Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine

Background  

MicroRNAs (miRNAs) are a group of short (~22 nt) non-coding RNAs that play important regulatory roles. MiRNA precursors (pre-miRNAs) are characterized by their hairpin structures. However, a large amount of similar hairpins can be folded in many genomes. Almost all current methods for computational prediction of miRNAs use comparative genomic approaches to identify putative pre-miRNAs from candidate hairpins. Ab initio method for distinguishing pre-miRNAs from sequence segments with pre-miRNA-like hairpin structures is lacking. Being able to classify real vs. pseudo pre-miRNAs is important both for understanding of the nature of miRNAs and for developing ab initio prediction methods that can discovery new miRNAs without known homology.     

Computational study of associations between histone modification and protein-DNA binding in yeast genome by integrating diverse information

Background

Several tools are available to identify miRNAs from deep-sequencing data, however, only a few of them, like miRDeep, can identify novel miRNAs and are also available as a standalone application. Given the difference between plant and animal miRNAs, particularly in terms of distribution of hairpin length and the nature of complementarity with its duplex partner (or miRNA star), the underlying (statistical) features of miRDeep and other tools, using similar features, are likely to get affected.

Results

The potential effects on features, such as minimum free energy, stability of secondary structures, excision length, etc., were examined, and the parameters of those displaying sizable changes were estimated for plant specific miRNAs. We found most of these features acquired a new set of values or distributions for plant specific miRNAs. While the length of conserved positions (nucleus) in mature miRNAs were relatively longer in plants, the difference in distribution of minimum free energy, between real and background hairpins, was marginal. However, the choice of source (species) of background sequences was found to affect both the minimum free energy and miRNA hairpin stability. The new parameters were tested on an Illumina dataset from maize seedlings, and the results were compared with those obtained using default parameters. The newly parameterized model was found to have much improved specificity and sensitivity over its default counterpart.

Conclusions

In summary, the present study reports behavior of few general and tool-specific statistical features for improving the prediction accuracy of plant miRNAs from deep-sequencing data.     

Genetic algorithm-based efficient feature selection for classification of pre-miRNAs

In order to classify the real/pseudo human precursor microRNA (pre-miRNAs) hairpins with ab initio methods, numerous features are extracted from the primary sequence and second structure of pre-miRNAs. However, they include some redundant and useless features. It is essential to select the most representative feature subset; this contributes to improving the classification accuracy. We propose a novel feature selection method based on a genetic algorithm, according to the characteristics of human pre-miRNAs. The information gain of a feature, the feature conservation relative to stem parts of pre-miRNA, and the redundancy among features are all considered. Feature conservation was introduced for the first time. Experimental results were validated by cross-validation using datasets composed of human real/pseudo pre-miRNAs. Compared with microPred, our classifier miPredGA, achieved more reliable sensitivity and specificity. The accuracy was improved nearly 12%. The feature selection algorithm is useful for constructing more efficient classifiers for identification of real human pre-miRNAs from pseudo hairpins.     

Synaptic enrichment of microRNAs in adult mouse forebrain is related to structural features of their precursors

   

Within mouse forebrain, a subset of microRNAs are significantly enriched in synaptoneurosomes (a synaptic fraction containing pinched-off dendritic spines) and a subset are significantly depleted relative to total forebrain homogenate. Here I show that, as a group, the pre-miR hairpin precursors of synaptically enriched microRNAs exhibit significantly different structural features than those that are non-enriched or depleted. Precursors of synaptically enriched microRNAs tend to have a) shorter uninterrupted double-stranded stem segments, and b) more symmetrical bulges containing a single nucleotide on each side. These structural differences may provide a basis for the differential binding of proteins that mediate dendritic transport of pre-miRs, or that prevent pre-miRs from being prematurely processed into mature miRNAs during the transport process.     

Exploring the complex folding kinetics of RNA hairpins: II. Effect of sequence, length, and misfolded states

The complexity of RNA hairpin folding arises from the interplay between the loop formation, the disruption of the slow-breaking misfolded states, and the formation of the slow-forming native base stacks. We investigate the general physical mechanism for the dependence of the RNA hairpin folding kinetics on the sequence and the length of the hairpin loop and the helix stem. For example, 1), the folding would slow down when a stable GC basepair moves to the middle of the stem; 2), hairpin with GC basepair near the loop would fold/unfold faster than the one with GC near the tail of the stem; 3), within a certain range of the stem length, a longer stem can cause faster folding; and 4), certain misfolded states can assist folding through the formation of scaffold structures to lower the entropic barrier for the folding. All our findings are directly applicable and quantitatively testable in experiments. In addition, our results can be useful for molecular design to achieve desirable fast/slow-folding hairpins, hairpins with/without specific misfolded intermediates, and hairpins that fold along designed pathways.     

Strand-loop-strand motifs: prediction of hairpins and diverging turns in proteins

Beta-sheet proteins have been particularly challenging for de novo structure prediction methods, which tend to pair adjacent beta-strands into beta-hairpins and produce overly local topologies. To remedy this problem and facilitate de novo prediction of beta-sheet protein structures, we have developed a neural network that classifies strand-loop-strand motifs by local hairpins and nonlocal diverging turns by using the amino acid sequence as input. The neural network is trained with a representative subset of the Protein Data Bank and achieves a prediction accuracy of 75.9 +/- 4.4% compared to a baseline prediction rate of 59.1%. Hairpins are predicted with an accuracy of 77.3 +/- 6.1%, diverging turns with an accuracy of 73.9 +/- 6.0%. Incorporation of the beta-hairpin/diverging turn classification into the ROSETTA de novo structure prediction method led to higher contact order models and somewhat improved tertiary structure predictions for a test set of 11 all-beta-proteins and 3 alphabeta-proteins. The beta-hairpin/diverging turn classification from amino acid sequences is available online for academic use (Meiler and Kuhn, 2003; www.jens-meiler.de/turnpred.html).     

cis-Acting elements important for retroviral RNA packaging specificity

Spleen necrosis virus (SNV) proteins can package RNA from distantly related murine leukemia virus (MLV), whereas MLV proteins cannot package SNV RNA efficiently. We used this nonreciprocal recognition to investigate regions of packaging signals that influence viral RNA encapsidation specificity. Although the MLV and SNV packaging signals (Psi and E, respectively) do not contain significant sequence homology, they both contain a pair of hairpins. This hairpin pair was previously proposed to be the core element in MLV Psi. In the present study, MLV-based vectors were generated to contain chimeric SNV/MLV packaging signals in which the hairpins were replaced with the heterologous counterpart. The interactions between these chimeras and MLV or SNV proteins were examined by virus replication and RNA analyses. SNV proteins recognized all of the chimeras, indicating that these chimeras were functional. We found that replacing the hairpin pair did not drastically alter the ability of MLV proteins to package these chimeras. These results indicate that, despite the important role of the hairpin pair in RNA packaging, it is not the major motif responsible for the ability of MLV proteins to discriminate between the MLV and SNV packaging signals. To determine the role of sequences flanking the hairpins in RNA packaging specificity, vectors with swapped flanking regions were generated and evaluated. SNV proteins packaged all of these chimeras efficiently. In contrast, MLV proteins strongly favored chimeras with the MLV 5'-flanking regions. These data indicated that MLV Gag recognizes multiple elements in the viral packaging signal, including the hairpin structure and flanking regions.     

Crystal structures and increased stabilization of the protein G variants with switched folding pathways NuG1 and NuG2

We recently described two protein G variants (NuG1 and NuG2) with redesigned first hairpins that were almost twice as stable, folded 100-fold faster, and had a switched folding mechanism relative to the wild-type protein. To test the structural accuracy of our design algorithm and to provide insights to the dramatic changes in the kinetics and thermodynamics of folding, we have now determined the crystal structures of NuG1 and NuG2 to 1.8 A and 1.85 A, respectively. We find that they adopt hairpin structures that are closer to the computational models than to wild-type protein G; the RMSD of the NuG1 hairpin to the design model and the wild-type structure are 1.7 A and 5.1 A, respectively. The crystallographic B factor in the redesigned first hairpin of NuG1 is systematically higher than the second hairpin, suggesting that the redesigned region is somewhat less rigid. A second round of structure-based design yielded new variants of NuG1 and NuG2, which are further stabilized by 0.5 kcal/mole and 0.9 kcal/mole.     

β‐Hairpin folding and stability: molecular dynamics simulations of designed peptides in aqueous solution

The structural properties of a 10‐residue and a 15‐residue peptide in aqueous solution were investigated by molecular dynamics simulation. The two designed peptides, SYINSDGTWT and SESYINSDGTWTVTE, had been studied previously by NMR at 278 K and the resulting model structures were classified as 3:5 β‐hairpins with a type I + G1 β‐bulge turn. In simulations at 278 K, starting from the NMR model structure, the 3:5 β‐hairpin conformers proved to be stable over the time period evaluated (30 ns). Starting from an extended conformation, simulations of the decapeptide at 278 K, 323 K and 353 K were also performed to study folding. Over the relatively short time scales explored (30 ns at 278 K and 323 K, 56 ns at 353 K), folding to the 3:5 β‐hairpin could only be observed at 353 K. At this temperature, the collapse to β‐hairpin‐like conformations is very fast. The conformational space accessible to the peptide is entirely dominated by loop structures with different degrees of β‐hairpin character. The transitions between different types of ordered loops and β‐hairpins occur through two unstructured loop conformations stabilized by a single side‐chain interaction between Tyr2 and Trp9, which facilitates the changes of the hydrogen‐bond register. In agreement with previous experimental results, β‐hairpin formation is initially driven by the bending propensity of the turn segment. Nevertheless, the fine organization of the turn region appears to be a late event in the folding process. Copyright © 2004 European Peptide Society and John Wiley & Sons, Ltd.     

Prediction of Conserved Precursors of miRNAs and Their Mature Forms by Integrating Position-Specific Structural Features

MicroRNA (miRNA) precursor hairpins have a unique secondary structure, nucleotide length, and nucleotide content that are in most cases evolutionarily conserved. The aim of this study was to utilize position-specific features of miRNA hairpins to improve their identification. To this end, we defined the evolutionary and structurally conserved features in each position of miRNA hairpins with heuristically derived values, which were successfully integrated using a probabilistic framework. Our method, miRRim2, can not only accurately detect miRNA hairpins, but infer the location of a mature miRNA sequence. To evaluate the accuracy of miRRim2, we designed a cross validation test in which the whole human genome was used for evaluation. miRRim2 could more accurately detect miRNA hairpins than the other computational predictions that had been performed on the human genome, and detect the position of the 5'-end of mature miRNAs with sensitivity and positive predictive value (PPV) above 0.4. To further evaluate miRRim2 on independent data, we applied it to the Ciona intestinalis genome. Our method detected 47 known miRNA hairpins among top 115 candidates, and pinpointed the 5'-end of mature miRNAs with sensitivity and PPV about 0.4. When our results were compared with deep-sequencing reads of small RNA libraries from Ciona intestinalis cells, we found several candidates in which the predicted mature miRNAs were in good accordance with deep-sequencing results.