On nucleotide solvent accessibility in RNA structure

Singh, Yumlembam H.; Andrabi, Munazah; Kahali, Bratati; Ghosh, Tapash Chandra; Mizuguchi, Kenji; Кочетов, А. В.; Ahmad, Shandar

doi:10.1016/j.gene.2010.05.001

Cited by 10 publications

(10 citation statements)

References 25 publications

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“…This goal still remains challenging. Future work may include the incorporation of accessibility features calculated according to nucleotide solvent accessibility [45] and not only, as in our case, by the tetranucleotide single-stranded form, as well as the creation of protein-dependent models trained with pre-selected RBP-specific features.…”

Section: Discussionmentioning

confidence: 99%

Protein-specific prediction of mRNA binding using RNA sequences, binding motifs and predicted secondary structures

Livi

Blanzieri

2014

BMC Bioinformatics

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BackgroundRNA-binding proteins interact with specific RNA molecules to regulate important cellular processes. It is therefore necessary to identify the RNA interaction partners in order to understand the precise functions of such proteins. Protein-RNA interactions are typically characterized using in vivo and in vitro experiments but these may not detect all binding partners. Therefore, computational methods that capture the protein-dependent nature of such binding interactions could help to predict potential binding partners in silico.ResultsWe have developed three methods to predict whether an RNA can interact with a particular RNA-binding protein using support vector machines and different features based on the sequence (the Oli method), the motif score (the OliMo method) and the secondary structure (the OliMoSS method). We applied these approaches to different experimentally-derived datasets and compared the predictions with RNAcontext and RPISeq. Oli outperformed OliMoSS and RPISeq, confirming our protein-specific predictions and suggesting that tetranucleotide frequencies are appropriate discriminative features. Oli and RNAcontext were the most competitive methods in terms of the area under curve. A precision-recall curve analysis achieved higher precision values for Oli. On a second experimental dataset including real negative binding information, Oli outperformed RNAcontext with a precision of 0.73 vs. 0.59.ConclusionsOur experiments showed that features based on primary sequence information are sufficiently discriminating to predict specific RNA-protein interactions. Sequence motifs and secondary structure information were not necessary to improve these predictions. Finally we confirmed that protein-specific experimental data concerning RNA-protein interactions are valuable sources of information that can be used for the efficient training of models for in silico predictions. The scripts are available upon request to the corresponding author.

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Section: Discussionmentioning

confidence: 99%

Protein-specific prediction of mRNA binding using RNA sequences, binding motifs and predicted secondary structures

Livi

Blanzieri

2014

BMC Bioinformatics

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“…Only three of these values are independent and in this study, we defined the following three ASA features for each amino acid residue: 1) residue total (tASA), 2) side chain total (scASA) and 3) non-polar total (npASA). This set of structural properties has been widely studied for water-soluble and membrane proteins and more recently for DNA and RNA structures [24,25]. …”

Section: Methodsmentioning

confidence: 99%

Integrated prediction of one-dimensional structural features and their relationships with conformational flexibility in helical membrane proteins

et al. 2010

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BackgroundMany structural properties such as solvent accessibility, dihedral angles and helix-helix contacts can be assigned to each residue in a membrane protein. Independent studies exist on the analysis and sequence-based prediction of some of these so-called one-dimensional features. However, there is little explanation of why certain residues are predicted in a wrong structural class or with large errors in the absolute values of these features. On the other hand, membrane proteins undergo conformational changes to allow transport as well as ligand binding. These conformational changes often occur via residues that are inherently flexible and hence, predicting fluctuations in residue positions is of great significance.ResultsWe performed a statistical analysis of common patterns among selected one-dimensional equilibrium structural features (ESFs) and developed a method for simultaneously predicting all of these features using an integrated system. Our results show that the prediction performance can be improved if multiple structural features are trained in an integrated model, compared to the current practice of developing individual models. In particular, the performance of the solvent accessibility and bend-angle prediction improved in this way. The well-performing bend-angle prediction can be used to predict helical positions with severe kinks at a modest success rate. Further, we showed that single-chain conformational dynamics, measured by B-factors derived from normal mode analysis, could be predicted from observed and predicted ESFs with good accuracy. A web server was developed (http://tardis.nibio.go.jp/netasa/htmone/) for predicting the one-dimensional ESFs from sequence information and analyzing the differences between the predicted and observed values of the ESFs.ConclusionsThe prediction performance of the integrated model is significantly better than that of the models performing the task separately for each feature for the solvent accessibility and bend-angle predictions. The predictability of the features also plays a role in determining flexible positions. Although the dynamics studied here concerns local atomic fluctuations, a similar analysis in terms of global structural features will be helpful in predicting large-scale conformational changes, for which work is in progress.

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“…RNAs except for miRNAs typically have regions that are solventinaccessible and/or double-stranded, preventing intramolecular interactions [22]. mRNAs have more secondary structures or intra-strand base pairing than expected by chance [23].…”

Section: Words That Are Solvent-accessiblementioning

confidence: 99%

“…[25,26]) in a pooling predictor using machine learning [27]. Additionally, nucleotide solvent-accessibility in RNA structures could be estimated by the neural network method of Singh [22] using models of window size 3 nt, which could be expanded to 5-9 nt windows for k length. Alternatively, accessible surface area can be calculated by a publically available program NACCESS [28] to refine the STIC transcriptome words to those populated from singlestranded regions only, along with confidence measures.…”

Section: Words That Are Solvent-accessiblementioning

confidence: 99%

Models of RNA Interaction from Experimental Datasets: Framework of Resilience

Seffens¹

2017

Applications of RNA-Seq and Omics Strategies - From Microorganisms to Human Health

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Resilience is a network property of systems responding under stress, which for biomedicine correlates to chronic or acute insults. Current need exists for models and algorithms to study whole transcriptome differences between tissues and disease states to understand resilience. Goal of this effort is to interpret cellular transcription in a dynamic system biology framework of RNA molecules forming an information structure with regulatory properties acting on individual transcripts. We develop and evaluate a bioinformatics framework based on information theory that utilizes RNA expression data to create a whole transcriptome model of interaction that could lead to the discovery of new biological control mechanisms. This addresses a fundamental question as to why transcription yields such a small fraction of protein products. We focus on a transformative concept that individual transcripts collectively form an "information cloud" of sequence words, which for some genes may have significant regulatory impact. Extending the concept of cis-and trans-regulation, we propose to search for RNAs that are modulated by interactions with the transcriptome cloud and calling such examples nebula regulation. This framework has implications as a paradigm change for RNA regulation and provides a deeper understanding of nucleotide sequence structure and -omic language meaning.

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On nucleotide solvent accessibility in RNA structure

Cited by 10 publications

References 25 publications

Protein-specific prediction of mRNA binding using RNA sequences, binding motifs and predicted secondary structures

Protein-specific prediction of mRNA binding using RNA sequences, binding motifs and predicted secondary structures

Integrated prediction of one-dimensional structural features and their relationships with conformational flexibility in helical membrane proteins

Models of RNA Interaction from Experimental Datasets: Framework of Resilience

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