This chapter gives an overview over the current methods for automated modeling of RNA structures, with emphasis on template-based methods. The currently used approaches to RNA modeling are presented with a side view on the protein world, where many similar ideas have been used. Two main programs for automated template-based modeling are presented: ModeRNA assembling structures from fragments and MacroMoleculeBuilder performing a simulation to satisfy spatial restraints. Both approaches have in common that they require an alignment of the target sequence to a known RNA structure that is used as a modeling template. As a way to find promising template structures and to align the target and template sequences, we propose a pipeline combining the ParAlign and Infernal programs on RNA family data from Rfam. We also briefly summarize template-free methods for RNA 3D structure prediction. Typically, RNA structures generated by automated modeling methods require local or global optimization. Thus, we also discuss methods that can be used for local or global refinement of RNA structures.
During B cell development, the precursor B cell receptor (pre-BCR) checkpoint is thought to increase immunoglobulin ? light chain (Ig?) locus accessibility to the V(D)J recombinase. Accordingly, pre-B cells lacking the pre-BCR signaling molecules Btk or Slp65 showed reduced germline V(?) transcription. To investigate whether pre-BCR signaling modulates V(?) accessibility through enhancer-mediated Ig? locus topology, we performed chromosome conformation capture and sequencing analyses. These revealed that already in pro-B cells the ? enhancers robustly interact with the ?3.2 Mb V(?) region and its flanking sequences. Analyses in wild-type, Btk, and Slp65 single- and double-deficient pre-B cells demonstrated that pre-BCR signaling reduces interactions of both enhancers with Ig? locus flanking sequences and increases interactions of the 3'? enhancer with V(?) genes. Remarkably, pre-BCR signaling does not significantly affect interactions between the intronic enhancer and V(?) genes, which are already robust in pro-B cells. Both enhancers interact most frequently with highly used V(?) genes, which are often marked by transcription factor E2a. We conclude that the ? enhancers interact with the V(?) region already in pro-B cells and that pre-BCR signaling induces accessibility through a functional redistribution of long-range chromatin interactions within the V(?) region, whereby the two enhancers play distinct roles.
Precursor B cell production from bone marrow in mice and humans declines with age. Because the mechanisms behind are still unknown, we studied five precursor B cell subsets (ProB, PreBI, PreBII large, PreBII small, immature B) and their differentiation-stage characteristic gene expression profiles in healthy individual toddlers and middle-aged adults. Notably, the composition of the precursor B cell compartment did not change with age. The expression levels of several transcripts encoding V(D)J recombination factors were decreased in adults as compared with children: RAG1 expression was significantly reduced in ProB cells, and DNA-PKcs, Ku80, and XRCC4 were decreased in PreBI cells. In contrast, TdT was 3-fold upregulated in immature B cells of adults. Still, N-nucleotides, P-nucleotides, and deletions were similar for IGH and IGK junctions between children and adults. PreBII large cells in adults, but not in children, showed highly upregulated expression of the differentiation inhibitor, inhibitor of DNA binding 2 (ID2), in absence of changes in expression of the ID2-binding partner E2A. Further, we identified impaired Ig locus contraction in adult precursor B cells as a likely mechanism by which ID2-mediated blocking of E2A function results in reduced bone marrow B cell output in adults. The reduced B cell production was not compensated by increased proliferation in adult immature B cells, despite increased Ki67 expression. These findings demonstrate distinct regulatory mechanisms in B cell differentiation between adults and children with a central role for transcriptional regulation of ID2.
One of the mechanisms of Candida albicans resistance to azoledrugs used in antifungal therapy relies on increased expression and presence of point mutations in the ERG11 gene that encodes sterol 14? demethylase (14DM), an enzyme which is the primary target for the azole class of antifungals. The aim of the study was to analyze nucleotide substitutions in the Candida albicans ERG11 gene of azole-susceptible and azole-resistant clinical isolates. The Candida albicans isolates represented a collection of 122 strains selected from 658 strains isolated from different biological materials. Samples were obtained from hospitalized patients. Fluconazole susceptibility was tested in vitro using a microdilution assay. Candida albicans strains used in this study consisted of two groups: 61 of the isolates were susceptible to azoles and the 61 were resistant to azoles. Four overlapping regions of The ERG11 gene of the isolates of Candida albicans strains were amplified and sequenced. The MSSCP (multitemperature single strand conformation polymorphism) method was performed to select Candida albicans samples presenting genetic differences in the ERG11 gene fragments for subsequent sequence analysis. Based on the sequencing results we managed to detect 19 substitutions of nucleotides in the ERG11 gene fragments. Sequencing revealed 4 different alterations: T495A, A530C, G622A and A945C leading to changes in the corresponding amino acid sequence: D116E, K128T, V159I and E266D. The single nucleotide changes in the ERG11 gene did not affect the sensitivity of Candida albicans strains, whereas multiple nucleotide substitutions in the ERG11 gene fragments indicated a possible relation with the increase in resistance to azole drugs.
One of the first steps towards holistic understanding of cellular networks is the integration of the available information in a human and machine readable format. This network reconstruction process is well established for metabolic networks, and numerous genome wide metabolic reconstructions are already available. Extending these strategies to signalling networks has proven difficult, primarily due to the combinatorial nature of regulatory modifications. The combinatorial nature of possible protein-protein interactions and post translational modifications affects both network size and the correspondence between the reconstructed network and the underlying empirical data. Here, we discuss different approaches to reconstruction of signal transduction networks. We divide the current approaches into topological, specific state based and reaction-contingency based, and discuss their different information content and scalability. The discussion focusses on graphical formats but the points are in general applicable also to mathematical models and databases. While the formats have complementary strengths especially for small networks, reaction-contingency based formats have a number of advantages in the light of global network reconstruction. In particular, they minimise the need for assumptions, maximise the congruence with empirical data, and scale efficiently with network size.
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/.
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.
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.
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.
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 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.
Delivering hands-on tutorials on bioinformatics software and web applications is a challenging didactic scenario. The main reason is that trainees have heterogeneous backgrounds, different previous knowledge and vary in learning speed. In this article, we demonstrate how multi-stage learning aids can be used to allow all trainees to progress at a similar speed. In this technique, the trainees can utilize cards with hints and answers to guide themselves self-dependently through a complex task. We have successfully conducted a tutorial for the molecular viewer PyMOL using two sets of learning aid cards. The trainees responded positively, were able to complete the task, and the trainer had spare time to respond to individual questions. This encourages us to conclude that multi-stage learning aids overcome many disadvantages of established forms of hands-on software training.
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.
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