Prediction of dinucleotide-specific RNA-binding sites in proteins

<p>Abstract</p> <p>Background</p> <p>Regulation of gene expression, protein synthesis, replication and assembly of many viruses involve RNA–protein interactions. Although some successful computational tools have been reported to recognize RNA binding sites in proteins, the problem of specificity remains poorly investigated. After the nucleotide base composition, the dinucleotide is the smallest unit of RNA sequence information and many RNA-binding proteins simply bind to regions enriched in one dinucleotide. Interaction preferences of protein subsequences and dinucleotides can be inferred from protein-RNA complex structures, enabling a training-based prediction approach.</p> <p>Results</p> <p>We analyzed basic statistics of amino acid-dinucleotide contacts in protein-RNA complexes and found their pairing preferences could be identified. Using a standard approach to represent protein subsequences by their evolutionary profile, we trained neural networks to predict multiclass target vectors corresponding to 16 possible contacting dinucleotide subsequences. In the cross-validation experiments, the accuracies of the optimum network, measured as areas under the curve (AUC) of the receiver operating characteristic (ROC) graphs, were in the range of 65-80%.</p> <p>Conclusions</p> <p>Dinucleotide-specific contact predictions have also been extended to the prediction of interacting protein and RNA fragment pairs, which shows the applicability of this method to predict targets of RNA-binding proteins. A web server predicting the 16-dimensional contact probability matrix directly from a user-defined protein sequence was implemented and made available at: <url>http://tardis.nibio.go.jp/netasa/srcpred</url>.</p

Predicting variation of DNA shape preferences in protein-DNA interaction in cancer cells with a new biophysical model

DNA shape readout is an important mechanism of target site recognition by transcription factors, in addition to the sequence readout. Several models of transcription factor-DNA binding which consider DNA shape have been developed in recent years. We present a new biophysical model of protein-DNA interaction by considering the DNA shape features, which is based on a neighbour dinucleotide dependency model BayesPI2. The parameters of the new model are restricted to a subspace spanned by the 2-mer DNA shape features, which allowing a biophysical interpretation of the new parameters as position-dependent preferences towards certain values of the features. Using the new model, we explore the variation of DNA shape preferences in several transcription factors across cancer cell lines and cellular conditions. We find evidence of DNA shape variations at FOXA1 binding sites in MCF7 cells after treatment with steroids. The new model is useful for elucidating finer details of transcription factor-DNA interaction. It may be used to improve the prediction of cancer mutation effects in the future

Computational prediction of splicing regulatory elements shared by Tetrapoda organisms

Background: auxiliary splicing sequences play an important role in ensuring accurate and efficient splicing by promoting or repressing recognition of authentic splice sites. These cis-acting motifs have been termed splicing enhancers and silencers and are located both in introns and exons. They co-evolved into an intricate splicing code together with additional functional constraints, such as tissue-specific and alternative splicing patterns. We used orthologous exons extracted from the University of California Santa Cruz multiple genome alignments of human and 22 Tetrapoda organisms to predict candidate enhancers and silencers that have reproducible and statistically significant bias towards annotated exonic boundaries.Results: a total of 2,546 Tetrapoda enhancers and silencers were clustered into 15 putative core motifs based on their Markov properties. Most of these elements have been identified previously, but 118 putative silencers and 260 enhancers (~15%) were novel. Examination of previously published experimental data for the presence of predicted elements showed that their mutations in 21/23 (91.3%) cases altered the splicing pattern as expected. Predicted intronic motifs flanking 3' and 5' splice sites had higher evolutionary conservation than other sequences within intronic flanks and the intronic enhancers were markedly differed between 3' and 5' intronic flanks.Conclusion: difference in intronic enhancers supporting 5' and 3' splice sites suggests an independent splicing commitment for neighboring exons. Increased evolutionary conservation for ISEs/ISSs within intronic flanks and effect of modulation of predicted elements on splicing suggest functional significance of found elements in splicing regulation. Most of the elements identified were shown to have direct implications in human splicing and therefore could be useful for building computational splicing models in biomedical researc

Saturation mutagenesis reveals manifold determinants of exon definition.

To illuminate the extent and roles of exonic sequences in the splicing of human RNA transcripts, we conducted saturation mutagenesis of a 51-nt internal exon in a three-exon minigene. All possible single and tandem dinucleotide substitutions were surveyed. Using high-throughput genetics, 5560 minigene molecules were assayed for splicing in human HEK293 cells. Up to 70% of mutations produced substantial (greater than twofold) phenotypes of either increased or decreased splicing. Of all predicted secondary structural elements, only a single 15-nt stem-loop showed a strong correlation with splicing, acting negatively. The in vitro formation of exon-protein complexes between the mutant molecules and proteins associated with spliceosome formation (U2AF35, U2AF65, U1A, and U1-70K) correlated with splicing efficiencies, suggesting exon definition as the step affected by most mutations. The measured relative binding affinities of dozens of human RNA binding protein domains as reported in the CISBP-RNA database were found to correlate either positively or negatively with splicing efficiency, more than could fit on the 51-nt test exon simultaneously. The large number of these functional protein binding correlations point to a dynamic and heterogeneous population of pre-mRNA molecules, each responding to a particular collection of binding proteins

A machine learning strategy to identify candidate binding sites in human protein-coding sequence

BACKGROUND: The splicing of RNA transcripts is thought to be partly promoted and regulated by sequences embedded within exons. Known sequences include binding sites for SR proteins, which are thought to mediate interactions between splicing factors bound to the 5' and 3' splice sites. It would be useful to identify further candidate sequences, however identifying them computationally is hard since exon sequences are also constrained by their functional role in coding for proteins. RESULTS: This strategy identified a collection of motifs including several previously reported splice enhancer elements. Although only trained on coding exons, the model discriminates both coding and non-coding exons from intragenic sequence. CONCLUSION: We have trained a computational model able to detect signals in coding exons which seem to be orthogonal to the sequences' primary function of coding for proteins. We believe that many of the motifs detected here represent binding sites for both previously unrecognized proteins which influence RNA splicing as well as other regulatory elements

Quantitative principles of cis-translational control by general mRNA sequence features in eukaryotes.

BackgroundGeneral translational cis-elements are present in the mRNAs of all genes and affect the recruitment, assembly, and progress of preinitiation complexes and the ribosome under many physiological states. These elements include mRNA folding, upstream open reading frames, specific nucleotides flanking the initiating AUG codon, protein coding sequence length, and codon usage. The quantitative contributions of these sequence features and how and why they coordinate to control translation rates are not well understood.ResultsHere, we show that these sequence features specify 42-81% of the variance in translation rates in Saccharomyces cerevisiae, Schizosaccharomyces pombe, Arabidopsis thaliana, Mus musculus, and Homo sapiens. We establish that control by RNA secondary structure is chiefly mediated by highly folded 25-60 nucleotide segments within mRNA 5' regions, that changes in tri-nucleotide frequencies between highly and poorly translated 5' regions are correlated between all species, and that control by distinct biochemical processes is extensively correlated as is regulation by a single process acting in different parts of the same mRNA.ConclusionsOur work shows that general features control a much larger fraction of the variance in translation rates than previously realized. We provide a more detailed and accurate understanding of the aspects of RNA structure that directs translation in diverse eukaryotes. In addition, we note that the strongly correlated regulation between and within cis-control features will cause more even densities of translational complexes along each mRNA and therefore more efficient use of the translation machinery by the cell