kinase (PKR). VAI consists of three base-paired regions; the apical stem responsible for the interaction with double-stranded RNA binding motifs (dsRBMs) of PKR, the central stem required for inhibition, and the terminal stem. The solution conformation of VAI and VAI lacking the terminal stem were determined using SAXS that suggested extended conformations that are in agreement with their secondary structures. Solution conformations of VAI lacking the terminal stem in complex with the dsRBMs of PKR indicated that the apical stem interacts with both dsRNA-binding motifs whereas the central stem does not. Hydrodynamic properties calculated from ab initio models were compared to experimentally determined parameters for model validation. Furthermore, SAXS envelopes were used as a constraint for the in silico modeling of tertiary structure for RNA and RNA-protein complex. Finally, full-length PKR was also studied, but concentration-dependent changes in hydrodynamic parameters prevented ab initio shape determination. Taken together, results provide an improved structural framework that further our understanding of the role VAI plays in evading host innate immune responses.

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.