microRNA靶基因预测算法研究概况及发展趋势

microRNA（miRNA）是一类约22个核苷酸（nt）长的非编码小分子RNA,广泛存在于动植物细胞中,通过和靶基因的不精确互补配对而裂解mRNA或抑制翻译的起始。准确地预测miRNA靶基因和正确地认识miRNA及其靶基因的作用机理已成为当前研究的热点。作者试图对目前常用的10余个高等生物miRNA靶基因预测软件的实现原理、适用对象及各算法的创新之处等加以综述,以便为进行靶基因预测算法设计人员提供参考,对生物学实验验证提供更好的理论指导。     

Effect of ion pair on thermostability of F1 protease: integration of computational and experimental approaches

A thermophilic Bacillus stearothermophilus F1 produces an extremely thermostable serine protease. The F1 protease sequence was used to predict its three-dimensional (3D) structure to provide better insights into the relationship between the protein structure and biological function and to identify opportunities for protein engineering. The final model was evaluated to ensure its accuracy using three independent methods: Procheck, Verify3D, and Errat. The predicted 3D structure of F1 protease was compared with the crystal structure of serine proteases from mesophilic bacteria and archaea, and led to the identification of features that were related to protein stabilization. Higher thermostability correlated with an increased number of residues that were involved in ion pairs or networks of ion pairs. Therefore, the mutants W200R and D58S were designed using site-directed mutagenesis to investigate F1 protease stability. The effects of addition and disruption of ion pair networks on the activity and various stabilities of mutant F1 proteases were compared with those of the wild-type F1 protease.     

The effect of media perfusion on three-dimensional cultures of human chondrocytes: integration of experimental and computational approaches

This work examines the effect of perfusion on human mature articular chondrocytes cultured on synthetic biodegradable scaffolds (DegraPol). Human chondrocytes were isolated, seeded on the scaffolds and subjected to perfused culture at a flow rate of 0.5 ml/min, corresponding to an average inlet fluid velocity of 44 microm/s, with flow inversion every 1 minute. The flow was imposed at the construct surface in some constructs, it was forced through the construct thickness in other constructs and was absent in the static controls. We compared cell viability and morphology and we evaluated material properties of the constructs at 1 month of culture. Thickness-perfused constructs showed significantly higher material properties and roughly a two-fold cell viability, when compared both to surface-perfused constructs and to static controls. Chondrocytes maintained a phenotypic morphology in all experiments, probably favoured by a limited cell-scaffold interaction. Biosynthetic activity could be demonstrated only in the bioreactor-cultured constructs. In this experimental model, a bi-directional flow of culture medium was applied to the cells at a macroscopic level and computational modelling was used to quantify the fluid-dynamic environment induced on the cells at a microscopic level. This method may be used to quantify the effects of fluid-dynamic shear on the growth modulation of tissue-engineered cartilage constructs, to potentially enhance tissue growth in vitro.     

MicroRNA target prediction based on second-order Hidden Markov Model

MicroRNAs are one class of small single-stranded RNA of about 22 nt serving as important negative gene regulators. In animals, miRNAs mainly repress protein translation by binding itself to the 3′ UTR regions of mRNAs with imperfect complementary pairing. Although bioinformatics investigations have resulted in a number of target prediction tools, all of these have a common shortcoming—a high false positive rate. Therefore, it is important to further filter the predicted targets. In this paper, based on miRNA:target duplex, we construct a second-order Hidden Markov Model, implement Baum-Welch training algorithm and apply this model to further process predicted targets. The model trains the classifier by 244 positive and 49 negative miRNA:target interaction pairs and achieves a sensitivity of 72.54%, specificity of 55.10% and accuracy of 69.62% by 10-fold cross-validation experiments. In order to further verify the applicability of the algorithm, previously collected datasets, including 195 positive and 38 negative, are chosen to test it, with consistent results. We believe that our method will provide some guidance for experimental biologists, especially in choosing miRNA targets for validation.     

Synergistic computational and experimental proteomics approaches for more accurate detection of active serine hydrolases in yeast

An analysis of the structurally and catalytically diverse serine hydrolase protein family in the Saccharomyces cerevisiae proteome was undertaken using two independent but complementary, large-scale approaches. The first approach is based on computational analysis of serine hydrolase active site structures; the second utilizes the chemical reactivity of the serine hydrolase active site in complex mixtures. These proteomics approaches share the ability to fractionate the complex proteome into functional subsets. Each method identified a significant number of sequences, but 15 proteins were identified by both methods. Eight of these were unannotated in the Saccharomyces Genome Database at the time of this study and are thus novel serine hydrolase identifications. Three of the previously uncharacterized proteins are members of a eukaryotic serine hydrolase family, designated as Fsh (family of serine hydrolase), identified here for the first time. OVCA2, a potential human tumor suppressor, and DYR-SCHPO, a dihydrofolate reductase from Schizosaccharomyces pombe, are members of this family. Comparing the combined results to results of other proteomic methods showed that only four of the 15 proteins were identified in a recent large-scale, "shotgun" proteomic analysis and eight were identified using a related, but similar, approach (neither identifies function). Only 10 of the 15 were annotated using alternate motif-based computational tools. The results demonstrate the precision derived from combining complementary, function-based approaches to extract biological information from complex proteomes. The chemical proteomics technology indicates that a functional protein is being expressed in the cell, while the computational proteomics technology adds details about the specific type of function and residue that is likely being labeled. The combination of synergistic methods facilitates analysis, enriches true positive results, and increases confidence in novel identifications. This work also highlights the risks inherent in annotation transfer and the use of scoring functions for determination of correct annotations.     

MicroRNA expression patterns and target prediction in multiple myeloma development and malignancy

Epigenetic changes have emerged as key causes in the development and progression of multiple myeloma (MM). In this study, global microRNA (miRNA) expression profiling were performed for 27 MM (19 specimens and 8 cell lines) and 3 normal controls by microarray. miRNA-targets were identified by integrating the miRNA expression profiles with mRNA expression profiles of the matched samples (unpublished data). Two miRNAs were selected for verification by RT-qPCR (miR-150-5p and miR-4430). A total of 1791 and 8 miRNAs were over-expressed and under-expressed, respectively in MM compared to the controls (fold change ≥2.0; p?<?0.05). The miRNA-mRNA integrative analysis revealed inverse correlation between 5 putative target genes (RAD54L, CCNA2, CYSLTR2, RASGRF2 and HKDC1) and 15 miRNAs (p?<?0.05). Most of the differentially expressed miRNAs are involved in survival, proliferation, migration, invasion and drug resistance in MM. Some have never been described in association with MM (miR-33a, miR-9 and miR-211). Interestingly, our results revealed 2 miRNAs, which are closely related to B cell differentiation (miR-150 and miR-125b). For the first time, we suggest that miR-150 might be potential negative regulator for two critical cell cycle control genes, RAD54L and CCNA2, whereas miR-125b potentially target RAS and CysLT signaling proteins, namely RASGRF2 and CYSLTR2, respectively. This study has enhanced our understanding on the pathobiology of MM and opens up new avenues for future research in myelomagenesis.     

Current approaches to micro-RNA analysis and target gene prediction

It is becoming increasingly evident that micro-RNAs (miRNA) play a significant role in regulating the cellular machinery. These ∼22-nt non-coding RNAs function as negative regulators of gene expression. Since their discovery, considerable information has been obtained on miRNA biology and the mechanism of their action. Guidelines have been established for miRNA nomenclature and databases have been built to house all miRNA from many species. A number of methodologies are available for miRNA analysis. There is a lot of interest in developing bioinformatics approaches to predict miRNA target genes. This article will bring together the information on our current knowledge of miRNA biology, the approaches for miRNA analysis, and computational strategies to gain insight in miRNA functional roles.     

New insight into cofactor-free oxygenation from combined experimental and computational approaches

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New experimental and computational approaches to the analysis of gene expression.

Public and private EST (Expressed Sequence Tag) programs provide access to a large number of ESTs from a number of plant species, including Arabidopsis, corn, soybean, rice, wheat. In addition to the homology of each EST to genes in GenBank, information about homology to all other ESTs in the data base can be obtained. To estimate expression levels of genes represented in the DuPont EST data base we count the number of times each gene has been seen in different cDNA libraries, from different tissues, developmental stages or induction conditions. This quantitation of message levels is quite accurate for highly expressed messages and, unlike conventional Northern blots, allows comparison of expression levels between different genes. Lists of most highly expresses genes in different libraries can be compiled. Also, if EST data is available for cDNA libraries derived from different developmental stages, gene expression profiles across development can be assembled. We present an example of such a profile for soybean seed development. Gene expression data obtained from Electronic Northern analysis can be confirmed and extended beyond the realm of highly expressed genes by using high density DNA arrays. The ESTs identified as interesting can be arrayed on nylon or glass and probed with total labeled cDNA first strand from the tissue of interest. Two-color fluorescent labeling allows accurate mRNA ratio measurements. We are currently using the DNA array technology to study chemical induction of gene expression and the biosynthesis of oil, carbohydrate and protein in developing seeds.     

Peptide self-assembly at the nanoscale: a challenging target for computational and experimental biotechnology

Self-assembly at the nanoscale is becoming increasingly important for the fabrication of novel supramolecular structures, with applications in the fields of nanobiotechnology and nanomedicine. Peptides represent the most favorable building blocks for the design and synthesis of nanostructures because they offer a great diversity of chemical and physical properties, they can be synthesized in large amounts, and can be modified and decorated with functional elements, which can be used in diverse applications. In this article, we review some of the most recent experimental advances in the use of nanoscale self-assembled peptide structures and the theoretical efforts aimed at understanding the microscopic determinants of their formation, stability and conformational properties. The combination of experimental observations and theoretical advances will be fundamental to fully realizing the biotechnological potential of peptide self-organization.     

Two novel RNA-binding proteins identification through computational prediction and experimental validation

Since RBPs play important roles in the cell, it's particularly important to find new RBPs. We performed iRIP-seq and CLIP-seq to verify two proteins, CLIP1 and DMD, predicted by RBPPred whether are RBPs or not. The experimental results confirm that these two proteins have RNA-binding activity. We identified significantly enriched binding motifs UGGGGAGG, CUUCCG and CCCGU for CLIP1 (iRIP-seq), DMD (iRIP-seq) and DMD (CLIP-seq), respectively. The computational KEGG and GO analysis show that the CLIP1 and DMD share some biological processes and functions. Besides, we found that the SNPs between DMD and its RNA partners may be associated with Becker muscular dystrophy, Duchenne muscular dystrophy, Dilated cardiomyopathy 3B and Cardiovascular phenotype. Among the thirteen cancers data, CLIP1 and another 300 oncogenes always co-occur, and 123 of these 300 genes interact with CLIP1. These cancers may be associated with the mutations occurred in both CLIP1 and the genes it interacts with.