Characteristic comparison between two types of miRNA precursors in metazoan species

MicroRNAs (miRNAs) are a class of small non-coding RNAs discovered in recent years, which are found to play important regulatory roles in various organisms. As the number of experimentally validated miRNAs is rapidly increasing, systematic analysis on the characteristics of these known miRNAs is necessary and indispensable, especially for computational prediction of new miRNAs. We extensively analyzed precursor sequences for all experimentally validated mature miRNAs in metazoan species, focusing on the characteristics at the level of primary sequences and secondary structures. An observation over the secondary structures of 2729 miRNA precursors (pre-miRNAs) reveals that these hairpin structures can be approximately classified into two types: one with a hairpin loop, and the other with multiple loops. Interestingly, the two types of pre-miRNAs show significant differences in both sequence and structure characteristics, and our study indicates that separate consideration on each type of pre-miRNAs is more reasonable, especially in computational prediction. Besides, we develop a new criterion called mAMFE which shows robust discriminative power in distinguishing pre-miRNAs against other RNAs, thus can potentially serve as a discriminative feature in prediction of new pre-miRNAs.     

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.     

Structural assembly of two-domain proteins by rigid-body docking

Background  

Modelling proteins with multiple domains is one of the central challenges in Structural Biology. Although homology modelling has successfully been applied for prediction of protein structures, very often domain-domain interactions cannot be inferred from the structures of homologues and their prediction requiresab initiomethods. Here we present a new structural prediction approach for modelling two-domain proteins based on rigid-body domain-domain docking.     

Synthetic pre-microRNAs reveal dual-strand activity of miR-34a on TNF-α

Functional microRNAs (miRNAs) are produced from both arms of their precursors (pre-miRNAs). Their abundances vary in context-dependent fashion spatiotemporarily and there is mounting evidence of regulatory interplay between them. Here, we introduce chemically synthesized pre-miRNAs (syn-pre-miRNAs) as a general class of accessible, easily transfectable mimics of pre-miRNAs. These are RNA hairpins, identical in sequence to natural pre-miRNAs. They differ from commercially available miRNA mimics through their complete hairpin structure, including any regulatory elements in their terminal-loop regions and their potential to introduce both strands into RISC. They are distinguished from transcribed pre-miRNAs by their terminal 5′ hydroxyl groups and their precisely defined terminal nucleotides. We demonstrate with several examples how they fully recapitulate the properties of pre-miRNAs, including their processing by Dicer into functionally active 5p; and 3p-derived mature miRNAs. We use syn-pre-miRNAs to show that miR-34a uses its 5p and 3p miRNAs in two pathways: apoptosis during TGF-β signaling, where SIRT1 and SP4 are suppressed by miR-34a-5p and miR-34a-3p, respectively; and the lipopolysaccharide (LPS)-activation of primary human monocyte-derived macrophages, where TNF (TNFα) is suppressed by miR-34a-5p indirectly and miR-34a-3p directly. Our results add to growing evidence that the use of both arms of a miRNA may be a widely used mechanism. We further suggest that syn-pre-miRNAs are ideal and affordable tools to investigate these mechanisms.     

Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information

Background  

Structural properties of proteins such as secondary structure and solvent accessibility contribute to three-dimensional structure prediction, not only in the ab initio case but also when homology information to known structures is available. Structural properties are also routinely used in protein analysis even when homology is available, largely because homology modelling is lower throughput than, say, secondary structure prediction. Nonetheless, predictors of secondary structure and solvent accessibility are virtually always ab initio.     

A novel method for prokaryotic promoter prediction based on DNA stability

Background  

In the post-genomic era, correct gene prediction has become one of the biggest challenges in genome annotation. Improved promoter prediction methods can be one step towards developing more reliable ab initio gene prediction methods. This work presents a novel prokaryotic promoter prediction method based on DNA stability.     

Improved residue contact prediction using support vector machines and a large feature set

Background  

Predicting protein residue-residue contacts is an important 2D prediction task. It is useful for ab initio structure prediction and understanding protein folding. In spite of steady progress over the past decade, contact prediction remains still largely unsolved.     

Ab initio modeling of small proteins by iterative TASSER simulations

Background  

Predicting 3-dimensional protein structures from amino-acid sequences is an important unsolved problem in computational structural biology. The problem becomes relatively easier if close homologous proteins have been solved, as high-resolution models can be built by aligning target sequences to the solved homologous structures. However, for sequences without similar folds in the Protein Data Bank (PDB) library, the models have to be predicted from scratch. Progress in the ab initio structure modeling is slow. The aim of this study was to extend the TASSER (threading/assembly/refinement) method for the ab initio modeling and examine systemically its ability to fold small single-domain proteins.     

Analyzing modular RNA structure reveals low global structural entropy in microRNA sequence

Secondary structure remains the most exploitable feature for noncoding RNA (ncRNA) gene finding in genomes. However, methods based on secondary structure prediction may generate superfluous amount of candidates for validation and have yet to deliver the desired performance that can complement experimental efforts in ncRNA gene finding. This paper investigates a novel method, unpaired structural entropy (USE) as a measurement for the structure fold stability of ncRNAs. USE proves to be effective in identifying from the genome background a class of ncRNAs, such as precursor microRNAs (pre-miRNAs) that contains a long stem hairpin loop. USE correlates well and performs better than other measures on pre-miRNAs, including the previously formulated structural entropy. As an SVM classifier, USE outperforms existing pre-miRNA classifiers. A long stem hairpin loop is common for a number of other functional RNAs including introns splicing hairpins loops and intrinsic termination hairpin loops. We believe USE can be further applied in developing ab initio prediction programs for a larger class of ncRNAs.     

MED: a new non-supervised gene prediction algorithm for bacterial and archaeal genomes

Background  

Despite a remarkable success in the computational prediction of genes in Bacteria and Archaea, a lack of comprehensive understanding of prokaryotic gene structures prevents from further elucidation of differences among genomes. It continues to be interesting to develop new ab initio algorithms which not only accurately predict genes, but also facilitate comparative studies of prokaryotic genomes.     

Two-step cleavage of hairpin RNA with 5' overhangs by human DICER

Background  

DICER is an RNase III family endoribonuclease that processes precursor microRNAs (pre-miRNAs) and long double-stranded RNAs, generating microRNA (miRNA) duplexes and short interfering RNA duplexes with 20~23 nucleotides (nts) in length. The typical form of pre-miRNA processed by the Drosha protein is a hairpin RNA with 2-nt 3' overhangs. On the other hand, production of mature miRNA from an endogenous hairpin RNA with 5' overhangs has also been reported, although the mechanism for this process is unknown.