Irina Tuszynska

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.

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.

DARS-RNP and QUASI-RNP: New statistical potentials for protein-RNA docking

BackgroundProtein-RNA interactions play fundamental roles in many biological processes. Understanding the molecular mechanism of protein-RNA recognition and formation of protein-RNA complexes is a major challenge in structural biology. Unfortunately, the experimental determination of protein-RNA complexes is tedious and difficult, both by X-ray crystallography and NMR. For many interacting proteins and RNAs the individual structures are available, enabling computational prediction of complex structures by computational docking. However, methods for protein-RNA docking remain scarce, in particular in comparison to the numerous methods for protein-protein docking.ResultsWe developed two medium-resolution, knowledge-based potentials for scoring protein-RNA models obtained by docking: the quasi-chemical potential (QUASI-RNP) and the Decoys As the Reference State potential (DARS-RNP). Both potentials use a coarse-grained representation for both RNA and protein molecules and are capable of dealing with RNA structures with posttranscriptionally modified residues. We compared the discriminative power of DARS-RNP and QUASI-RNP for selecting rigid-body docking poses with the potentials previously developed by the Varani and Fernandez groups.ConclusionsIn both bound and unbound docking tests, DARS-RNP showed the highest ability to identify native-like structures. Python implementations of DARS-RNP and QUASI-RNP are freely available for download at http://iimcb.genesilico.pl/RNP/

Predicting atomic details of the unfolding pathway for YibK, a knotted protein from the SPOUT superfamily.

Abstract Several protein structures have been reported to contain intricate knots of the polypeptide backbone but the mechanism of the (un)folding process of knotted proteins remains unknown. The members of the SPOUT superfamily of RNA methyltransferases are some of the most intensely studied systems for investigation of the knot formation and function. YibK (whose biochemical function remains unknown) is the representative protein of the SPOUT superfamily. This protein exhibits a deep trefoil knot at the C-terminus. We conducted an extensive computational analysis of the unfolding process for the monomeric form of YibK. In order to predict the (un)folding pathway of YibK, we have calculated the order of secondary structure disassembly using UNFOLD, and performed thermal unfolding simulations using classical Molecular Dynamics (MD), as well as simulations employing reduced representation of the peptide chain using either MD with the UNRES method or the Monte Carlo (MC) unfolding with the REFINER method. Results obtained from all methods used in this work are in qualitative agreement. We found that YibK unfolds through four intermediate states. The trefoil knot in YibK disappears at the end of the unfolding process, long after the protein loses its native topology. We observed that the C-terminus leaves the knotting loop folded into a hairpin-like structure, in agreement with the results of coarse-grained simulation reported earlier. We propose that the folding pathway of YibK corresponds to the reversed sequence of events observed in the unfolding pathway elucidated in this study. Thus, we predict that the knot formation is the slowest part of the YibK folding process.

THUMP from archaeal tRNA:m22G10 methyltransferase, a genuine autonomously folding domain

The tRNA:m22G10 methyltransferase of Pyrococus abyssi (PAB1283, a member of COG1041) catalyzes the N2,N2-dimethylation of guanosine at position 10 in tRNA. Boundaries of its THUMP (THioUridine synthases, RNA Methyltransferases and Pseudo-uridine synthases)—containing N-terminal domain [1–152] and C-terminal catalytic domain [157–329] were assessed by trypsin limited proteolysis. An inter-domain flexible region of at least six residues was revealed. The N-terminal domain was then produced as a standalone protein (THUMPα) and further characterized. This autonomously folded unit exhibits very low affinity for tRNA. Using protein fold-recognition (FR) methods, we identified the similarity between THUMPα and a putative RNA-recognition module observed in the crystal structure of another THUMP-containing protein (ThiI thiolase of Bacillus anthracis). A comparative model of THUMPα structure was generated, which fulfills experimentally defined restraints, i.e. chemical modification of surface exposed residues assessed by mass spectrometry, and identification of an intramolecular disulfide bridge. A model of the whole PAB1283 enzyme docked onto its tRNAAsp substrate suggests that the THUMP module specifically takes support on the co-axially stacked helices of T-arm and acceptor stem of tRNA and, together with the catalytic domain, screw-clamp structured tRNA. We propose that this mode of interactions may be common to other THUMP-containing enzymes that specifically modify nucleotides in the 3D-core of tRNA.

PROTMAP2D: visualization, comparison and analysis of 2D maps of protein structure

MOTIVATION Protein structure comparison is a fundamental problem in structural biology and bioinformatics. Two-dimensional maps of distances between residues in the structure contain sufficient information to restore the 3D representation, while maps of contacts reveal characteristic patterns of interactions between secondary and super-secondary structures and are very attractive for visual analysis. The overlap of 2D maps of two structures can be easily calculated, providing a sensitive measure of protein structure similarity. PROTMAP2D is a software tool for calculation of contact and distance maps based on user-defined criteria, quantitative comparison of pairs or series of contact maps (e.g. alternative models of the same protein, model versus native structure, different trajectories from molecular dynamics simulations, etc.) and visualization of the results. AVAILABILITY PROTMAP2D for Windows / Linux / MacOSX is freely available for academic users from http://genesilico.pl/protmap2d.htm

Computational modeling of protein–RNA complex structures

Protein-RNA interactions play fundamental roles in many biological processes, such as regulation of gene expression, RNA splicing, and protein synthesis. The understanding of these processes improves as new structures of protein-RNA complexes are solved and the molecular details of interactions analyzed. However, experimental determination of protein-RNA complex structures by high-resolution methods is tedious and difficult. Therefore, studies on protein-RNA recognition and complex formation present major technical challenges for macromolecular structural biology. Alternatively, protein-RNA interactions can be predicted by computational methods. Although less accurate than experimental measurements, theoretical models of macromolecular structures can be sufficiently accurate to prompt functional hypotheses and guide e.g. identification of important amino acid or nucleotide residues. In this article we present an overview of strategies and methods for computational modeling of protein-RNA complexes, including software developed in our laboratory, and illustrate it with practical examples of structural predictions.

Sequence-specific cleavage of the RNA strand in DNA–RNA hybrids by the fusion of ribonuclease H with a zinc finger

Ribonucleases (RNases) are valuable tools applied in the analysis of RNA sequence, structure and function. Their substrate specificity is limited to recognition of single bases or distinct secondary structures in the substrate. Currently, there are no RNases available for purely sequence-dependent fragmentation of RNA. Here, we report the development of a new enzyme that cleaves the RNA strand in DNA–RNA hybrids 5 nt from a nonanucleotide recognition sequence. The enzyme was constructed by fusing two functionally independent domains, a RNase HI, that hydrolyzes RNA in DNA–RNA hybrids in processive and sequence-independent manner, and a zinc finger that recognizes a sequence in DNA–RNA hybrids. The optimization of the fusion enzyme’s specificity was guided by a structural model of the protein-substrate complex and involved a number of steps, including site-directed mutagenesis of the RNase moiety and optimization of the interdomain linker length. Methods for engineering zinc finger domains with new sequence specificities are readily available, making it feasible to acquire a library of RNases that recognize and cleave a variety of sequences, much like the commercially available assortment of restriction enzymes. Potentially, zinc finger-RNase HI fusions may, in addition to in vitro applications, be used in vivo for targeted RNA degradation.