Dataset Information

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

ABSTRACT: BACKGROUND: RNA-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. RESULTS: We 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. CONCLUSIONS: Our 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.

SUBMITTER: Livi CM

PROVIDER: S-EPMC4098778 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Livi Carmen M CM Blanzieri Enrico E

BMC bioinformatics 20140429

<h4>Background</h4>RNA-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 he ...[more]

PMID: 24780077

Similar Datasets

Project description:BackgroundAccurate structure prediction methods play an important role for the understanding of RNA function. Energy-based, pseudoknot-free secondary structure prediction is one of the most widely used and versatile approaches, and improved methods for this task have received much attention over the past five years. Despite the impressive progress that as been achieved in this area, existing evaluations of the prediction accuracy achieved by various algorithms do not provide a comprehensive, statistically sound assessment. Furthermore, while there is increasing evidence that no prediction algorithm consistently outperforms all others, no work has been done to exploit the complementary strengths of multiple approaches.ResultsIn this work, we present two contributions to the area of RNA secondary structure prediction. Firstly, we use state-of-the-art, resampling-based statistical methods together with a previously published and increasingly widely used dataset of high-quality RNA structures to conduct a comprehensive evaluation of existing RNA secondary structure prediction procedures. The results from this evaluation clarify the performance relationship between ten well-known existing energy-based pseudoknot-free RNA secondary structure prediction methods and clearly demonstrate the progress that has been achieved in recent years. Secondly, we introduce AveRNA, a generic and powerful method for combining a set of existing secondary structure prediction procedures into an ensemble-based method that achieves significantly higher prediction accuracies than obtained from any of its component procedures.ConclusionsOur new, ensemble-based method, AveRNA, improves the state of the art for energy-based, pseudoknot-free RNA secondary structure prediction by exploiting the complementary strengths of multiple existing prediction procedures, as demonstrated using a state-of-the-art statistical resampling approach. In addition, AveRNA allows an intuitive and effective control of the trade-off between false negative and false positive base pair predictions. Finally, AveRNA can make use of arbitrary sets of secondary structure prediction procedures and can therefore be used to leverage improvements in prediction accuracy offered by algorithms and energy models developed in the future. Our data, MATLAB software and a web-based version of AveRNA are publicly available at http://www.cs.ubc.ca/labs/beta/Software/AveRNA.

Project description:The E7 and E6 proteins are the main oncoproteins of human papillomavirus types 16 and 18 (HPV-16 and HPV-18), and possess unknown protein structures. E7 interacts with the cellular tumour-suppressor protein pRB and contains a zinc-binding site with two Cys-Xaa2-Cys motifs spaced 29 or 30 residues apart. E6 interacts with another cellular tumour-suppressor protein p53 and contains two zinc-binding sites, each with two Cys-Xaa2-Cys motifs at a similar spacing of 29 or 30 residues. By using the GOR I/III, Chou-Fasman, SAPIENS and PHD methods, the effectiveness of consensus secondary structure predictions on zinc-finger proteins was first tested with sequences for 160 transcription factors and 72 nuclear hormone receptors. These contain Cys2His2 and Cys2Cys2 zinc-binding regions respectively, and possess known atomic structures. Despite the zinc- and DNA-binding properties of these protein folds, the major alpha-helix structures in both zinc-binding regions were correctly identified. Thus validated, the use of these prediction methods with 47 E7 sequences indicated four well-defined alpha-helix (alpha) and beta-sheet (beta) secondary structure elements in the order beta beta alpha beta in the zinc-binding region of E7 at its C-terminus. The prediction was tested by Fourier transform infrared spectroscopy of recombinant HPV-16 E7 in H2O and 2H2O buffers. Quantitative integration showed that E7 contained similar amounts of alpha-helix and beta-sheet structures, in good agreement with the averaged prediction of alpha-helix and beta-sheet structures in E7 and also with previous circular dichroism studies. Protein fold recognition analyses predicted that the structure of the zinc-binding region in E7 was similar to a beta beta alpha beta motif found in the structure of Protein G. This is consistent with the E7 structure predictions, despite the low sequence similarities with E7. This predicted motif is able to position four Cys residues in proximity to a zinc atom. A model for the zinc-binding motif of E7 was constructed by combining the Protein G coordinates with those for the zinc-binding site in transcription factor TFIIS. Similar analyses for the two zinc-binding motifs in E6 showed that they have different alpha/beta secondary structures from that in E7. When compared with 12 other zinc-binding proteins, these results show that E7 and E6 are predicted to possess novel types of zinc-binding structure.

Project description:BACKGROUND: Prediction of protein structural classes (alpha, beta, alpha + beta and alpha/beta) from amino acid sequences is of great importance, as it is beneficial to study protein function, regulation and interactions. Many methods have been developed for high-homology protein sequences, and the prediction accuracies can achieve up to 90%. However, for low-homology sequences whose average pairwise sequence identity lies between 20% and 40%, they perform relatively poorly, yielding the prediction accuracy often below 60%. RESULTS: We propose a new method to predict protein structural classes on the basis of features extracted from the predicted secondary structures of proteins rather than directly from their amino acid sequences. It first uses PSIPRED to predict the secondary structure for each protein sequence. Then, the chaos game representation is employed to represent the predicted secondary structure as two time series, from which we generate a comprehensive set of 24 features using recurrence quantification analysis, K-string based information entropy and segment-based analysis. The resulting feature vectors are finally fed into a simple yet powerful Fisher's discriminant algorithm for the prediction of protein structural classes. We tested the proposed method on three benchmark datasets in low homology and achieved the overall prediction accuracies of 82.9%, 83.1% and 81.3%, respectively. Comparisons with ten existing methods showed that our method consistently performs better for all the tested datasets and the overall accuracy improvements range from 2.3% to 27.5%. A web server that implements the proposed method is freely available at http://www1.spms.ntu.edu.sg/~chenxin/RKS_PPSC/. CONCLUSION: The high prediction accuracy achieved by our proposed method is attributed to the design of a comprehensive feature set on the predicted secondary structure sequences, which is capable of characterizing the sequence order information, local interactions of the secondary structural elements, and spacial arrangements of alpha helices and beta strands. Thus, it is a valuable method to predict protein structural classes particularly for low-homology amino acid sequences.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets